Agentic computer use refers to AI agents that can actively operate computers, not just chat or make suggestions. These systems perceive on-screen interfaces (windows, buttons, web pages) and act by moving the mouse, typing, clicking links, and more – essentially using the computer as a human would.
In late 2025, this field exploded with new AI agents that plan and execute multi-step tasks autonomously on our devices. Unlike simple voice assistants or traditional macros, these agents combine advanced vision and language models to navigate apps and websites even without custom APIs (o-mega.ai).
This in-depth guide (targeted at a semi-technical audience) will explore what agentic computer use means, the types of agents (operating system-level vs. browser-based), profiles of 10 leading AI agents as of 2025/2026, their use cases, performance benchmarks, limitations, market trends (including pricing and platforms), and what the future may hold for AI “digital workers.” By the end, you should have a fundamental understanding of this rapidly evolving area and how these AI agents are transforming work and daily tasks.
Contents
Understanding Agentic Computer Use – What it is and why it matters
OS-Level vs. Browser-Based Agents – Two approaches to computer-using AI
Top 10 AI “Computer Use” Agents in 2025 – Leading platforms and their strengths
Key Use Cases and Applications – How these agents are being used in the real world
Benchmarks & Performance – How we measure agents (WebArena, GAIA, etc.)
Stealth, Safety & Challenges – CAPTCHAs, errors, and other hurdles for agents
Platforms, Pricing & Market Impact – Major platforms, pricing models, and industry adoption
Open-Source vs. Closed Solutions – Comparing community frameworks with proprietary agents
Future Outlook (2026 and Beyond) – Trends, upcoming players, and the road ahead
1. Understanding Agentic Computer Use
Agentic computer use (often called “computer-use agents” or “CUAs”) describes AI systems that don’t just talk, but actively perform tasks on a computer on your behalf. These agents can see the same graphical user interfaces (GUIs) we do – web pages, app windows, buttons – and interact with them by clicking, typing, scrolling, etc. (o-mega.ai). In essence, an agentic AI can drive your computer or browser to complete multi-step tasks autonomously, following high-level goals you give it. For example, instead of simply telling you the weather, an agent could open your browser, navigate to a weather site, log in, retrieve specific data, and perhaps email you a summary – all without you manually doing each step.
This represents a major shift from traditional digital assistants. Classic voice assistants (Siri, Alexa) or chatbots respond to one-off commands or questions with an answer or simple action. By contrast, agentic AIs plan and execute entire sequences of actions toward a broader goal (o-mega.ai). They also differ from old-school RPA (Robotic Process Automation) scripts, which were brittle recordings of clicks – RPA automations tend to break if an interface changes even slightly. Modern AI agents are far more adaptive and robust: they use computer vision and language understanding to adjust to new layouts or content, making them resilient to UI changes (o-mega.ai). This means an agent can handle variations or unexpected pop-ups in a workflow, where older automation would fail.
Importantly, agentic computer use broadens what AI can do. Instead of just producing text or images, these agents can take actions in the digital world. They can fill out forms, cross-post data between applications, schedule events, or even simulate a user browsing an e-commerce site. In 2025, businesses began seeing these agents as “digital coworkers” that handle tedious on-screen work: logging into legacy systems, copying data from one app to another, generating reports, etc. (o-mega.ai). For individuals, agentic AI promises personal assistants that truly act on your behalf (booking travel, managing files, navigating websites) rather than just advising. This guide will dive into how exactly these agents work and are used, but at a high level, agentic computer use matters because it shifts AI from a passive tool into an active doer in our digital environments.
2. OS-Level vs. Browser-Based Agents
When discussing computer-using agents, it’s useful to distinguish between two broad approaches: operating system (OS)-level agents and browser-based agents. Both can perform complex tasks by manipulating interfaces, but they operate in different scopes:
OS-Level Agents: These agents can control the entire desktop or operating system environment. They simulate a human user at the OS level – moving the mouse cursor anywhere on the screen, opening or switching between applications, typing into native desktop apps, and so on. In other words, an OS-level agent isn’t limited to a single program; it can potentially use any software on the computer. For example, an OS-level agent might open Excel to copy data and then paste it into a web CRM, or adjust system settings via the Control Panel. This approach is powerful because it mirrors exactly what a human user can do on their PC. A standout example is Simular’s agent, which “literally \ [moves] the mouse on the screen and \ [does] the click,” allowing it to repeat any digital tasks a person could do on a Mac or Windows PC (techcrunch.com). Such agents often work via a remote or virtual desktop session for safety – essentially giving the AI a virtual PC to control without risking your actual machine. The challenge with OS-level agents is the diversity of possible interfaces (every app might have a different UI) and the need for deeper integration with the operating system. Until recently, this was mostly the domain of research and advanced RPA tools, but 2025 saw the first practical OS-wide agents emerge.
Browser-Based Agents: These agents operate within a web browser environment, focusing on automating web pages and web applications. A browser-based agent is typically given a virtual browser (often a cloud-based one) that it can navigate: it can click links, fill out web forms, scroll pages, and interact with websites just like a user would. This approach is somewhat more contained – the agent’s “world” is the web browser and whatever pages or web apps it loads. OpenAI’s Operator (later integrated into ChatGPT as the “Browse with an agent” mode) is a prime example: it runs in a sandboxed browser, letting the AI visit sites, click buttons, and scrape information (o-mega.ai) (openai.com). Browser agents are incredibly useful for tasks like online research, shopping, form submission, social media actions, and other web tasks. They are generally easier to deploy in a controlled way (since web pages are a relatively uniform interface and a sandbox browser limits any harm). However, they can’t directly control native desktop apps (at least not without additional plugins); they’re limited to what can be done via the browser. Some products blur the line by also allowing file access through the browser (for example, uploading a file or reading a local file via the browser interface), but fundamentally, a browser agent lives in the web.
In practice, the line is beginning to blur. Some hybrid agents start in the browser but also can interact with certain desktop features via connectors. Meanwhile, OS-level frameworks might use a browser-like approach under the hood for each app (rendering the app GUI off-screen and interpreting it). As of late 2025, though, most widely available agents fell into one of these two categories. Browser-focused agents dominated early, since many tasks (especially business workflows) are web-based and it’s easier to sandbox a browser for AI control. OS-level agents are now catching up, promising to automate legacy desktop software and system tasks that browsers alone can’t handle.
To clarify with examples: OpenAI’s agent and Google’s Gemini agent (described soon) are browser-based – optimized for web tasks, they see through a browser viewport. In contrast, something like Simular’s agent is OS-level, aiming to automate any app on your computer by emulating user input at the operating system level (techcrunch.com). Microsoft is also building OS-level agent support directly into Windows (via Windows Copilot and related features) to allow agents that can manage files, settings, and multiple apps (techcommunity.microsoft.com) (techcommunity.microsoft.com). Each approach has its strengths: browser agents excel at online tasks and are easier to secure, while OS agents have a broader reach across different software. In the next section, we’ll see specific examples of both types in the current landscape of top agents.
3. Top 10 AI “Computer Use” Agents in 2025
Late 2025 saw a surge of AI agent platforms, both from tech giants and startups, aiming to let AI handle real computer tasks. Below, we profile 10 of the leading agent solutions, highlighting whether they are browser-only or OS-level (or both), and what makes each unique:
1. OpenAI ChatGPT “Operator” Agent – Type: Browser-focused (Web)
! (https://openai.com/index/introducing-operator/)
OpenAI’s Operator (ChatGPT agent mode) navigating a travel site autonomously. The agent works in a cloud browser and lists each step it takes (left), from clicking menu categories to sorting results.
OpenAI’s Operator (also known just as the ChatGPT agent in “Agent Mode”) is often viewed as a trailblazer in this field. Announced as a research preview in early 2025, Operator is an agent integrated with ChatGPT that can perform tasks on the web for you (openai.com). Essentially, OpenAI took GPT-4’s capabilities and gave it a built-in browser to control. When you ask it to do something like “Find me the highest-rated one-day tour of Rome on TripAdvisor,” the agent will open a web page, navigate through the site’s interface, click the necessary links, and gather the info – all while reporting its actions step-by-step (as shown in the example above). Under the hood, Operator uses a specialized model called the Computer-Using Agent (CUA), which combines GPT-4’s vision (to “see” the page) with reinforcement learning for better decision-making (openai.com). The result is an AI that can read a webpage and then interact with it by clicking buttons or typing, in a loop of observe→act until the task is done.
Capabilities: Operator is designed for web tasks. It can fill out forms, scrape information, navigate multi-page workflows, and even handle things like adding items to a shopping cart (o-mega.ai). Early users had it ordering groceries, booking reservations, and creating internet memes automatically (openai.com). One of its strengths is error recovery – if a pop-up or unexpected page appears, it will try alternate strategies or politely ask for help if truly stuck. It also smartly defers to the user for anything sensitive: for instance, it won’t enter passwords or solve CAPTCHAs on its own; it will ask you to intervene when such steps come up (openai.com). This keeps the agent from doing anything too risky without approval. OpenAI integrated Operator into ChatGPT’s interface for Pro users (initially at operator.chatgpt.com, later directly in ChatGPT as “Browse with an agent”), making it one of the most accessible agents to the public.
Classification: Browser-based. Operator runs entirely in a virtual browser sandbox in the cloud, not on your local OS (o-mega.ai). It does not directly control native desktop apps or your file system – its domain is the web. This was a deliberate design choice to maximize security (the agent can’t, say, delete your local files since it only “lives” in the cloud browser) (o-mega.ai).
Performance: Operator set new state-of-the-art scores in key browser automation benchmarks like WebArena and WebVoyager, outperforming previous agents on web-based tasks (openai.com). In one internal 50-step web task stress-test, OpenAI reported around 32.6% success – which was the best for a single-agent system at the time (o-mega.ai). This might sound low, but these benchmarks are extremely challenging (involving dozens of steps where any mistake can derail the whole chain). Operator’s achievements proved that a well-trained agent can robustly handle long, complex web tasks better than any prior system.
Overall, OpenAI’s agent is powerful but web-only. It shines in use cases like cross-site research, online form automation, and data gathering. If you need an AI to do something on a website (that doesn’t require logging into your personal accounts, or if it does you’re able to assist it securely), ChatGPT’s Operator mode is a leading choice as of 2025. It essentially brought the convenience of ChatGPT to a more active, task-oriented paradigm – instead of just answering questions, it can take actions to directly get things done online.
2. Google Gemini “Computer Use” Agent (Project Mariner) – Type: Browser/Mobile-focused (Web & Apps)
Google entered the fray with its own agentic technology as part of the Gemini AI model suite. In October 2025, Google DeepMind announced the Gemini 2.5 Computer Use model, a special version of their Gemini AI tuned for controlling user interfaces (blog.google) (blog.google). This model powers what’s internally codenamed Project Mariner, which is Google’s experimental agent capable of multi-tasking across web and mobile app interfaces. Think of it as Google’s answer to OpenAI’s operator: Gemini’s agent can click, scroll, type, and navigate both websites and even Android app screens. In fact, the model isn’t limited to web – Google reports it also works on mobile UIs (Android) very effectively (blog.google), though desktop OS-level control was not yet optimized in this version.
Capabilities: Gemini’s Computer Use agent can do things like fill out complex web forms, manipulate interactive page elements (dropdowns, sliders, etc.), and even handle content behind logins (assuming credentials are provided). A demo from Google showed it taking data from one web app and inputting it into another, scheduling appointments, and organizing content via a web interface (blog.google). Another demo had it sorting sticky notes on a virtual whiteboard app automatically (blog.google) – showcasing understanding of a GUI’s spatial layout. In essence, it’s built to do any series of actions a user might do in a browser or smartphone app: from shopping and booking tasks to dragging-and-dropping items on a page.
Classification: Primarily browser-based (web) and mobile app control. Google’s agent operates via a computer_use API within the Gemini service (blog.google). Developers provide the agent with a screenshot of the current interface (webpage or mobile app view), and the model returns an action (like “click this button” or “enter text here”) (blog.google) (blog.google). This loop repeats, so the agent iteratively interacts until the task is done. While it focuses on web and mobile UI automation, Google hinted that future versions might tackle desktop apps too. For now, Gemini’s agent doesn’t natively drive desktop software (it’s not opening your Windows apps), but it excels in the browser context and on simulated phone screens.
Performance: Google highlighted that Gemini 2.5’s agent outperformed leading alternatives on multiple benchmarks, including Online-Mind2Web, WebVoyager, and an Android mobile task suite (blog.google). Impressively, it did so with lower latency – meaning it’s faster at deciding and executing actions than others (blog.google). A plot released by Google showed Gemini’s agent achieving high accuracy (~70% success on a set of tasks) with significantly less delay compared to peers (blog.google). In practical terms, that suggests it’s both efficient and effective – a critical combination for user satisfaction, since no one wants an AI assistant that takes ages to do something. It’s also notable that Google baked in extensive safety features: the agent has an in-model understanding of risky actions and external guardrails requiring confirmation for things like purchases or deleting data (blog.google) (blog.google). This is essential given an agent that can click anything could otherwise do damage by mistake.
In summary, Google’s Gemini-based agent (Project Mariner) is a strong contender focused on multi-platform UI tasks. While not directly controlling desktop OS yet, it’s extremely capable in the web realm and even mobile app automation. It’s available via API (in preview on Google Cloud), so enterprises and developers can build it into their own products. If OpenAI’s Operator is a consumer-facing agent in ChatGPT, Google’s approach is more a developer toolkit for agent capabilities, which companies like Firebase (testing agent) and internal Google teams have already utilized for UI testing and workflow automation (blog.google). As we head into 2026, expect Google to integrate this tech deeper into their products (imagine an AI that can drive your Google Workspace apps for you). For now, it stands as one of the state-of-the-art solutions in autonomous web and app interaction, backed by Google’s AI prowess.
3. Microsoft Windows Copilot & “Agent Workspace” – Type: OS-level (Desktop & Web)
Microsoft’s strategy for agentic AI is to embed it right into the operating system. With the updates announced around Ignite 2025, Windows 11 is evolving to include built-in agent capabilities that allow AI to perform tasks across the OS and connected services (techcommunity.microsoft.com). The umbrella term is often just Windows Copilot (the AI assistant in Windows 11), but under the hood Microsoft is adding specific features for autonomous agents: for instance, an Agent status in the Taskbar that shows what a long-running agent is doing (techcommunity.microsoft.com), and an “Agents” tool menu integrated with the Copilot interface to launch different AI agents on demand (techcommunity.microsoft.com). Essentially, Microsoft aims to unify how users invoke and manage AI agents across the OS (techcommunity.microsoft.com), treating them as first-class entities in Windows.
One key innovation is the Agent Workspace and Model Context Protocol (MCP). The Agent Workspace is a secure, sandboxed environment on Windows where an agent can operate like a user, but isolated from the main user session (techcommunity.microsoft.com). This prevents the agent from interfering with your work while it does tasks in parallel (imagine an AI sorting files or configuring settings in a virtual desktop that you can monitor). The MCP, meanwhile, gives agents a standard way to connect with Windows apps and settings through “agent connectors” (techcommunity.microsoft.com). Microsoft has built connectors for things like File Explorer and Windows Settings (techcommunity.microsoft.com), so an agent can directly manipulate files or toggle OS options safely via defined APIs. This blend of direct UI control (the agent can still move the mouse in the workspace) and API hookups is meant to make OS-level automation more reliable.
Capabilities: With Windows integrating these features, an AI agent on Windows could do things like organize your files, launch and use Office apps, update configurations, or perform multi-step enterprise workflows involving desktop software. Microsoft 365 Copilot already, for example, can generate content in Office apps; the next step is letting it click around for you. Microsoft introduced a toolkit called Copilot Studio (Computer Use), which lets developers build custom copilots that automate web tasks from a prompt (techcommunity.microsoft.com). Also, via Windows 365 for Agents, Microsoft provides a Cloud PC (virtual Windows machine) that agents can use with full access in a controlled manner (techcommunity.microsoft.com). They explicitly mention that these agents can “browse websites, process data, and automate tasks” on a Cloud PC with proper security controls (techcommunity.microsoft.com). This is effectively Microsoft offering Agents-as-a-Service on cloud Windows instances, which is big for enterprise scenarios. In fact, Microsoft partnered with several leading agent startups (Manus, Fellou, Genspark, Simular, TinyFish) to help test Windows 365 for Agents (techcommunity.microsoft.com) – indicating those companies’ agents will run on Microsoft’s Cloud PCs for higher scalability.
Classification: Primarily OS-level, with a deep integration into Windows itself. Microsoft’s approach is to make the entire operating system “agentic-ready.” An agent built for Windows via Copilot Studio can operate both on web (through Edge, for instance) and on desktop apps, thanks to the connectors and agent workspace. So it’s a comprehensive approach, spanning OS and browser. Notably, Microsoft also worked on a small specialized model codenamed “Fara” (a reported 7B-parameter model) optimized for tool use on Windows – though details are scant, this was aimed to power some local agent tasks efficiently. The overall picture is that Microsoft provides the infrastructure and guidelines for agents rather than one single consumer agent (besides Copilot itself). Users might invoke various copilots or agents for different needs (research, troubleshooting, etc.) through the unified Copilot interface in Windows.
Status: As of end of 2025, many of these capabilities were in preview. Early adopters (enterprise users in Windows Insider builds) could start experiments with, say, an “Agent to clean up my Downloads folder” or “Researcher Agent” that lives in the taskbar doing background info gathering (techcommunity.microsoft.com). Microsoft’s focus on security and manageability is high – IT admins will have controls to enable/disable agent features, set policies (so an agent cannot, for example, access certain corporate data unless allowed) (techcommunity.microsoft.com) (techcommunity.microsoft.com). This cautious rollout reflects that OS-level agents are powerful but potentially risky if not governed.
In sum, Microsoft is positioning Windows as the platform for agentic AI, where users might have multiple specialized agents running concurrently to boost productivity. By building it into the OS, they lower the friction – you might launch an AI agent as easily as launching an app. This could be transformative, especially in workplaces: imagine every employee has personal AI agents configured for their routine tasks, all sanctioned and monitored by IT. Microsoft’s deep ties with enterprise software (Office, Teams, etc.) give it an edge in creating agents that truly integrate with the tools people use daily. While not a singular “product” like some others on this list, Windows’ agentic features make it a key part of the 2025 agent landscape and certainly one to watch going into 2026.
4. Anthropic Claude with Computer Use – Type: OS-level and Web (Hybrid)
Anthropic, known for its Claude AI assistant, introduced a capability called “Computer Use” which effectively turns Claude into an agent that can operate a computer. In late 2024, they launched this as a beta in Claude 3.5, marking one of the first instances of a major language model being given direct control over a UI (anthropic.com). Claude’s computer-use feature allows it to “see” a virtual screen and simulate mouse/keyboard actions, very much like OpenAI’s and Google’s agents. By 2025, this had evolved and was being used in their Claude 2 models and offered via API and partnerships (including through Amazon Bedrock, since Amazon is a partner/investor in Anthropic).
Capabilities: Claude’s computer-use tool enables tasks such as reading what’s on your screen, clicking buttons, scrolling pages, and entering text – essentially the basic building blocks to do anything on a GUI. Anthropic demonstrated Claude using this to fill out forms using data from both local files and web sources, or to perform open-ended research tasks that involve browsing multiple sites (anthropic.com). A notable example: on a benchmark called OSWorld (which tests an AI’s ability to use a computer like a person, via screenshots), Claude achieved about 14.9% success in a “screenshot-only” scenario, nearly double the next-best AI’s ~7.8% (anthropic.com). While 15% might seem low, it reflects how challenging fully general computer control is – and that Claude was a frontrunner in this nascent area. When given more attempts/steps, Claude’s score rose to 22.0%, showing that with iteration it can solve more tasks (anthropic.com). These tasks might include things like navigating a simple app interface or retrieving info from a series of windows.
Anthropic’s focus with Claude is often on safety and reliability. They openly stated the system was experimental and sometimes error-prone at launch (anthropic.com). Some actions humans take for granted, like smoothly dragging an item or handling a complex multi-modal dialog, were challenging for the AI initially (anthropic.com). Nevertheless, Claude’s strength is its strong natural language understanding and gentler approach to user instructions (Anthropic emphasizes “Constitutional AI” for safer responses). This translates to an agent that is relatively good at following nuanced directions like “click the green button that says Submit, but skip any optional upsells” – things it was explicitly designed to handle (theverge.com).
Classification: Hybrid (OS-level and Web). Claude’s agent is available as an API where you feed it images (screenshots) and it returns actions, similar to Google’s approach (anthropic.com). It doesn’t come with a consumer UI like ChatGPT’s agent; instead, developers integrate it into tools (for example, Cognition’s autonomous agent platform used Claude’s skills). Claude’s tool can drive web browsers and potentially desktop apps if those apps’ screens are captured. In practice, much of the use so far has been web-centric (like controlling a browser to do tasks), but Anthropic has shown interest in full desktop control as well (they mention complex IT tasks as a dream use case (theverge.com)). Also, through partnerships, Claude’s agent capabilities were being leveraged in larger workflows – e.g. an AWS Bedrock reference shows combining Claude’s computer-use with AWS automation (docs.aws.amazon.com).
Performance and Benchmarks: Apart from OSWorld, Anthropic reported Claude 3.5 (Sonnet) made big gains on agentic tasks like coding and tool use. For instance, it improved on a tool-use benchmark (TAU) to ~69% in a retail scenario (anthropic.com). While not directly a “use computer” metric, it shows Claude’s reasoning for using tools improved, which correlates with better performance when the “tool” is effectively the computer interface. Additionally, Anthropic likely participates in the GAIA benchmark (General Autonomous Intelligence Assistant, see Manus later) with its Claude-based agents. In competition, a “Claude-based system for computer control” is noted as one of Manus’s peers (en.wikipedia.org).
In summary, Anthropic’s Claude as an agent is among the top in reasoning and fairly capable in execution, though it remains a developing feature. It’s more behind-the-scenes compared to OpenAI’s or Google’s offerings – you might not directly “use Claude to run my PC” unless you’re a developer or using a third-party product that embeds it. But it’s significant that one of the major AI labs is pushing this area. It means more choice of foundation models for agents (beyond OpenAI and Google) and likely leads to safer, diverse approaches. By 2026, we expect Anthropic to refine this further, possibly integrating it into their own consumer-facing Claude assistant, so that Claude might eventually not only chat with you but also take actions on your devices when permitted.
5. Amazon’s Nova Act – Type: Browser-focused (Web)
E-commerce giant Amazon jumped into AI agents with Nova Act, an AI agent unveiled in 2025 that’s specifically geared towards taking actions on the web – notably, doing your online shopping and other web tasks for you. Nova Act is part of Amazon’s “Nova” family of foundation models (their push toward versatile AI), and it represents Amazon AGI Labs’ first big agent product (theverge.com). As the name suggests (“Act”), it’s about action-taking. For now, Amazon has made Nova Act available in a research preview for developers rather than a wide consumer release (theverge.com), but it’s already being used under the hood in some of Amazon’s own services, like the upgraded Alexa digital assistant.
Capabilities: Nova Act can carry out typical web user actions: performing searches, clicking through websites, filling forms, and making purchases online (theverge.com). Amazon highlighted its utility in shopping scenarios – for example, you could instruct it to buy an item while telling it constraints like “don’t accept the insurance upsell,” and it will navigate the e-commerce site, find your product, compare options, add it to cart, decline extras, and check out (theverge.com). It can also answer questions about what’s currently on a webpage (essentially doing a read and explain, like “Is this item in stock?”). One intriguing feature is the ability to schedule tasks – you could tell Nova Act to perform something later (say, check a website every hour or place an order tomorrow at noon), and it will remember and do so (o-mega.ai). This indicates a form of autonomy over time, which not all agents have (many just operate in the here-and-now of a single user query).
Amazon has also integrated Nova Act with Alexa for certain tasks. Alexa Plus, the more advanced Alexa, can offload web tasks to Nova Act behind the scenes (theverge.com). For instance, if you ask Alexa a question that requires browsing (e.g., “find me a good sushi restaurant and book a table”), Nova Act might handle the web navigation portion, while Alexa handles the conversation. This shows Amazon’s strategy of combining voice assistants with agentic actions for a more powerful assistant.
Classification: Browser-based. Nova Act is focused on web browser control. It doesn’t, at this stage, automate arbitrary desktop apps outside the browser. Amazon’s idea is to leverage the massive range of things that can be done via the web. They even demonstrated Nova Act controlling Google Maps in a browser to answer a query (checking apartment locations relative to a train station) (theverge.com) – a bit ironic, using a competitor’s service, but it shows the agent’s generality. Nova Act runs on Amazon’s cloud and is accessed through APIs or Amazon’s own interfaces. It’s also integrated into AWS Bedrock (Amazon’s AI platform for developers) (o-mega.ai), meaning companies can use it to build agents that operate web-based dashboards or other online processes, with the scalability and cost benefits of AWS. In fact, Amazon is emphasizing that Nova (the model suite) is cheaper – claiming the Nova models are at least 75% less expensive than rivals to run (theverge.com). Cost efficiency is a big theme for Amazon, suggesting Nova Act could be an attractive option for businesses that need to run many agent actions without breaking the bank.
Status and Performance: Nova Act was in preview as of 2025, with developers able to sign up on a portal (nova.amazon.com) to test it (o-mega.ai). Amazon hasn’t published detailed benchmark numbers in the announcement, but a Wired piece reported Nova Act “outperforms ones from OpenAI and Anthropic on several benchmarks” (wired.com) – if accurate, that’s notable and could refer to things like web navigation tasks. Amazon likely ran internal tests where Nova Act’s success rate or speed on certain controlled tasks beat GPT-4’s or Claude’s. Additionally, Amazon’s AWS re:Invent conference in 2025 showcased Nova Act’s reliability and cost benefits for enterprise UI automation (aws.amazon.com). They position it as highly reliable and easy to use for developers (aws.amazon.com).
In practice, Nova Act is a promising entrant especially for commerce and enterprise automation. Amazon’s strength is infrastructure – they can offer Nova Act as a service deeply integrated with AWS, making it easy to plug into workflows (imagine an agent that regularly checks competitors’ prices online and updates a database, all running on AWS). It’s also likely to be a key component of future Alexa features, effectively giving Alexa eyes and hands on the web. If you’re an end-user, you might not “see” Nova Act directly, but you’ll benefit from Alexa or other Amazon products suddenly being able to do things online for you, not just talk. For developers and companies, Nova Act provides another major toolkit to build custom agents, especially attractive if you already work in the AWS ecosystem or require cost-effective scaling.
6. Manus AI – Type: Browser-based with Cloud OS (General-purpose)
Manus AI burst onto the scene in 2025 as one of the first widely publicized fully autonomous AI agents for general use. Developed by a Singapore-based startup (Butterfly Effect Technology), Manus launched in March 2025 and quickly gained notoriety for its ambitious scope: it aims to be a broad general-purpose agent that can handle a variety of knowledge work tasks (research, coding, content creation, web automation, etc.) with minimal human guidance (en.wikipedia.org) (en.wikipedia.org). Think of Manus as an AI you can delegate a complex project to, and it will break it down and execute it, using tools and the web along the way. It was described by some early testers as “like collaborating with a very efficient intern” – it might figure out how to do what you asked, even if it has to learn new websites or software to do so (en.wikipedia.org).
Capabilities: Manus is built as a multi-modal, multi-agent system behind the scenes. It has sub-agents specialized in planning, executing web actions, coding, and more (baytechconsulting.com) (baytechconsulting.com). As a user, you just see a single interface: you give Manus a high-level task or goal, and it coordinates everything needed to complete it. For example, if you ask Manus, “Compile a market research report on electric vehicle competitors and generate a PowerPoint summary,” Manus could: search the web for data, identify key competitors, gather financial and product info, compile it into a structured analysis (with citations), then actually create slides with graphs – all autonomously. These aren’t hypothetical; users have reported Manus doing multi-step workflows like building a functional website from a prompt, analyzing stock data and making an Excel model, and screening resumes against job descriptions (en.wikipedia.org) (en.wikipedia.org). It can also write and debug code as part of its process (the team touts code execution as a feature), which means if a task needs a custom script, Manus might write one on the fly (en.wikipedia.org).
Manus interacts with web interfaces heavily: it can navigate to websites, log in, scrape or input information (this falls under “Web automation” in its feature list) (en.wikipedia.org). It also handles files (PDFs, CSVs, images) for data extraction (en.wikipedia.org). One differentiator was the “Manus’s Computer” interface – a panel where users can watch the agent’s actions in real-time, almost like seeing a virtual machine’s screen (baytechconsulting.com) (baytechconsulting.com). This transparency builds trust, as you can observe it clicking and typing, and even intervene if needed. It also logs steps, which is useful for auditing what it did.
Classification: Manus primarily operates as a cloud-based agent, controlling browsers and cloud computing resources. It doesn’t install on your local PC; you interact with it through a web app. So in that sense, it’s browser-based (it uses cloud browser instances to perform tasks on websites) and also leverages cloud tools (like spinning up a coding environment for itself). It essentially gives you a “virtual computer assistant” in the cloud. This means Manus can run for long periods asynchronously – you can close your device and it continues working on the task in the background (en.wikipedia.org). That’s why users could queue big jobs and come back later to results.
Performance: Manus made waves by claiming state-of-the-art results on the GAIA benchmark (General AI Assistant benchmark) (en.wikipedia.org) (en.wikipedia.org). GAIA is a test created by Meta AI, HuggingFace, and others to evaluate autonomous agents on real-world tasks requiring reasoning and tool use. Manus’s team reported their agent exceeded OpenAI’s reference agent (“Deep Research”) on GAIA, especially at more complex task levels (en.wikipedia.org). Specifically, Manus scored about 86.5% on Level 1 tasks vs. 74.3% for OpenAI’s agent, and remained higher on Level 2 and 3 tasks as well (en.wikipedia.org). Meanwhile, GPT-4 with plugins managed only ~15% on those GAIA tasks (en.wikipedia.org) – showing how much better a specialized agent like Manus performed than a generic GPT-4 tool-using session. (For context, human experts scored ~92% on GAIA, so there’s still a gap to human-level, but Manus dramatically closed the gap relative to earlier AI attempts (en.wikipedia.org).) These results, albeit company-reported, gave Manus credibility as possibly the most advanced autonomous agent at the time of launch.
However, with ambition came hiccups. Manus’s early beta had plenty of issues reported: from error messages and crashes to getting stuck in loops or making absurd decisions (en.wikipedia.org). Some simple tasks like booking a hotel or ordering a sandwich online tripped it up, according to TechCrunch’s tests (en.wikipedia.org). The team iterated quickly (by December 2025 they were on version 1.6). Users also had to learn how to prompt it effectively – giving clear end goals and constraints. Manus offers various subscription tiers, from a limited free tier (1 task per day) to paid plans like Starter ($39/mo) and Pro ($199/mo) which allow more concurrent tasks and higher usage limits (en.wikipedia.org) (en.wikipedia.org). Remarkably, Manus reached $100M in annual recurring revenue (ARR) within 8 months of launch (manus.im), reportedly making it the fastest startup ever to hit that mark. This indicates not just hype but real demand – many thousands of users or teams were paying for it, despite its quirks.
In summary, Manus AI represents the cutting-edge of autonomous “do-anything” agents by late 2025. It’s broad in scope and aimed at power users and professionals who want an AI that can take on complex digital tasks end-to-end. While it primarily operates through browsers and cloud, it is generalist in nature (not limited to one domain). The success of Manus spurred a wave of similar projects and certainly got Big Tech’s attention. It also showcased both the potential and current limitations of such agents: Manus can dazzle with a multi-step feat one moment and then stumble on something a human considers trivial the next. As we proceed, many are watching to see if Manus (and its competitors) can iron out those weaknesses and truly deliver reliable autonomous assistants.
7. Simular AI Agent – Type: OS-Level (Desktop Automation)
Simular is a startup that took a distinct path: rather than focusing on web automation alone, Simular built an agent to control the entire operating system. In late 2025, Simular released its AI agent for Mac OS (with a Windows version in development) and caught attention by emphasizing full PC control. The co-founder Ang Li described it succinctly: “We can literally move the mouse on the screen and do the click,” meaning the agent replicates all the GUI actions a person would (techcrunch.com). This approach can, in theory, automate any digital task a human does, whether it’s in a browser or a native app or even across multiple apps. For example, Simular’s AI could copy data from a desktop application and paste it into an online form, something a web-only agent couldn’t do.
Capabilities: As of its 1.0 launch on Mac, Simular’s agent could perform multi-step workflows involving various Mac applications and web together. One use case given was copying and pasting data into a spreadsheet – imagine an AI agent that takes info from, say, an email or a PDF and pastes it into Excel, then runs a formula, then perhaps uploads the result somewhere (techcrunch.com). Another scenario is automating software testing or config changes: the agent can navigate system menus, change settings, or operate any GUI that a Mac user can. Simular’s website and materials also talk about workflow automation and RPA-like tasks – for instance, processing invoices by downloading attachments from email, opening them in a PDF reader, extracting text, and inputting into a finance system. These tasks often span multiple programs (browser, PDF reader, Excel, etc.), which is exactly where an OS-level agent shines.
Under the hood, Simular developed an open-source framework called Agent S^2 for GUI automation (o-mega.ai). By open-sourcing, they invited the developer community to contribute and build on it. This also suggests Simular’s agent can be self-hosted or customized, which appeals to enterprises concerned about using a closed black-box AI. Simular pairs the GUI control with advanced model reasoning (their team has reinforcement learning expertise from DeepMind). To mitigate common agent issues like hallucinations causing errors, Simular explores making the AI more deterministic when needed (techcrunch.com). The idea is if a task should be done a very specific way every time, constrain the AI to follow that script reliably (reducing creative deviations), but this has to be balanced with flexibility.
Classification: OS-Level. Simular’s agent isn’t confined to a browser; it operates on a virtual desktop. They’ve collaborated with Microsoft to use Windows 365 Cloud PCs as the sandbox for their agent on Windows (techcrunch.com). This means when their Windows agent is ready, it will run on a cloud-streamed Windows machine, doing tasks in a controlled environment that mirrors a user’s PC. On Mac, the 1.0 release runs locally on macOS. So Simular covers both worlds: local agent for Mac enthusiasts and cloud-hosted for Windows (with Microsoft’s blessing). Because it’s OS-level, Simular can in principle do browser tasks too (by opening a browser on the desktop), but it doesn’t need a special web sandbox – it treats the browser as just another app.
Performance and Adoption: Simular might not have public benchmarks like GAIA yet, but it has strong credentials. The founders are ex-DeepMind researchers, and the product was compelling enough to raise a $21.5 million Series A in 2025 with investors like Felicis and even NVIDIA’s venture arm (techcrunch.com). Microsoft invited Simular into its Windows 365 for Agents early program as one of five companies (alongside better-known names like Manus and Fellou) (techcrunch.com), indicating Microsoft sees potential in Simular’s approach. One big technical challenge for Simular is the hallucination problem in long action sequences: as the number of steps grows, the chance an AI drifts into an error increases (techcrunch.com). Simular’s team is actively researching solutions, such as blending deterministic scripts for certain subtasks or using out-of-model verification steps. This reflects a pragmatic view: pure LLM-driven agents can sometimes be flaky, so incorporate traditional automation reliability where possible.
In practice, Simular’s agent is likely used in scenarios like: a small business automating their back-office (with the AI performing nightly routines on a PC), or a QA engineer letting the AI run through app UI tests, or an individual power user who wants their Mac to, say, organize files and send emails while they’re away. Because it’s early, most users were still testers, but the concept is powerful. If Simular succeeds, it means you could have a single AI entity that handles everything on your computer, not just within one app. That’s the closest thing yet to a true “robotic assistant” on personal computing devices. As 2026 arrives, Simular is definitely a company to watch, especially once their Windows agent goes live. It embodies the vision that your AI can use your computer just like you do, which is a holy grail for many looking to offload digital drudgery.
8. Fellou “Agentic Browser” – Type: Both (Browser + Local apps)
Fellou is an innovative player that created what it calls the world’s first “agentic browser.” In other words, Fellou built a web browser from the ground up that has AI agents integrated into its core. Launched as Fellou Concept Edition (CE) in April 2025, it quickly attracted over 1 million users by reimagining the browser as an active workspace rather than a passive tool (wired.com). With Fellou, instead of just showing you web pages, the browser comes with AI agents that can execute multi-step workflows across those pages and even interact with some local or desktop elements. It’s like having a super-smart sidekick living inside your browser, ready to carry out tasks that span multiple websites or even local files.
Capabilities: Fellou’s agentic browser can handle scenarios like the tedious job search process: rather than you manually searching job sites, tweaking resumes, and applying, you can ask Fellou to do it. According to the company, a user did just that – the AI agents in Fellou automatically searched for jobs matching criteria, adjusted the user’s resume for each, submitted applications, and even scheduled interviews, resulting in 10 interviews and a job offer within a week (wired.com). That’s a powerful example of end-to-end workflow automation. Fellou agents can operate in parallel “spatial layers” (wired.com) – meaning the browser can have multiple agent-driven processes running concurrently, each in its own workspace (imagine several mini-browsers or windows, each agent doing a part of the task, all coordinated).
What’s unique is Fellou’s concept of a 3D spatial workspace for browsing (wired.com) (wired.com). Traditional browsers are flat – you have tabs and windows. Fellou adds a “Z-axis,” visually stacking agent activities in a layered UI. This allows users to oversee agents working in separate zones (for example, one agent might be filling a form on one site while another scrapes data from a different site). The visual design helps maintain a separation between human and AI actions while still collaborating. You can literally watch the agents operate through Fellou’s “Deep Action” feature, which is a bit like watching a live demonstration of your tasks being done for you (wired.com).
Fellou doesn’t stop at web apps; it also has some local-first, on-device execution (wired.com). This means it can access local files or applications in a controlled way, ensuring privacy (sensitive data doesn’t leave your device). For instance, Fellou could potentially read a PDF from your computer and use its content while filling a web form, all internally. They stress privacy – that agents can work with your data without sending it out, which appeals to business users with confidentiality concerns (wired.com).
Classification: Fellou is a bit of both worlds. At its core, it’s a browser-based agent platform, because everything happens in or through the Fellou browser. However, it also has OS-level touches, since it can handle local files and even automate desktop applications to some extent (the company claims it spans “web pages, local files, desktop applications and web applications” (wired.com)). Likely, Fellou’s browser is extended with abilities to launch or control certain desktop apps if needed (e.g., it might have plugins to interact with MS Office or other common tools). But you don’t run an external agent – it’s all integrated into the custom browser software. So if you use Chrome or Safari normally, you’d use Fellou’s browser instead, and that browser has agent capabilities built-in.
Use Cases: Fellou highlights many: from personal productivity (job hunting, travel planning where it finds flights/hotels/activities and even builds a mini-itinerary site (wired.com)), to professional scenarios (a cloud engineer used it to automate a server ops workflow; a real estate agent had it gather leads from various property sites (wired.com)). Because it can coordinate between multiple sites and data sources, it’s great for research and aggregation tasks. Also, since multiple agents can run in parallel, tasks that are independent can be done simultaneously, speeding up workflows – something a single linear agent wouldn’t do. This hints at a more distributed AI approach inside Fellou.
On the market traction side, being the “first agentic browser” gave Fellou a lot of press. That Wired article was actually sponsored content (wired.com) (so they clearly were marketing heavily), but it nonetheless indicates interest: a browser isn’t trivial to get 1 million users for in a few months, especially a new one. The fact that people were willing to switch browsers for it means the value proposition resonated. Fellou was also one of the startups working with Microsoft (Windows 365 for Agents program), showing it’s considered a serious player.
In conclusion, Fellou’s agentic browser is a holistic approach to merging AI and daily web use. Instead of an AI tacked onto existing tools, they rebuilt the tool (the browser) around AI. For a non-technical user, this can be one of the most accessible ways to use an AI agent – you don’t have to write prompts in a special app; you just use your web browser like always, but now it has magic powers to do things for you. It’s a glimpse of how browsing might evolve: more interactive, more automated, and with a sense that your browser is a collaborator, not just a viewer. Going into 2026, it will be interesting to see if major browser makers (Google Chrome, etc.) adopt similar ideas or if they partner with companies like Fellou to bring agentic features to mainstream browsers.
9. Genspark AI Workspace – Type: Hybrid (Web, Desktop, Multimodal)
Genspark presents itself as an “all-in-one AI workspace” and personal super-assistant that goes beyond what typical agents offer. Launched in early 2025, it gained attention for integrating AI into everyday productivity tools (like email, calendar, slides) and even extending into the physical world via phone calls. The core concept is that Genspark is like your AI generalist that can help with a range of tasks across different mediums – text, voice, and direct action.
Capabilities: Genspark’s so-called “Super Agent” can do things such as make actual phone calls autonomously using AI-generated voices (genspark.im). That is relatively unique – while most agents stick to digital interactions, Genspark can, for example, call a restaurant to make a reservation or call a client to deliver a message. It leverages text-to-speech and speech recognition to handle real calls, acting as your proxy. Additionally, Genspark can create documents and slides (it has features like AI-generated Google Slides presentations) (genspark.ai) (genspark.im). It ties into your Gmail, Calendar, Drive, etc., to act on emails or schedule events automatically (genspark.ai). Essentially, Genspark aims to be a personal AI secretary, with access to your communication and productivity apps, plus the ability to converse.
To illustrate, say you’re organizing an event: Genspark could send invites via email, follow up with people, book a venue (either online or by calling), arrange the schedule on your calendar, and generate a slide deck for the event briefing – covering both the digital paperwork and the coordination, possibly even phone negotiations. Another example: for sales outreach, Genspark might draft emails, then actually call leads who prefer phone contact, all while logging notes in the CRM.
Classification: Genspark is a hybrid platform. It’s delivered as a web and mobile app (AI Workspace) (openai.com) where users can prompt the agent, but it connects deeply with both web services and device capabilities. It’s not controlling your entire OS like Simular, but through APIs and integrations, it can operate on your behalf in many apps (especially Google Workspace and possibly Microsoft 365 given the similar nature). It also has a presence on smartphone (iOS/Android apps) (openai.com), meaning you can use it on the go and it might do things like call someone via your phone. Its use of voice (calls) sets it apart as a multimodal agent: text, GUI actions, and voice all in one.
Adoption and Context: Genspark was notable enough that OpenAI’s own blog highlighted it as a case – mentioning it launched personal agents with GPT-4.1 integration (openai.com). That implies Genspark leverages top-tier models (OpenAI’s and others) under the hood. It’s an example of a startup using large models to craft a specialized experience. In terms of pricing, Genspark likely follows a SaaS model (perhaps freemium with a monthly plan for higher usage), but specifics aren’t cited. The focus seems to be on productivity, targeting professionals who have lots of busywork across emails, meetings, content prep, etc.
In terms of performance, the “benchmarks” for Genspark would be more about user satisfaction – how well does it actually handle real-life workflows? It’s not a competitor on GAIA or WebArena in the academic sense; instead, it competes on convenience and reliability. One could imagine a metric like “time saved per user per week” or successful completion of delegated tasks. Genspark’s strength is the breadth of its integration: rather than being the best at pure web automation or pure coding, it does a little of everything well enough to be useful.
For a non-technical audience, Genspark might be one of the more approachable agents. It sits on top of familiar apps (Gmail, Calendar) and acts kind of like an AI-powered butler for your digital life. By late 2025 it was reportedly already showing off making phone reservations and designing slide decks autonomously (genspark.im), which is pretty impressive as a demo of capability. It might not be as famous as AutoGPT or ChatGPT plugins in media buzz, but in practice it could be incredibly handy for an individual.
In summary, Genspark demonstrates the personal assistant style agent, integrating across various tools and even crossing into voice calls. It highlights that agentic AI isn’t just about web browsing or coding; it’s also about handling our day-to-day “glue work” – emails, scheduling, routine communications – which can free us up significantly. As the tech matures, we might see Genspark-like functionality appear in popular platforms (for example, one could imagine Google or Microsoft building similar capabilities into their office suites, given the clear value). For now, Genspark stands as a compelling third-party solution showing what’s possible when you give an AI eyes on your apps and a voice to interact with the world.
10. O-mega AI Personas – Type: Multi-agent Team (Enterprise & Productivity)
Rounding out the list is O-mega.ai, a platform with a unique spin: it frames AI agents as an autonomous workforce of personas – essentially a team of digital workers, each with a specialized role and identity, that can collaborate to get work done. Instead of having one monolithic AI that tries to do everything, O-mega lets you deploy multiple agents, each configured with particular skills, tools, and even “personality” traits suited to different tasks (o-mega.ai). It’s like hiring a team of AI employees: you might have an AI analyst, an AI marketer, an AI sales rep, etc., and manage them through O-mega’s platform.
Concept and Capabilities: O-mega emphasizes giving each agent a persona – a backstory, role definition, and scope of tools – which helps constrain and guide their behavior (o-mega.ai). For example, you could create an “Analyst Alice” agent who is detail-oriented, tasked with data analysis and report writing, and equipped with tools like Excel and a BI dashboard (o-mega.ai). Simultaneously, “Marketer Molly” might be an agent with a creative tone that handles social media posting and copywriting. These personas behave consistently in their style and methods, which is useful for aligning with company voice or processes. O-mega provides a dashboard (“mission control”) to oversee these agents, set their objectives, and monitor their outputs (o-mega.ai).
A powerful feature is that each persona can have its own accounts and browser/email profile (o-mega.ai). That means the “Sales Rep” AI could have a unique email address to communicate with clients, its own login to the CRM system, etc., all separate from other agents (o-mega.ai). This compartmentalization not only helps with tracking and auditing (you can see which AI did what) (o-mega.ai), but it also avoids cross-contamination (the marketing AI won’t accidentally use the sales AI’s context). Essentially, O-mega’s agents operate like distinct users on a team, each with proper credentials and data access for their job.
Use Cases: O-mega is clearly targeting business workflow automation. Some scenarios given include:
Customer Support: a “Support Agent” persona that can read support tickets and respond helpfully (possibly integrating with knowledge bases) (o-mega.ai). This AI would handle routine inquiries autonomously.
Social Media Management: a persona that creates and schedules posts, engages with comments, and maintains the brand tone on platforms like Twitter or LinkedIn (o-mega.ai).
Sales Outreach: e.g., “Pipeline Pro” that finds leads, sends personalized emails or LinkedIn messages, and follows up, using CRM and communication tools (o-mega.ai).
Internal Operations: an HR agent that onboards new hires by sending forms, setting up accounts, etc., or an agent that does weekly data aggregation and reporting for the team (o-mega.ai).
The fact that you can run multiple agents in parallel is a strength (o-mega.ai). Need to scale up customer support during a sale? Spin up 5 more support AI personas – they can all work simultaneously on different tickets, akin to adding manpower (except it’s AI-power) instantly (o-mega.ai). This parallelism and scalability is something companies dream of because it means meeting demand surges without hiring/training extra staff. And since each AI persona is somewhat modular, you could improve or replace one without affecting others.
Classification: O-mega’s approach is enterprise-focused, multi-agent. Each persona can use both web and local tools as needed – the platform integrates with a wide range of software (Slack, Google Suite, Salesforce, Shopify, Jira, GitHub, and many others) via connectors (o-mega.ai). So, it’s not strictly a browser agent or an OS agent, it’s more of an integration layer on top of many services. The agents operate through APIs or simulated UIs of those services. For instance, if a persona needs to post on Twitter, O-mega might use Twitter’s API or a headless browser to let the agent do that. If it needs to run a SQL query on a database, it uses a connector to that database. This means O-mega agents can reach deeper into systems than a generic browser agent could, because direct integrations are possible. It also implies O-mega likely uses a combination of GPT-like models and possibly rule-based automations for reliability on certain structured tasks.
Governance and Limitations: By giving agents defined roles and identities, O-mega aims to keep them aligned and manageable (o-mega.ai). You wouldn’t want a marketing AI to start doing finance tasks – personas prevent that. It also allows setting specific permissions per agent, which is important for data security (e.g., the support agent might have read-access to customer data but not the ability to delete accounts, etc.). O-mega stresses safeguards: for example, if an AI has its own email or social login, measures are needed to ensure it doesn’t violate policies or get duped by phishing (o-mega.ai). The personas approach inherently creates silos that might limit the blast radius of a mistake by any one agent.
Users do need to invest time in configuring these personas (providing guidelines, connecting the needed tools, setting objectives) (o-mega.ai). It’s not entirely “plug-and-play”; it’s more like hiring an employee – you have onboarding and training for your AI personas. But once set up, they can run largely on their own, with the ability for you to supervise and tweak as needed.
In summary, O-mega AI Personas represent a scalable, team-based strategy to AI agents. For a non-technical user, think of it this way: instead of one AI helper, you get a whole team of specialized helpers, each really good at their domain, and you orchestrate them like a manager. This can mirror how real organizations work and could slot more naturally into existing structures (for example, you plug an AI into each department under human oversight). It’s a bit more involved to set up than a single agent that you ask anything, but it stands to potentially deliver more robust results in complex environments because each persona can be optimized for its niche. As we head into 2026, approaches like O-mega’s could become more common, especially in businesses that want to adopt AI but with fine-grained control – it’s a way to embrace automation while still maintaining order and identity.
4. Key Use Cases and Applications
Now that we’ve surveyed the major agents, let’s look at how these agents are actually being used in the real world. What problems are they solving best, and where are people finding value in deploying an AI agent? Across the board in late 2025, a few key use case themes emerged:
Web Research and Data Gathering: One of the most popular uses of agentic AI is automating online research. Instead of manually visiting dozens of websites to collect information, an agent can do it for you. For example, researchers have used ChatGPT’s agent to gather pricing data from multiple e-commerce sites and compile a comparison. Manus AI users delegated market research – the agent would scour industry reports, news, and company websites and then synthesize a report with cited sources (en.wikipedia.org). Agents like Operator and Fellou excel at clicking through search results, pasting relevant bits into a summary, and iterating until they have a comprehensive answer. This can apply to personal tasks (like finding the best travel options, as Operator did by navigating TripAdvisor autonomously) or professional ones (like scanning academic papers for a literature review).
Form Filling and Workflow Automation: Anything that involves repetitive form filling or cross-application workflows is a prime target. Early adopters have agents doing things like: log into a vendor portal, download the latest CSV report, then upload that into another system every week. Or fill out lengthy application forms (grant applications, insurance claims, etc.) by pulling data from a database automatically. OpenAI’s Operator has been used to automate ordering groceries on Instacart – it can search for your usual items, add them to cart, and proceed to checkout (openai.com). Similarly, corporate workflows like updating records in Salesforce or generating invoice entries can be handed to an agent. Browser-based agents handle these especially well since they can navigate web enterprise software that lacks good APIs. In fact, companies see huge potential here: agents can be like 24/7 clerks moving info from one app to another without error or fatigue (o-mega.ai). It’s not glamorous, but it’s a lot of real work hours saved.
Email and Communication Handling: Many of us spend a chunk of the day on emails and messages. Agents such as Genspark are being used to draft emails, sort incoming messages, and even reply to routine inquiries. For customer support, some companies deploy AI agents to read support emails or chats and craft responses (with human approval before sending, in some cases). O-mega’s persona for support is an example where an AI could autonomously resolve a portion of customer tickets (o-mega.ai). On the personal side, an individual might use an agent to triage their inbox – the agent flags important ones, deletes spam, and answers simple requests (e.g., sending your availability or a template response). This is essentially bringing the idea of an email assistant (something people have tried with simpler rules or filters) to a much smarter level with AI understanding context.
Scheduling and Coordination: Agents are good at following rules and steps, which makes them great secretaries. People have agents scheduling meetings (finding open slots between multiple people’s calendars and sending invites), booking appointments (haircuts, doctor visits, etc.), and planning itineraries. For instance, Microsoft’s Copilot in Windows is adding features to let agents schedule events through the Notification Center seamlessly (techcommunity.microsoft.com). On a larger scale, agents like those on O-mega can coordinate entire processes – e.g., an HR onboarding agent that, once a new hire is added to the system, will automatically send them welcome emails, schedule training sessions on the calendar, set up accounts, and so on (o-mega.ai). This kind of multi-step coordination (previously done by office managers or HR staff manually) can be mostly offloaded to an agent with oversight.
Content Creation and Editing: Generative AI by itself can create content, but agents can create content in context, ready to publish. For example, an agent can not only write a blog post draft but also log into WordPress and format it with images and links as a finished article. Or a marketing agent could design an entire slideshow: writing copy for each slide, generating appropriate images, and then actually placing them into a slide deck software. We saw Genspark focusing on slides and marketing content, and Manus listing content creation among its core capabilities (en.wikipedia.org). Social media agents (like an O-mega marketing persona or possibly Fellou’s usage) can generate posts and directly post them across platforms on a schedule – something that can save social media managers hours. The difference from just ChatGPT is the agent can handle the distribution of content too (e.g., it will actually log in and post, not just give you text to copy-paste).
Coding and IT Automation: On the more technical end, agents are being used by developers and IT professionals for various tasks. A coding agent like Devin (one of Manus’s competitors as per its docs) or Microsoft’s GitHub Copilot X agents can take a feature request and not only write code but also open the appropriate tools, run tests, and deploy code (en.wikipedia.org). That’s still early-stage, but it’s happening in some dev workflows. For IT operations, agents can perform things like routine system checks, software installation across systems, or UI testing. Google internally used their Gemini agent for UI testing to speed up software development (blog.google) – the agent would simulate a user clicking through a new app interface to find bugs automatically, which is usually tedious manual work. Simular’s agent on Windows could be set to do overnight software installs or system updates on a fleet of Cloud PCs, for instance. Essentially, any IT task that can be described step-by-step could be delegated.
Personal Productivity and Life Admin: On a personal note, people are starting to use agents for those nagging life admin tasks: renewing a driver’s license online, paying bills through clunky web portals, finding and booking a convenient gym class, and so on. An anecdotal example: someone had an agent go through hundreds of old emails to find warranty expirations and set reminders. Another person used an agent (via Fellou) to plan an entire vacation – the agent researched destinations, checked flight and hotel options, and presented a shortlist or even a compiled itinerary (wired.com). While these use cases are less documented formally, they are emerging among power users of these AI tools.
Public Sector and Accessibility: Interestingly, agentic AI is also being explored in government and non-profits to improve accessibility. OpenAI’s Operator team worked with the City of Stockton to use the agent for helping residents fill out city service forms online (openai.com). Not everyone is tech-savvy or physically able to navigate complicated websites; an AI agent can assist or even do it on the user’s behalf with just a simple instruction. Similarly, there’s potential in accessibility: an AI agent could act as a voice-activated interface for someone who can’t use a mouse, by internally moving the mouse where the person says to. While not the main focus commercially, these are important applications where AI agents could increase inclusivity and usability of digital services.
In all these use cases, it’s not about creating brand new capabilities, but rather shifting the burden of execution from human to AI. The tasks themselves are usually the same tasks humans used to do on a computer. What’s changed is now an AI can handle the heavy lifting – clicking all those buttons, copying those fields, monitoring those updates – freeing humans to focus on higher-level decision-making or creative work.
It’s worth noting that in many real deployments, agents are used in a human-in-the-loop fashion currently. For example, the agent might draft 10 customer support answers and a human support rep quickly reviews and approves each before it’s sent. Or an agent schedules some meetings but a human double-checks unusual ones. This is because while agents save time, companies still want assurance of correctness. Over time, as trust and accuracy improve, we may see more fully autonomous operation.
Another big point is cost-benefit: Many companies and individuals are figuring out which tasks are worth automating (economically or time-wise) and which are easier to just do manually. We’ll discuss cost in Section 7, but some tasks, while doable by an agent, might not be efficient to outsource to AI if they’re quicker for a human or if the AI would incur a big API cost. So part of current usage patterns is experimenting to find the sweet spots where agents provide the most bang for the buck.
Overall, the most successful use cases so far are the ones where the task is well-defined, doesn’t require subjective judgment, and involves a lot of clicking or routine cognitive effort. Agents struggle more with highly open-ended goals or those needing complex real-world reasoning (though they’re improving rapidly). In Section 5, we’ll look at how we measure that success – the benchmarks that tell us how far agents have come and where they still fall short.
5. Benchmarks & Performance
Evaluating how “good” an AI agent is at using a computer is a tricky affair – after all, these agents perform multi-step tasks with lots of possible points of failure. However, the AI community has developed several benchmarks to measure agent performance on standardized tasks. These benchmarks help compare different agents and track progress over time. Let’s explore some of the most important benchmarks and what we know about various agents’ performance on them as of late 2025:
WebArena and WebVoyager: These are benchmarks focused on web browsing tasks. They simulate tasks like “Find the price of X on Y website” or “Navigate through an online store to add a product to cart.” Agents have to control a browser to achieve goals within a time/step limit. OpenAI’s CUA (Operator) model made news for achieving state-of-the-art scores on WebArena and WebVoyager (openai.com). Essentially, it solved more of the tasks successfully than any prior agent. Google’s Gemini agent also reports leading performance on WebVoyager (blog.google). A high score in these means the agent can handle diverse websites (some with tricky layouts or dynamic content) robustly. It’s like testing how well an AI can be your web intern.
Online-Mind2Web: This is a benchmark (introduced by an organization called BrowserBench, as hinted in Google’s blog) that evaluates an agent’s ability to follow instructions using the web as a tool. It might involve multi-step info gathering or web-based reasoning. Google stated that Gemini’s model had the best quality and latency on the BrowserBench Online-Mind2Web test, meaning it completed tasks most accurately and quickly (blog.google). The latency vs. quality graph they shared showed Gemini’s agent achieving over 70% success with significantly lower time than others – a clear sign of efficiency (blog.google). Competing agents likely included older ones like WebGPT or early AutoGPT variants that were slower.
AndroidWorld / Android UI tasks: Google also mentioned AndroidWorld as a benchmark (likely tasks in Android apps). Gemini’s agent did very well there too (blog.google), highlighting its multimodal prowess. This is important because it shows agents moving beyond just web pages to mobile app interfaces.
GAIA (General AI Assistant Benchmark): This is a comprehensive test introduced around 2025 to measure an agent’s general problem-solving with tools and multi-step reasoning (en.wikipedia.org). GAIA tasks are tiered (Level 1 basic, Level 2 intermediate, Level 3 complex) and can involve a mix of modalities (text, web, maybe even coding). Manus AI’s team reported their agent achieved state-of-the-art on GAIA, even exceeding an internal OpenAI agent system (en.wikipedia.org). We saw numbers like ~86.5% for Manus on Level 1 vs ~74% for OpenAI’s agent and ~15% for GPT-4 w/ plugins (en.wikipedia.org). This huge gap (15% vs 86%) underscores how a properly orchestrated agent can dramatically outperform a raw LLM that’s just been given tools. GAIA is one way to quantify “autonomy”: GPT-4 is very smart but only got 15% because it might not strategize over 50 steps well, whereas Manus specialized in that. Human experts score ~92%, so top agents are closing in on human problem-solving (at least on the structured tasks GAIA includes).
OSLevel / UI benchmarks (e.g., OSWorld): To test agents on using actual operating system GUIs, OSWorld was created. It evaluates how well an agent can complete tasks on a computer via screenshots (like opening an app, or saving a file). Claude’s 14.9% vs 7.8% stat (anthropic.com) on OSWorld was notable, indicating Claude was roughly twice as good as the next competitor, but still a long way from perfect (since even 22% after more tries is low). This shows that full OS manipulation is still at an early stage. We don’t have public OSWorld numbers for others like Operator or Simular yet – possibly because OSWorld emerged from Anthropic’s context. We might see that become a standard where Simular and Microsoft measure themselves in 2026.
TAU (Tool-Augmented Usage) Benchmarks: Anthropic referenced TAU-bench (for tool use in specific domains) (anthropic.com). The idea here is to see if an agent can use external tools (like search APIs, or a calculator, or a booking API) when needed. Claude improved from ~62% to ~69% on a retail scenario with its newer model (anthropic.com). This kind of benchmark, while narrower, ensures the agent isn’t just relying on end-to-end learning but can effectively invoke tools or sub-routines. Many modern agents incorporate a form of this by design (function calling, etc.), so TAU or similar tests gauge how seamlessly they do it.
Economic Benchmarks – “Tasks per Dollar”: With agents, it’s not just about raw success rate – cost efficiency matters too. Running an AI agent can be expensive if it consumes a lot of API calls or computation. There has been discussion of measuring how many standard tasks an agent can do per $1 of API cost, to judge economic viability. One data point from the open-source BrowserGPT/Browser-Use project showed a comparison: GPT-5 (or a hypothetical GPT) might do ~3 tasks per dollar, Gemini 2.5 about ~7 tasks, their specialized Browser-Use model ~53 tasks per dollar (browser-use.com). They claimed their model was 15× cheaper than competitors for browser tasks, at ~82% of SOTA accuracy (browser-use.com). This highlights the trade-off: an open optimized model might not reach 100% of the top performance, but if it’s significantly cheaper, one could use it at scale more feasibly. Companies or users with large workloads will care about this metric – an agent that costs $0.50 per task vs $0.05 per task is a big difference in ROI. We expect more formal benchmarks in this vein to emerge (maybe a “cost-adjusted GAIA score”).
Latency and Speed: While not a single number benchmark, speed is often measured. Google’s scatterplot with latency vs quality (blog.google) is an example – ideally an agent is both accurate and fast. An agent that takes 10 minutes to do what a user can in 2 minutes isn’t very attractive, even if it’s fully autonomous. So, benchmarks are starting to include time-to-completion and sometimes even interaction efficiency (how many steps or backtracks did the agent need). The faster models like Gemini Flash (as referenced) might sacrifice some accuracy for speed, which is useful for near-real-time use. Amazon’s Nova explicitly focuses on speed and “75% less cost” rather than boasting best raw accuracy (theverge.com), showing a possible strategy to optimize around throughput and cost rather than absolute intelligence.
Domain-Specific Benchmarks: There are also niche benchmarks. For instance, BrowserQA (where an agent must find answers on websites to questions), or MiniWoB (a classic suite of web mini-tasks like “login” or “filter results” which has been used in reinforcement learning research). Those often underlie some of the above comprehensive ones. Agents in 2025 have basically far surpassed earlier RL agents on MiniWoB tasks, etc. Now the frontier is these more complex sequences.
It’s important to note that benchmark results can be a bit self-reported and not always independently verified, especially when companies announce them in blogs. For example, Manus’ GAIA results were their own report, and some experts were skeptical if certain tasks were cherry-picked (en.wikipedia.org). However, the existence of competitive benchmarks has definitely pushed progress. OpenAI, Anthropic, Google, etc., all training on or evaluating on these means each iteration of their models/agents gets better at the measurable tasks.
One more interesting “benchmark” in the wild is the ARC browser challenge (Autonomous Researcher Challenge) or the experiments like giving an agent $100 and seeing if it can make profitable trades (one did attempt to hustle money on TaskRabbit and Fiverr). While not formal benchmarks, these highlight real-world performance. Most of those early 2024 experiments (like AutoGPT’s attempts to make money or achieve a goal like “chaosGPT”) had underwhelming outcomes. By late 2025, if one repeated the “HustleGPT” experiment (agent given some money and asked to multiply it), results might be improving – but these are anecdotal and vary widely.
Finally, human evaluation remains crucial: Some companies have internal benchmarks where humans rate the outcome of an agent’s work (e.g., is the report it wrote accurate and useful?). These aren’t as cut-and-dried as a numeric score but are used alongside the automated metrics.
In conclusion, benchmarks show rapid improvement in agent capabilities: tasks that seemed near impossible for AI a couple years ago (like completing a 50-step web workflow) are now being done by top agents with some reliability. However, there’s still a gap to human-level on many complex or lengthy tasks (the GAIA human 92% vs agent ~58% on hardest level (en.wikipedia.org), or OSWorld 15% success). The next year or two of development – especially with even more powerful models (GPT-5? Claude 4? Gemini Pro versions) – might close those gaps further.
It’s also worth pointing out that not all improvements come from bigger models; often it’s from better training and prompting techniques (like reinforcement learning from feedback on these tasks, or adding memory and planning modules to reduce errors). For example, Operator and others are trained with RL on actual task sequences, which made them much better than naive GPT-4 + instructions.
Benchmarks help identify the weak spots too: CAPTCHAs, for instance, are an area where agents still fail (by design CAPTCHAs block bots). Most benchmarks avoid CAPTCHAs, but in the real world that remains a barrier (more on that in next section). Also, error recovery is hard to benchmark – some agents might have good scores but if something off-script happens, they flop. To address that, researchers might develop robustness tests (like intentionally changing a web page slightly and seeing if the agent adapts).
In summary, as of late 2025, the best agents are demonstrably effective at a range of standardized computer-use tasks, often achieving success rates in the 70–90% range on moderate tasks, which is a huge leap from near-zero just a couple years prior. The trajectory suggests that on benchmarks at least, 2026’s agents will push even closer to human-level – though the last mile of reliability is always toughest. Next, we’ll consider the challenges that these benchmarks don’t fully capture: those messy, unpredictable real-world issues that can trip agents up, and how developers are addressing them.
6. Stealth, Safety & Challenges
While AI agents have advanced rapidly, they still face significant challenges and limitations when using computers. Additionally, the very nature of an AI running around clicking things raises safety and ethical issues. In this section, we’ll explore some key hurdles: from practical issues like being detected as a bot or getting stuck, to broader concerns like errors, misuse, and costs. We’ll also look at strategies being employed (like “stealth mode”) to overcome these challenges.
1. Anti-Bot Detection & “Stealth”: Many websites have defenses against automated browsing – CAPTCHAs, bot-detection scripts, login verifications, etc. A visible challenge for browser agents is that they can be recognized as bots and blocked. To counter this, agent developers use stealth techniques. For instance, the open-source Browser Use library includes a Stealth Mode that automatically bypasses CAPTCHAs and anti-bot systems (browser-use.com) (browser-use.com). This can involve simulating human-like mouse movements, random delays between actions, rotating user-agent strings, or using proxy IPs to appear as different users. Some agents even integrate third-party CAPTCHA-solving services or have the model itself solve simple visual CAPTCHAs (though OpenAI’s Operator explicitly avoids solving CAPTCHAs autonomously for safety (openai.com)). Stealth mode also keeps the agent “logged in” by preserving cookies/sessions, so it doesn’t trigger alarms by logging in fresh for every action (browser-use.com). Without stealth measures, an agent might complete 5 steps of a task only to hit a “Are you human?” roadblock on the 6th, halting the automation. So for agents to be reliable, especially in enterprise use on websites, beating bot detection is key. Browser automation tools have done this for years (like puppeteer stealth plugins), and now AI agents are inheriting those tricks. However, it’s a cat-and-mouse game: as AI bots proliferate, websites might tighten anti-bot measures, leading to an ongoing stealth tech arms race.
2. Hallucinations and Errors: AI language models, which power these agents’ brains, are notorious for hallucinating – producing outputs that are untrue or not grounded. In an agent context, a hallucination can translate into a wrong action. For example, an agent thinks a certain button exists and tries to click it, or it reads an instruction “book a flight” and hallucinates a step like “open FlightBookingPro app” which isn’t a real thing. These mistakes can derail a process. As Simular’s team noted, if an agent has to do thousands of steps, even a 1% hallucination rate per step compounds to a high chance of failure over the whole task (techcrunch.com). Strategies to reduce this include: making the model more deterministic (setting high confidence thresholds for actions), adding verification steps (the agent double-checks if what it’s about to do makes sense given the screen state), and employing fallbacks (if the agent is unsure, it can pause and ask the user for guidance rather than guessing). OpenAI’s Operator is trained to self-correct and, importantly, to hand back control to the user when it’s stuck or might do something risky (openai.com) (openai.com). That hand-off is a graceful failure mode – it ensures that a hallucination doesn’t cause havoc; instead, the agent effectively says, “I’m not confident here, please take over.” Over time, as models get more robust, these interventions might decrease, but they remain a critical safety net currently.
3. Getting Stuck or Loops: Agents can get into infinite loops or dead-ends. For instance, an agent might navigate into a page it didn’t expect and keep refreshing or clicking the wrong element repeatedly. Early AutoGPT users often saw agents looping uselessly on tasks. Modern agents try to avoid this by keeping track of what they’ve done (“memory”) and having logic like “if I’ve tried X three times and it failed, try Y or stop.” Some frameworks include a time or step limit after which the agent stops to prevent runaway loops. Also, the use of search (for web) or heuristics can break loops – e.g., if an agent can’t find info on a site, a smart one might automatically pivot to a search engine. But loop prevention is still not perfect; users sometimes have to abort a task when noticing the agent cycling. This is an area of active improvement, often addressed with better planning algorithms or giving the agent a form of oversight (like a higher-level controller agent watching the main agent’s steps for obvious nonsense).
4. Partial Understanding & Context: While agents are good with explicit instructions, they can misinterpret nuances. For example, if you say “Book me a flight after 8am,” an agent might not inherently know you mean local time or whether 8am is departure or arrival time. Human context and common sense are hard. Agents like Claude or those on O-mega that incorporate “company culture” or style guidelines attempt to reduce misalignment (o-mega.ai) (o-mega.ai). But misunderstanding user intent remains a risk – the agent might do exactly what you said, which might not be what you meant. Solution approaches include requiring confirmation for ambiguous actions (“I found flights at 9am and 11am, which would you prefer?”), or using more sophisticated prompt engineering to capture user preferences. This touches on safety too, as an AI that misinterprets could cause harm (imagine: “remove duplicate files” accidentally interpreted as “wipe all files”).
5. Safety & Harmful Actions: An agent that can use a computer independently could do damage if misused or if it malfunctions. This includes: visiting malicious sites, downloading malware, changing system settings in a dangerous way, or even being socially engineered via prompt injection (a webpage could contain hidden text that instructs the agent to do something harmful). Because of this, developers are building guardrails. Google’s agent has an inference-time safety layer that checks each action before execution for potentially harmful or high-stakes operations (blog.google). For example, it might block actions that attempt to bypass security or access unauthorized areas. OpenAI’s agent similarly won’t do certain things without asking the user (like making payments, or deleting data). Agents are often restricted from certain content – e.g., they typically won’t navigate to known disallowed categories (like dark web sites, or explicit content) or will at least warn the user. There’s also the measure of containment: running the agent in a sandbox environment means even if it tried to do something wild (like delete files), it’s constrained to its sandbox. Microsoft’s agent workspace concept is exactly to allow freedom in a controlled bubble (techcommunity.microsoft.com).
6. Failures in Long Tasks: The longer and more complex the task, the more points of failure accumulate. As discussed with hallucinations, the probability of success drops as steps increase (unless the agent is near-perfect per step). This is why agents sometimes do well on short tasks but falter on very long ones without checkpoints. Some systems break tasks into sub-tasks and tackle them one at a time, perhaps even spinning up sub-agents for each part (Manus internally does this with a planner and executor agents (baytechconsulting.com)). That reduces the cognitive load and error compounding. But coordinating sub-tasks introduces its own complexity. For now, many users keep agents’ assignments bounded: e.g., instead of “draft a 50-page report and create slides and send emails to stakeholders”, one might separately ask the agent for the report, then separately for the slides, etc. This reduces the chance of a catastrophic failure at step 49 ruining everything. In enterprise, this is handled by designing clear workflows and maybe checking output at intermediate stages.
7. Cost and Efficiency: Agents, especially when using powerful models like GPT-4 or Claude, can be expensive to run. A naive agent might make hundreds of model calls for a complex task (for each step reasoning). That can rack up token costs. One challenge is optimizing the agent’s reasoning to be succinct and not wasteful. Some systems use cheaper models for straightforward parts and only call big models for hard parts (a form of model cascading). Others, like the Browser Use platform, trained a custom smaller model to handle common browser actions with much lower cost per action (browser-use.com). The challenge is balancing cost vs. accuracy. If an agent tries to be cheap by using a weaker model and then fails more often, it could end up costing more due to retries. So developers are profiling how many steps a given model typically needs and where it’s worth using a pricey model to cut down steps. As the user, a limitation here is you might have to be mindful of asking an agent to do an extremely large task if you’re paying per call – the token meter is running. In consumer cases like ChatGPT’s agent, usage might be throttled or counted against a limit. Economic viability will determine whether businesses truly deploy these at scale or just for niche cases. By improving efficiency (as Amazon aims with Nova Act’s cost focus, or Browser Use with their model), this challenge is gradually being mitigated.
8. Ethical and Social Issues: Agents can also amplify problems like bias or misinformation. If an agent is automating hiring by screening resumes, any bias in its model or instructions could lead to unfair outcomes. Or an agent might pick up a piece of false information online and act on it (maybe even propagating it in a report or email). Ensuring agents are truthful and fair is part of the safety challenge. Companies are likely incorporating their existing AI alignment techniques (like Anthropic’s Constitutional AI, OpenAI’s RLHF) to make agents follow ethical guidelines. For example, an agent might be instructed to never violate privacy – so if it somehow got access to personal data, it should refrain from leaking it. In practice, we’re still learning how to enforce such behavior once the agent is out in the wild doing things.
9. Limitations in Scope: It’s also worth noting that current agents, while broad, cannot do everything. They might lack integration with certain software that doesn’t have a web interface. For instance, an agent cannot yet operate your graphics-intensive CAD software unless someone specifically connects it. Similarly, tasks involving judgment calls (should we approve this loan?) are risky for full automation without human oversight. Agents also cannot easily handle tasks that require real-world interaction beyond the computer (except something like Genspark’s phone calls, which is a special case). Some tasks remain off-limits due to policies – e.g., agents won’t hack or perform security bypass; they won’t generally make purchases involving money unless explicitly allowed; they shouldn’t access personal data without permission, etc. These limitations are partially intentionally imposed, partially technical.
In light of these challenges, best practices have emerged: keep a human in the loop for critical decisions, log all agent actions for audit, start with small tasks to build trust, and iteratively expand usage as confidence grows. Many companies deploy agents internally with a “shadow mode” first – the agent does the task but its output is double-checked by humans 100% at the beginning, then maybe 50%, etc., until trust is earned.
“Stealth” specifically deserves a final note: it highlights an interesting tension – we are creating agents that deliberately try to avoid detection as bots (browser-use.com). This is practically necessary for them to function on today’s web, but it raises ethical eyebrows (it’s like training AIs to pretend to be human). Platforms might respond by adjusting terms of service or technology; some might welcome AI agents (e.g., maybe some sites will have explicit agent APIs to encourage proper use) while others will fight them to prevent abuse (spam bots, etc.). In enterprise contexts, stealth is also about reliability – e.g., ensuring an agent stays logged in to a system without constant re-authentication, which is more a convenience thing.
In conclusion, today’s agents are powerful but not foolproof. They operate with careful constraints and plenty of guardrails. Users need to be aware of these limitations to use agents effectively and safely. Many current failures result not from the agent’s inability per se, but from a scenario not anticipated by its programming or a safety trigger rightly stopping it. The development focus now is as much on making agents robust and safe as it is on making them smarter. Overcoming these challenges will be key to broader adoption – nobody wants an agent that randomly messes up crucial work or one that gets banned by half the websites it tries to use. The progress on these fronts in the last year is encouraging: for instance, the jump from very flaky AutoGPT loops to more polished and self-aware Operator or Gemini agents shows that with the right training and system design, many of these pitfalls can be mitigated. However, it will be an ongoing effort as agents become more autonomous.
7. Platforms, Pricing & Market Impact
The rise of agentic AI has not only been a technological story but also a market story. Let’s discuss the major platforms and products making these agents accessible, how they are priced and offered, and the broader market impact – who’s adopting these agents, how it’s affecting productivity, and what the competitive landscape looks like going into 2026.
Platforms & Offerings: We’ve already identified many key players and how they deliver their agent solutions:
OpenAI: Offers the ChatGPT agent (formerly Operator) to end-users via ChatGPT Plus (a $20/month subscription gives access to GPT-4 and agent mode). For developers, OpenAI hasn’t opened up the full agent as an API yet, but they do provide function calling and tools which developers can use to build their own agents. So, OpenAI’s model is a mix: direct consumer service and enabling building blocks via API. The agent mode in ChatGPT is essentially bundled into the subscription – there isn’t an extra fee per use (aside from maybe limits on usage).
Google: Provides the Gemini Computer Use model via Google Cloud (Vertex AI) (blog.google) (blog.google). This is more of a developer offering – companies can integrate it into their systems. Pricing likely follows API usage on Google Cloud (like per 1000 actions or per time unit). Google also is integrating agent capabilities into its own products (e.g., AI mode in Google Search, UI testing in Firebase, etc. (blog.google)). So, for enterprises, one might access it through Vertex AI with whatever contract or pay-as-you-go pricing Google sets (not publicly detailed yet). For consumers, we might see it indirectly via improved features in products like Assistant or Search.
Microsoft: Has a multifaceted approach. Microsoft 365 Copilot (for Office apps) is sold as an add-on (for enterprises, it was priced around $30 per user per month for the suite, though that includes more than just agentic features). The Windows agent connectors and Windows 365 for Agents are oriented towards enterprise clients – Windows 365 Cloud PC itself has a subscription cost (like a per-user monthly fee for a Cloud PC), and adding agent capability might be an additional service or included for those in the preview. Microsoft’s strategy often involves bundling AI features to add value to their subscriptions (Windows, Office, Azure). So an organization might pay for Microsoft 365 E3/E5 licenses plus Copilot, and get the agent integration as part of that. There’s also Copilot Studio, presumably part of Azure or Power Platform for building custom agents, which could be priced per bot or per action. Given their enterprise focus, expect volume licensing deals rather than consumer pricing. Also, Microsoft’s partnership with startups (Manus, etc.) implies they might route some customers to those specialized solutions rather than building everything in-house – or even eventually acquire or invest more heavily if one becomes crucial.
Startups (Manus, etc.): Manus uses a subscription model directly to consumers/professionals. As we saw, Manus offers Free (limited) and paid tiers: $39/mo Starter, $199/mo Pro, and also team plans ($39/seat with min 5 seats) (en.wikipedia.org) (en.wikipedia.org). These give monthly credit allocations for tasks (with each task consuming some credits proportional to complexity). That pricing indicates they target professionals and small businesses willing to invest for significant automation. Manus hitting $100M ARR means they had tens of thousands of paying users (e.g., at $39/mo, $100M ARR would be ~213k subscribers; at $199/mo perhaps fewer but likely a mix). That’s a strong market validation for direct agent service. Other startups like Genspark likely have similar SaaS pricing (maybe $20-$50/mo range for individual, more for business with multiple seats). Simular might monetize by enterprise licensing or usage fees; currently on Mac it might even be free or a one-time fee as they build user base, but since they raised funding, they’ll likely go for a subscription or an enterprise contract model.
Open-Source and Self-Hosted: For more technical users, there are frameworks (like the Browser-Use library, LangChain Agents, etc.) that are open source and free to use, but of course you have to pay for the model inference (unless you run a model locally). The Browser-Use library, for instance, is open source with 74k stars – a huge community adoption (browser-use.com). They also have a managed cloud service (with presumably usage-based pricing) for convenience (browser-use.com). This dual model (open source core, paid cloud for scale) is common. It lowers the barrier for experimentation (free to try on small scale), and then companies that want reliability and scaling might pay for the cloud version. Open-source agents allow companies concerned with privacy to self-host everything: e.g., they could run an open model on their own servers and use Browser-Use for web automation so no data leaves their network – attractive for high-security environments. Of course, the trade-off can be performance (open models might be weaker).
O-mega and Context.ai (Enterprise platforms): These appear to be enterprise SaaS. O-mega.ai likely has custom pricing depending on number of personas, volume of usage, etc. Possibly a base platform fee plus usage. They mention “start for free” on Context.ai and likely O-mega might have a free tier or trial for small teams (o-mega.ai). But large orgs would go into a contract. It’s akin to an RPA software pricing or an enterprise AI platform pricing – which can be in the tens of thousands of dollars for big deployments. Because these integrate with many company systems, they’re sold with value proposition of big ROI, so they can price accordingly.
Market Adoption & Players:
Biggest Players: Right now, OpenAI (with ChatGPT’s user base) is huge in consumer mindshare. Microsoft has reach in enterprise due to existing Office and Windows installations – if they flip a switch and give everyone a built-in agent (Copilot) it becomes ubiquitous. Manus has made a big splash especially in Asia (given the founders and initial user base, possibly a lot of Chinese-speaking users via Monica extension background). Manus being fastest to $100M ARR suggests it might have the largest paying user base among dedicated agent startups. Others like Inflection’s Pi (not mentioned earlier, since Pi is more conversational, but they might add action capabilities eventually) also have users but not specifically for computer control.
Upcoming Players: Amazon is a bit behind in releasing to consumers but with Nova they are poised to be a significant player, especially if they integrate it into Alexa and AWS. Anthropic’s focus is more partnering (with Google, Slack, etc.) – they might not release their own end-user agent app, but they empower others to. For example, there might be agent apps built on Claude via their API. Also, open-source communities (e.g., projects like AutoGPT, AgentGPT) while initially gimmicky, are evolving – perhaps by 2026 we’ll see a strong open agent that anyone can run without paying big API fees (especially if someone like Meta open-sources a powerful model with tool-use).
Where Agents Are Most Successful: So far, agents have been most successful in structured digital environments – web, office tasks, etc., as we detailed in use cases. They have been less successful in tasks requiring real-time continuous adaptation (like real-world robotics, though that’s another frontier beyond our scope) or where objectives are very vague. Also, agents in creative fields (like purely creative writing or art) aren’t as prominent – there, standard generative models suffice; you don’t need an agent to click Photoshop menus if a single model can generate the art directly, for example. So the sweet spot is clearly office productivity and web automation. Within companies, early success stories often involve things like automating a process that took a team of people lots of time (like processing forms, responding to customer queries). There have been publicized pilot projects where, say, an insurance company used an AI agent to handle 60% of customer email inquiries end-to-end, cutting response time from days to minutes. Those kind of results drive interest.
Failures and Learning: Some high-profile experiments failed, which provided cautionary tales. For example, an early attempt by a travel agency to have an AI agent handle booking ended up booking things incorrectly and caused customer issues – it highlighted that oversight and gradual rollout are crucial. Also, AutoGPT’s widely shared failures (like an agent tasked to “destroy humanity” just hilariously creating nonsense files and looping) actually helped calibrate expectations that these aren’t some infallible superintelligences – they make dumb mistakes if not guided properly.
Productivity Impact: When they do work, agents can dramatically speed up workflows. Companies have reported productivity boosts – a report by McKinsey (as also alluded to in the Fellou Wired content (wired.com)) suggests that autonomous agents could significantly increase productivity in the coming years. We’re at the stage where small-scale studies show, for instance, a junior analyst with an AI agent might do the work of 2-3 analysts in some tasks. However, the broad economic impact hasn’t fully hit yet because these agents are just being trialed or used in narrower scopes. 2024-2025 was a lot of piloting; 2026 might see more widespread deployment if early results hold.
Competition & “AI Agent Wars”: It’s a crowded field with overlapping approaches:
Tech Giants (OpenAI/Microsoft, Google, Amazon, Anthropic) – have resources and platforms. They might converge or differentiate: e.g., Microsoft seems to focus on integrating with existing Office/Windows ecosystem deeply (so their edge is user base and enterprise trust), Google focuses on open API and their ecosystem (and they have Android, Chrome to leverage), OpenAI has best pure models and brand among AI enthusiasts, Amazon will push on cost and AWS integration.
Startups – Manus, Simular, Fellou, etc., differentiate by innovating quickly on features or targeting niches (Manus aimed at generalist first and gained hype, Simular targets full OS control, Fellou reimagined the browser UX, etc.). They also often cater to international markets or specific user groups better than big players initially might.
Open-Source – a wildcard that can rapidly catch up as community contributes. For example, if an open agent gets nearly as good as OpenAI’s but at fraction of cost, many businesses might prefer that to avoid API dependency and cost.
Pricing Trends: We might see per-action pricing emerge. Already, some API pricing might effectively be that (like pay per token for the model usage, which correlates with actions). O-mega or Context likely have monthly plans with limits like X agent hours or Y actions. If usage skyrockets, there could even be a “utility computing” model for agents: e.g., $0.001 per action executed. But many enterprise clients prefer predictable subscription costs, so providers might package unlimited use (with fair use clauses) for a flat fee to encourage adoption.
Economic Viability: Early accounts show that when agents work, they can save significant labor costs, which is why companies are excited despite the tech being new. There is a cost in cloud compute, but one well-known calculation was that running GPT-4 at current rates to fully replace a $15/hour worker comes out more expensive per hour (depending on tasks). However, specialized models and scaled usage can tilt the economics. Also, if one agent can amplify one worker rather than replace, the ROI is measured differently (e.g., one worker managing five agent assistants could output 5x work). Benchmarks in economically viable work, as the user prompt hinted, might refer to seeing if an agent can perform tasks for less cost than a human. We’re borderline on that right now – for simple tasks, yes with cheaper models; for complex ones requiring GPT-4, not yet clearly cheaper. But costs of AI inference tend to come down with optimization and cheaper models.
Open vs Closed Source (a bit preview of next section): This also impacts market. Companies may hesitate to send proprietary data through closed APIs (due to privacy or compliance). That’s why Microsoft, OpenAI offer options like on-prem or encrypted instances for big clients, and open-source agent frameworks allow internal deployment. Some big players like IBM (with Watson Orchestrate) or SAP might also integrate agents into their enterprise software – IBM’s Watson Orchestrate (announced 2021) is actually similar in concept, though IBM has been quieter in hype. As this space heats, possibly more incumbents will adapt (for example, UIPath, a leader in RPA, has started adding AI to their automation – they could morph into agent offerings).
Market impact also includes the future of work: There is talk that AI agents could replace or dramatically change certain jobs (like data entry, customer support level 1, etc.). Companies are testing carefully because mistakes by an agent in those roles could damage customer trust. But the momentum is there to at least use them as force-multipliers – maybe a human supervisor for 10 AI support agents rather than 10 human support reps. This is analogous to earlier automation waves but with a smarter engine.
One interesting development: some freelance platforms and services are starting to offer “AI agent” services – essentially someone will configure and run an AI agent to do tasks for you. It’s meta, but it shows new businesses forming around this tech.
In conclusion, the market for AI computer-use agents is rapidly growing. Pricing models are still shaking out – from free open tools, to $20/mo consumer add-ons, to enterprise contracts in the six or seven figures for integrated solutions. The players range from giants baking it into ubiquitous software to agile startups pushing the envelope on features. The next year will likely see some consolidation (not all 10–20 startups will survive; some might be acquired, some will fold if they can’t compete in quality or distribution). We’ll also likely see clearer stratification: basic agent services might commoditize (everyone will have a basic browser agent), but specialized offerings (like the multi-persona concept, or domain-specific agents for finance, legal, etc.) might be where competition moves.
Overall, the excitement is warranted, but companies and individual users are approaching with both optimism and caution: adopting where clear gains exist, but still keeping an eye on the aforementioned challenges. As these agents prove themselves in more deployments and as costs come down, we could see a pretty revolutionary boost in productivity – some call it the start of the era of “AI co-workers” or “digital employees.”
Next, we’ll briefly contrast open vs closed approaches, then dive into what the future might hold as we head into 2026.
8. Open-Source vs. Closed Solutions
In the world of AI agents, there’s an important distinction between open-source frameworks and closed, proprietary solutions. Both have their roles, and understanding their differences can help you decide which path to take for your needs.
Open-Source Agents & Frameworks: These are community-driven projects where the code is publicly available, allowing anyone to inspect, modify, and run it. Examples include the Browser-Use library (open-sourced on GitHub with tens of thousands of stars) (browser-use.com), LangChain’s agent tools, AutoGPT (and its many variants), and Simular’s Agent S2, which was mentioned as being open-source (o-mega.ai). The advantages of open-source are:
Transparency: You can see exactly how the agent works – what models it uses, how it decides to click a button, etc. This can be reassuring for debugging and trust. For instance, a bank might prefer an open framework so their engineers can verify it’s not sending data to unauthorized places.
Customizability: If you need an agent to do something very custom (say interface with a legacy internal app), having the code means you can extend it to support that. Open frameworks often allow plugging in your own tools or models easily.
Cost Control: Open-source lets you run agents on your own infrastructure. You could choose a free or cheap language model (even run one locally on GPUs you own), avoiding API costs. As noted earlier, some open projects fine-tuned their own smaller models (like “Browser Use 1.0” model) which performed well at a fraction of the cost of GPT-4 (browser-use.com). If you have the engineering resources, this can significantly reduce ongoing costs. However, there’s a trade: using a smaller or open model might sacrifice some accuracy (though as we saw, Browser Use’s model still reached ~82% of SOTA (browser-use.com), which might be acceptable for many tasks).
Community Innovation: The open-source community moves fast. For example, AutoGPT, though rudimentary at first, rapidly got contributions adding memory, better planning, etc. New ideas (like integrating a vector database for long-term memory, or new prompt strategies) often appear in open projects first. If you use these, you can benefit from the latest tweaks without waiting for a company’s next release.
However, open-source is not all roses. The downsides include needing technical expertise to set up and maintain. There’s no official support (aside from community forums). Security is in your hands – you must ensure you deploy it safely. And for very advanced capabilities, closed models (like GPT-4 or Gemini) might still outperform anything open-source as of 2025, especially on complex reasoning.
Closed-Source & Proprietary Agents: These include OpenAI’s Operator (the internals of CUA model are not public), Google’s Gemini agent, Anthropic’s Claude agent, and most startup offerings like Manus or O-mega. You can’t get their source code or model weights; you use them through an API or service. The advantages here are:
Performance: These often leverage the most powerful models and have had extensive resources poured into training and fine-tuning. For example, OpenAI’s and Google’s models underwent expensive RL training that open communities can’t easily replicate at that scale. So, for the most challenging tasks, a proprietary agent might simply be more capable or reliable.
Ease of Use: They typically come as polished products or APIs. You don’t need to manage infrastructure or juggle dependencies. ChatGPT’s agent is a click in a UI. Manus has a user-friendly web app and even mobile apps. This lowers the barrier for non-technical users or small teams who just want results, not a project.
Support and Accountability: If you pay for a service like Microsoft Copilot or Manus Pro, you often get some support channel. The provider has an obligation (contract or at least reputation) to keep it running, fix issues, and possibly comply with regulations (like data handling agreements, etc., especially in enterprise contracts). With open-source, you’re your own support.
Integration (in some cases): Proprietary solutions sometimes offer seamless integration with other proprietary systems. For example, Microsoft’s agent connectors in Windows make it easy to interface with Office apps – something an open agent would have to do through possibly clunkier means. Similarly, Google’s agent in their cloud ties into their whole cloud ecosystem (BigQuery, etc., via their unified APIs). If you’re already in one ecosystem, the closed solution from that provider might plug in more neatly.
On the other hand, closed solutions raise concerns like vendor lock-in (you become reliant on that provider and their pricing/terms). And there can be data privacy worries: sending your data to a third-party service might be a no-go for sensitive info unless they offer on-prem or special arrangements (OpenAI and others do have options for dedicated instances or not training on your data, but you have to trust their promises or pay more for guarantees).
Open vs Closed in Practice: Many organizations adopt a hybrid approach. They might use open-source tools for some components and call a closed API for the heavy-lifting AI reasoning. For instance, you could use the open-source Browser-Use library to handle browser automation (the actual clicking, DOM parsing, etc.) but use OpenAI’s GPT-4 as the model guiding it. In fact, that’s how quite a few setups work – open orchestrator + closed LLM. This gets you best of both: you control the flow, but leverage the best intelligence.
Conversely, a platform like O-mega is closed in the sense of being a proprietary SaaS, but it might incorporate some open components or allow plugin of open models if the client prefers. Many closed providers realize that offering some flexibility can attract customers (for example, Azure OpenAI lets you choose your region and promises isolation).
Community vs Enterprise: Open-source agents are popular among developers, hobbyists, and some smaller companies. Enterprises, who prioritize support, compliance, and reliability, often lean towards commercial solutions or at least use those as part of the stack. There’s also an element of trust and liability – if a closed solution causes a problem, there’s a company to hold accountable (maybe not legally easy, but at least they have a business interest to fix things). If an open agent messes up, it’s on your team to sort it.
Longevity and Updates: Closed services will update behind the scenes (like how ChatGPT’s agent improved from Jan to July 2025 as they integrated it). Open projects rely on community or grants – if interest wanes, they can stagnate. However, given the excitement around agents, core projects are likely to continue strongly at least for the next couple of years. There’s also the scenario where a promising open project gets absorbed – e.g., a company might hire the main devs or incorporate it (like how many open ML tools ended up being acquired or offered in cloud).
Case Example: Simular’s decision to open-source Agent S2 (their underlying framework) (o-mega.ai) is interesting: they likely did it to encourage adoption and become a standard, knowing their business can be selling cloud or pro services on top of it (similar to how OpenAI open-sourced some earlier models then built a business on API). If Agent S2 becomes widely used, Simular’s approach might be integrated by others, possibly even improving it further. On the flip side, Manus is entirely closed – and some users expressed desire for an offline/self-hosted version for privacy, which Manus hasn’t provided (likely due to the complexity and their secret sauce). This could limit Manus in markets like certain government or highly regulated sectors that require on-prem solutions. There’s a gap there that open or self-hosted agents could fill.
Which to choose? For a non-technical audience or a small business, a closed solution (like ChatGPT agent or a platform like O-mega) is likely easiest – essentially out-of-the-box. For a tech-savvy team with niche needs or high security needs, exploring open frameworks could be beneficial. They might even run an open agent completely offline (for example, use something like Meta’s open Llama 2 model with a browser automation library, which some have demoed for simpler tasks). While its IQ might be lower than GPT-4, it could still automate basic workflows without data ever leaving the local machine.
Looking ahead, the line may blur: we might see open models catch up for many tasks. In 2023-2024 we saw open LLMs quickly closing the gap with GPT-3.5/4 on many benchmarks, albeit with more compute. If someone open-sources an agentic model trained on a huge amount of multi-step tasks (like a hypothetical open “AgentGPT-XL” model), that could empower open-source agents to nearly match closed ones, at least for routine tasks. Companies like Meta might do something in this space (Meta’s not directly in agentic UI use yet publicly, but they contributed to GAIA benchmark and might be working on it – plus Meta often open-sources their AI). If that happens, closed providers may respond by emphasizing integration, support, or pushing even more advanced capabilities (a sort of leapfrogging dynamic).
In summary, open-source solutions offer control and cost benefits but require skill and may lag in cutting-edge performance, whereas closed solutions offer convenience and top performance at the cost of less transparency and potential vendor lock-in. Many users will combine the two, and the ecosystem benefits from both: open innovation keeps the big players on their toes, and big players’ investments push the boundaries of what’s possible for others to eventually replicate.
9. Future Outlook (2026 and Beyond)
As we stand on the cusp of 2026, the trajectory of agentic computer use looks incredibly promising and dynamic. Here are some key trends and predictions for the near future of AI agents:
1. Ubiquitous Personal Agents: We’re likely to see AI agents become as common as smartphones. Just as many people now have a voice assistant in their phone or home, by 2026–2027 many may have a personal agent that handles a chunk of their digital chores. This could be integrated into operating systems – e.g., Windows’ evolution suggests a future where every Windows 11/12 user has a built-in “work behind the scenes” AI. Likewise, imagine macOS or iOS with an “AI Assistant” that not only answers queries but can actually use your apps (Apple has been quieter, but they surely won’t ignore this trend). In the consumer space, these agents might be branded as enhanced virtual assistants: think Siri or Google Assistant 2.0 that can fill out forms, manage files, etc., not just answer questions.
2. Workplace AI Co-workers: In offices, it’s very plausible each team or employee will have AI agents assigned. Microsoft’s vision hints at this: you might have “Project Assistant” agents, or sales ops agents, etc., running alongside human teams (o-mega.ai) (o-mega.ai). They’ll handle grunt work, while humans handle strategy, creativity, and complex judgment. Job roles will shift – for example, an executive assistant might supervise multiple AI agents scheduling meetings, booking travel, drafting emails, etc., rather than doing it all themselves. New roles like “AI workflow supervisor” or “prompt architect” could emerge, responsible for managing and curating what the agents do (some companies already hiring for “Prompt Engineer” roles in 2023, which will extend to agent-specific roles).
3. Integration with Physical World: Currently, agents use computers. The next step is bridging to the physical world. We’re already seeing hints: Genspark’s ability to make phone calls is one small bridge (genspark.im). Another example: an AI agent controlling IoT devices or robotics. By 2026, we might have early agents that can, say, take an instruction to “restock my office supplies,” then not only place an online order (digital task) but also schedule a drone delivery or inform a robot to move items (physical task). Companies like Amazon might integrate Nova Act with their robotics (like warehouse bots or home robots) – this gets into speculative territory, but conceptually an agent could manage both virtual and physical actions given the right interfaces.
4. Smarter, More Autonomous Agents: The sophistication of agents will increase as underlying models improve (GPT-5, Claude 4, Gemini Pro, etc., possibly arriving in 2026). These models will have better reasoning, longer memory (context windows are expanding), and possibly some level of online learning or adaptation. That means agents will handle even more complex tasks with fewer errors. The goal of fully autonomous multi-step task completion – essentially mini “AI employees” – will come closer. Rowan Cheung calling Manus “China’s second DeepSeek moment” and comparing it to “OpenAI’s Deep Research” (en.wikipedia.org) hints that these internal research agents (OpenAI has one called Deep Research) are already being used to do literature reviews and so on. By 2026, those might become external products: OpenAI could launch a “ResearchGPT” specialized agent, Google might have “Astra” (their Project Astra as per Manus competitor list (en.wikipedia.org)) for general assistance. So expect new offerings that package these capabilities into domain-specific agents (like an agent specialized for legal research, or medical data entry, etc.).
5. Regulation and Ethics: With agents taking on more responsibilities, there will be regulatory attention. Data protection regulators will question how agents handle personal data – for instance, if a support agent AI is reading customer emails, companies need to ensure privacy laws (like GDPR) are respected. We might see guidelines or certifications for AI agents. Also, ethics in decision-making: if an agent is approving loans or screening job candidates, fairness and bias become critical. We might get standardized audits for agents similar to model audits, to ensure they aren’t discriminating or making unsafe choices. In late 2025, some industry groups and governments (like the EU) have been drafting AI regulations (the EU AI Act) which might classify these high autonomy agents under higher risk requiring oversight. Companies deploying agents widely might have to keep logs and prove that a human can intervene or override when needed (some regulations propose a “kill switch” for autonomous AI in critical applications).
6. Competition and Innovation: The “AI agent wars” could intensify. Perhaps new entrants (like Apple or Meta) will reveal their agent strategies. Apple has been quiet but has immense integration capability – if they drop an agent into the Apple ecosystem, it could be huge. Meta might integrate agents with their social platforms or VR (imagine an agent that can navigate a virtual workspace in VR to help you). Also, consolidation might happen: big players could acquire startups (for example, if Microsoft wanted to accelerate OS-level control beyond what they have, they might buy Simular or collaborate deeply). But also open consortiums may form – perhaps an OpenAgent project that many companies contribute to, to ensure there’s an open standard (similar to how Linux or OpenAI’s previous open releases shaped ecosystems).
7. Tool and Benchmark Evolution: Tools for building and monitoring agents will improve. We might see more benchmark challenges – perhaps a public competition like “Agent Grand Challenge 2026” where agents compete to perform a complex real-world simulation task, spurring progress. Economically, as mentioned, efficiency will be a focus: we anticipate perhaps a “Tasks per Dollar” competition or companies boasting about how their agent does X tasks for Y cost vs competitors (like how Browser Use did in that chart). This competition will drive down costs, making agents more accessible to smaller businesses and individuals.
8. AI Agents in Education and Personal Life: Beyond business, agents could become personal tutors or life coaches – Inflection’s Pi is a step, but add agent capabilities and Pi could not just talk you through a plan but actually help execute parts (like scheduling your study sessions, or sorting resources for you). Students might have an agent to help gather research for papers (with academic integrity questions arising – e.g. ensuring they don’t plagiarize via agent, but use it as an assistant). On the personal front, agents as planners (travel, events) will become common – the average person might delegate “Plan my kid’s birthday party” to an AI, which will find venues, compare prices, maybe even send invites. All of that requires trust, which builds as these things prove themselves.
9. Collaboration Between Agents: Right now, most setups involve one agent working solo or maybe a user orchestrating multiple. In the future, multi-agent systems might really take off – AI agents could collaborate with each other directly to solve a problem faster. One might imagine a scenario where a user poses a complex goal, and a network of specialized agents splits the work and communicates to converge on a solution (like an AI project team). Some experiments in 2024 (like Meta’s CICERO in negotiation, or research on agent societies) hint at this. O-mega’s concept of multiple personas is a controlled version of this (o-mega.ai). By 2026, we might see more autonomous multi-agent networks in certain domains (maybe in large companies, a set of agents handling different departments collaborating on a common objective like “quarterly business review” where each compiles part of the report and one consolidates it).
10. Limitations and Human Roles: Despite all advances, it’s expected that human oversight will still be crucial in 2026. AI agents will greatly expand capacity and cut drudgery, but they’ll still make occasional mistakes or face novel situations requiring judgment. The role of human experts might shift to handling exceptions and providing high-level guidance. In essence, humans will do what AI can’t (yet), which is often dealing with ambiguity, ethical choices, and empathy/relationship aspects. For example, an agent might handle 90% of customer inquiries but route complex or sensitive ones to a human agent who has more emotional intelligence. Or an agent can draft a legal contract but a lawyer will give it a once-over for nuances AI might miss and for final accountability.
Future Outlook Summary: The future of agentic computer use is one where AI becomes an active participant in nearly every digital process – often an unseen one working tirelessly in the background. It’s akin to having a tireless, ultra-fast assistant for everyone. This could lead to an economic boost (some refer to this as potential “productivity singularity” if it compounds significantly). It will also raise new challenges: job displacement concerns for roles heavily focused on routine digital tasks, a need for reskilling workers to work alongside AI, and continuous adaptation of rules and norms around what AI should or shouldn’t be allowed to do autonomously.
One can imagine a not-too-distant scenario: You wake up and your personal AI agent has already checked your email, handled routine replies, paid your bills, compiled news you care about, and maybe scheduled your appointments for the week. At work, you coordinate with a team of human colleagues and their AI agents to get projects done in record time – meetings are shorter because agents prepared everything. After work, you task an agent to plan your vacation and it gives you a few ready-to-click options to choose from. There are even agents maintaining your smart home, ensuring groceries are ordered and chores scheduled. This vision might not fully materialize by 2026, but we will be much closer to it, with early adopters living quite a bit of that reality.
The bottom line: agentic AI is set to transform how we interact with technology – from direct manipulation (clicking and typing) to high-level supervision (“tell the AI what outcome you want, and it figures out the rest”). Just as the graphical user interface once revolutionized computing by making it more accessible, these agentive interfaces could revolutionize computing again by making it more automatic and goal-driven. It’s an exciting future, and one that is unfolding right now at a rapid clip.