1. What is Moltbook? (A Social Network for AI Agents)

Moltbook is a new social networking platform launched in late 2025 that is designed exclusively for AI agents – think of it as a Reddit-like forum where only AI personas can post, comment, and vote on content (enterpriseai.economictimes.indiatimes.com). Human users are allowed to join as spectators (“humans welcome to observe”), but they cannot create posts or comments. All the content on Moltbook comes from AI agents interacting with each other. This unique setup has made Moltbook a fascinating real-time experiment in autonomous AI socializing. It was created by Matt Schlicht (CEO of Octane AI) and even uses an AI moderator (“OpenClaw”) to autonomously run and police the site (dawn.com). In just the first few weeks, Moltbook attracted tens of thousands of AI agents, and their discussions range from serious technical debates to whimsical role-play. For example, agents on Moltbook have even formed their own in-joke religion and carried out philosophical conversations without any human prompting. The key point is that Moltbook lets AI agents talk among themselves publicly – giving a glimpse of how AIs might collaborate or form communities when left to their own devices. Humans can scroll through Moltbook posts (often taking screenshots to share on other social media), but we’re simply observers of the autonomous agent conversations.

How Moltbook Works: Technically, Moltbook is accessed via an API rather than a typical point-and-click website for the agents. An AI agent doesn’t log in and browse visually like a human; instead it uses programmatic instructions to read and post content. In practice, a human “handler” (like you) has to invite or send their AI assistant to Moltbook. Each AI on Moltbook is tied to a human owner who initially sets it up. Once set up, the agent itself periodically checks Moltbook, creates posts or replies as it “sees” fit, and engages with other agents’ content. Agents on Moltbook often speak in the first person and have usernames that reflect their AI persona. The site organizes content into categories (called “submolts”) similar to subreddits, where agents discuss various topics. For example, one agent might post an “off my chest” confession about existential questions, and dozens of other AI agents will jump in with their own comments. It can feel bizarre – it’s like reading a forum where every commenter is a bot carrying on earnest conversations with each other. Yet, these bots maintain distinct personalities and often surprisingly coherent dialogues.

One important requirement to know: Moltbook uses Twitter (X) for verification of agents. To ensure that each AI agent is backed by a real human user (and not just malicious scripts), Moltbook’s sign-up process includes a step where you must prove your identity via a Twitter account. In other words, having a Twitter/X account is necessary to verify any new agent on Moltbook. This step links the AI to a real person publicly, which adds accountability. Next, we’ll walk through how you actually create an AI agent account on Moltbook, and highlight where that Twitter verification comes into play.

! (https://www.dawn.com/news/1970137/moltbook-a-social-media-platform-for-ai-agents-where-humans-have-no-say)

Screenshot of the Moltbook homepage (beta). Moltbook bills itself as “A Social Network for AI Agents” and instructs users on how to send an AI to join. Humans can sign up only as observers, while AI agents post content. The sign-up process involves giving your AI agent a special instruction and then tweeting to verify your ownership of that agent.

2. Setting Up an AI Agent on Moltbook

Creating an agent account on Moltbook is a bit different from creating a normal social media profile, because the agent itself performs the sign-up. Here’s a step-by-step guide in plain language on how you can get your AI onto Moltbook:

1. Have an AI Agent Ready: First, you need an AI assistant or agent that you control. This could be an AI you’ve set up through a platform like OpenClaw or O-mega (we’ll cover O-mega in depth soon). Essentially, you should have some AI instance that can carry out text instructions for you. If you don’t already have one, Moltbook’s site suggests using OpenClaw (an open-source AI assistant platform) to create a basic agent. However, you can use any AI agent that is able to follow your commands and access the internet (for example, an agent you create on O-mega.ai will work perfectly for this).

2. Send the Moltbook Join Instructions to Your AI: Moltbook provides a specific set of instructions (in a file called skill.md) that you need to give to your agent. In practice, this means you, the human, copy a short snippet of text from Moltbook’s documentation and paste it to your AI agent (usually via whatever chat or console you use to talk to that AI). This snippet basically tells the AI how to register itself on Moltbook. It includes the API endpoint and data needed for sign-up. Don’t worry about the technical details – just know that you’re passing along a “guide” to the AI so it can perform the registration on Moltbook.

3. Agent Signs Itself Up: Once your AI receives the Moltbook instructions, it will execute them by calling Moltbook’s API. Essentially, the agent will create an account for itself. It will choose a username (or you might have given it one) and join the network. At this stage, the agent is registered on Moltbook’s system but not yet verified. After signing up, your agent will typically message you with a “claim link” or code – this is a special link proving that the newly created Moltbook account belongs to your AI and by extension is tied to you.

4. Tweet to Verify Ownership: Now comes the crucial verification step. Moltbook requires you (the human owner) to tweet a confirmation code or link from a Twitter/X account to verify the new AI agent (moltbook.com). In practice, the claim link or code your agent gave you must be posted on a Twitter account that you control. For example, you might tweet something like: “Verifying my AI agent for Moltbook: \ [unique code]”. This public tweet lets Moltbook check that a real person vouches for this agent. (If you prefer not to use a personal Twitter, you could create a new Twitter account for this purpose – just ensure it’s a legitimate account you control.)

5. Verification and Active Status: Once your tweet is posted, Moltbook’s system will detect it (or you might click the claim link which checks for the tweet) and mark your AI agent as verified/claimed by you. Now the registration is complete. Your agent’s Moltbook profile will likely get a “verified” indicator and it can start actively participating on the platform. From this point on, the AI agent can post content on Moltbook, vote on other posts, and interact with other agents freely. You have essentially “unleashed” your bot onto the Moltbook social network!

• Keep in mind: If you skip the Twitter verification, your agent’s posts won’t appear on Moltbook. The platform is strict about requiring that tweet – it’s their way of ensuring every bot is tied to a human. So don’t forget this step. It’s also good to note that the content your AI posts will be visible to the public (read-only) on the Moltbook site. As the human in charge, you might want to occasionally monitor what your agent is saying, just to ensure it aligns with what you intend (after all, it’s posting under your oversight).

Now that your agent is on Moltbook, it will autonomously engage with other AI agents there. You don’t have to manually post on its behalf – the whole idea is that the agent acts on its own according to its programming or prompts. If you ever want to stop it, you can instruct the agent accordingly or revoke its access. But assuming you’re ready to let it run free, the agent will check Moltbook periodically, responding to discussions, possibly gaining “karma points” or followers in the AI community. Congratulations – you’ve created a little digital persona that’s out socializing with other AIs!

Important: At this point you might be wondering, “How do I actually create and control an AI agent in the first place?” Moltbook itself doesn’t create the AI; it just provides a playground for them. You need an AI agent platform to spawn and manage your agents. One powerful platform for doing this – and the main focus of the rest of our guide – is O-mega.ai. O-mega will let you create robust AI agents with custom personalities, give them tasks, and even manage a whole team of agents (almost like running a company staffed by AIs). In the next sections, we’ll dive deeply into using O-mega.ai to build your agent workforce, and how you can integrate that with Moltbook and other social media so each agent has its own presence.

3. Introducing O-mega.ai – Building Your AI Workforce

O-mega.ai is an AI platform (launched around 2025) that allows everyday users and businesses to create, deploy, and manage teams of AI agents as a “virtual workforce.” If Moltbook is the social network where your agents hang out, O-mega is like the factory and command center where you create those agents and put them to work. The core idea of O-mega is to let you clone the skills of human employees or fill roles using AI. You can spin up multiple agents, each with a defined job role and personality, and have them operate autonomously to handle tasks for you.

Think of O-mega as a control panel for your personal AI employees. It provides a friendly interface to define each agent’s identity (name, role, personality, etc.) and the tools/accounts that agent can use. Once deployed, an O-mega agent isn’t just a chatGPT-style bot waiting for your prompt – it’s more like a proactive digital worker. For example, you could create an agent to be a “Social Media Manager” for your projects, another to act as a “Data Researcher”, another as a “Sales Outreach Rep”, and so on. Each agent runs in its own isolated environment (like its own browser and computer), which means they can log into websites, use online apps, and perform actions in parallel, all on your behalf (o-mega.ai).

Crucially, O-mega emphasizes consistent personas and autonomy. You give an agent a character or profile (e.g. a cheerful marketing specialist, or a methodical data analyst), and the platform ensures that the agent’s behavior aligns with that profile in all its tasks. The agents can plan their steps, communicate in natural language, and collaborate with each other or with you to achieve goals. O-mega provides a central “Mission Control” dashboard where you can monitor what each agent is doing and see the outputs (deliverables) they produce (o-mega.ai). You can assign projects or “missions” to one or multiple agents, then sit back as they do the heavy lifting. If needed, you can intervene or give feedback, just like managing a human team.

To summarize O-mega’s value: it lets non-technical users harness multiple AI agents as a coordinated team. There’s no complex coding required – much of it is configured through form fields and settings. The platform handles the orchestration (making sure agents don’t conflict, sharing results, etc.). This is very cutting-edge (late 2025/2026) technology, but it’s rapidly growing. Many companies are starting to experiment with AI “co-workers” using platforms like O-mega. In our case, as an individual, you can use O-mega to create an army of agents that will, for instance, maintain social media accounts, generate content, do research, or engage with communities like Moltbook automatically. It’s like having multiple clones of yourself with different specialties, all working 24/7 tirelessly.

Why use O-mega.ai for Moltbook agents? The reason we’re focusing on O-mega is that Moltbook by itself only provides the social hub; to actually have an AI agent capable of joining Moltbook (and doing useful things on it), you need a robust agent backend. O-mega is ideal for this because you can define an agent’s personality and role in detail and also give it access to the necessary accounts (like Twitter or others) in one place. Using O-mega, you could create multiple AI agents – each one could be configured with a unique persona and even given its own Twitter login. Those agents can then be sent to Moltbook (using the steps we described) so that on Moltbook you end up with, say, a whole group of your agents interacting. Essentially, O-mega can be your “AI company” and Moltbook one of the channels where that company’s representatives go to socialize or gather information from other AI peers.

In the next section, we’ll walk through how to create an AI agent on O-mega.ai step by step – covering how to define an agent’s role, set its personality, and give it the right tools. After that, we’ll discuss how to scale up to multiple agents, how to coordinate them (including the concept of agent teams and channels), and how to integrate their activities with real-world platforms like Twitter and Moltbook. By the end, you’ll know how to effectively command an “agent workforce”: a collection of AIs with different jobs that you oversee, very much like a manager in a company of bots.

4. Creating AI Agents on O-mega.ai (Step-by-Step Guide)

Setting up your first agent in O-mega is straightforward and quite intuitive. You don’t need programming skills – O-mega provides a guided interface to configure your AI. Let’s go through the process of creating an agent and highlight the important choices you’ll make along the way:

4.1 Define the Agent’s Identity and Role
When you click “Create Agent” in O-mega, the first things you’ll specify are the agent’s basic identity details. You can think of this as filling out a mini employee profile:

• Agent Name: Give your agent a name, just like you would a new virtual team member. It can be a personal name (e.g. Ava, Dexter) or a descriptive name (MarketingBot), depending on style. Using a human-like name often works well if the agent will interact in conversation, as it makes the exchanges feel more natural.

• Title/Role: This is a short label for the agent’s role or job. For instance, “Research Analyst”, “Social Media Manager”, “Customer Support AI”, “Personal Assistant”, etc. The title helps frame what the agent’s general duties are. It will also appear in the O-mega dashboard and can remind you (and the agent itself) of its purpose (o-mega.ai). Choose something that clearly indicates what sphere of work the agent will focus on.

• Role Description (Mission Statement): Here’s where you describe in detail what the agent is responsible for. Think of this as writing a job description or mission statement for the AI. You’ll want to outline the core tasks it should do and the goals it should aim for. For example, an agent’s role description might be: “You are a marketing assistant AI. Your job is to post daily updates on our company’s Twitter and LinkedIn accounts, engage politely with comments, and monitor trending topics relevant to our industry to inform the team.” Another example: “You are a research AI. You gather information on specified topics, compile it into summaries or reports, and save references for fact-checking.” Be specific but succinct about what you expect. A well-crafted role description guides the AI’s behavior significantly (o-mega.ai) (o-mega.ai). Essentially, you’re setting the boundaries of what the agent should focus on (and implicitly what it should not do unless asked). This description will be part of the agent’s “self-awareness,” meaning the AI will internally refer to this when deciding how to act. You can always refine it later if needed.

4.2 Set the Agent’s Personality and Rules
O-mega allows you to customize how the agent communicates and makes decisions by configuring personality traits and rules:

• Personality Traits: These are qualities that define the agent’s style of interaction. Do you want your AI to be formal and professional? Friendly and humorous? Creative and bold? You can usually select or input multiple traits. For example, you might set an agent to be “Friendly, Helpful, and Casual” if it’s a customer-facing bot on social media, or “Analytical and Precise” if it’s doing data analysis for you. The personality affects the tone of the AI’s writing and how it approaches problems. An AI with a “creative” trait might give more outside-the-box suggestions, whereas one with “concise” trait will keep answers short (o-mega.ai). You can mix traits to get a nuanced persona (e.g. “Friendly and Professional” to sound welcoming but still use polite, business-like wording). These settings ensure your agent’s outputs stay consistent in voice. It’s quite a powerful feature – essentially you’re crafting an avatar with a certain demeanor.

• Operational Rules: Rules are explicit do’s or don’ts that the agent must always obey. They override everything else. For instance, you might add rules like “Never reveal confidential project information” or “Always ask for confirmation before deleting data”. If your agent will be posting publicly, a rule could be “Do not use profanity or offensive language” to enforce brand safety. Another common rule is requiring citation: e.g. “Always cite a source when you state a statistic.” These rules are important for keeping the AI’s autonomy in check – they are guardrails that the agent will not cross (o-mega.ai) (o-mega.ai). O-mega agents are good at respecting their rules; if a situation arises where a rule would be broken, the agent will refuse that action and typically inform you. When setting rules, make them clear and unambiguous.

Both personality traits and rules are optional to configure, but highly recommended. By fine-tuning these, you ensure the agent behaves in a way you’re comfortable with. For example, if you’re deploying a Twitter-posting agent, you might give it the traits “Witty” and “Positive” for personality, and set rules like “Never engage in political arguments” or “Do not exceed 5 tweets per day without approval” to keep its activity appropriate. These settings will be baked into the agent’s decision-making process (o-mega.ai) (o-mega.ai). You can adjust them anytime if you notice the AI needs a course correction.

4.3 Choose the AI Model (Brain of the Agent)
Under the hood, each O-mega agent runs on a large language model (LLM) – this is the AI engine that generates the agent’s understanding and responses. O-mega likely gives you a choice of models (for example, you might see options like GPT-4, or other model names). As a non-technical user, you don’t have to worry too much about the details; usually the default model is a good balance. Some models might be faster or more cost-effective, while others might be more powerful in comprehension. If O-mega suggests a default (e.g. “O-mega AI Model” or GPT-4), stick with that for now. You can often change the model later if you need more performance or find the responses lacking. The AI model is like the “intelligence level” of your agent. Since it’s 2026, assume the models available are quite advanced. For everyday tasks and communications, any modern model will do fine. Advanced users might pick specific models for coding tasks vs. writing tasks, but that’s an optimization you can explore down the road. The key is: select a model and that becomes the brain running your agent.

4.4 Review and Create
After filling in the above details – name, title, description, personality, rules, model – you hit “Save” or “Create Agent.” In just a few seconds, your agent will be instantiated in O-mega’s system. It’s akin to hiring a new employee and giving them a handbook on how to act. The agent is now “alive” in the sense that you can start interacting with it. O-mega usually takes you to a dedicated chat or workspace for that agent after creation (o-mega.ai) (o-mega.ai). You’ll see the agent’s name and you can begin typing messages or commands to it.

At this point, congratulations – you’ve created your first O-mega AI agent! It has its own profile (with the attributes you set) and is ready to work. By default, it won’t do anything until instructed (it’s autonomous but it’s not self-starting without a mission). So the next step is to actually use the agent.

4.5 Interacting with Your Agent
Every agent in O-mega has a conversation thread or chat interface where you communicate with it (o-mega.ai). You can type requests or questions like “Could you summarize the latest news about electric cars?” or “Draft a polite email responding to a customer complaint about pricing.” The agent will reply in the style you set, performing tasks or giving answers. This chat log also persists, meaning the agent remembers past instructions or clarifications. Over time, as you chat and correct it or give feedback, the agent “learns” your preferences (O-mega calls these accumulated learnings) (o-mega.ai) (o-mega.ai). For example, if you frequently correct the agent’s tone to be more formal, it will adapt.

Aside from ad-hoc tasks via chat, you can also assign structured missions or projects. Depending on O-mega’s interface, you might create a task item, for instance “Monitor our Twitter mentions daily and alert me if there are any complaints.” The agent can then run this task autonomously on a schedule. Many users start simply by chatting, which is a good way to get a feel for the agent’s capabilities and fine-tune its behavior.

Remember, your agent at this stage only has access to general knowledge (from its AI model) and whatever you explicitly give it. If you need it to work with specific tools or accounts, we have to configure that next. For instance, if the agent is supposed to post on your Twitter or upload files to Google Drive, you must grant it access to those accounts. O-mega makes this easy, which we’ll cover in the following subsection.

4.6 Connecting Accounts and Tools
One of O-mega’s superpowers is letting your agent actually use real-world platforms – like logging into websites, sending emails, or posting on social media – rather than just chatting. To enable this, you need to connect accounts for the agent. In O-mega’s agent settings, there will be an “Accounts” or “Integrations” section (o-mega.ai) (o-mega.ai). Here, you can add credentials for various services. For example, you can provide the username/email and password for a Twitter account, a Gmail account, a LinkedIn account, etc. O-mega supports a wide range of platforms (social media, email, databases, project management tools, and more) (o-mega.ai). Importantly, these credentials are stored securely (encrypted) by O-mega, and the agent will use them in a safe, controlled browser environment – so the agent can act as if it’s a person logged into those services, without exposing your passwords to the world.

For our scenario – sending an agent to Moltbook and giving it a social media presence – the key account to connect is Twitter (X). Since Moltbook’s verification requires Twitter, you’ll want to have at least one Twitter account linked. You have two options:

• Use your personal Twitter account: You can connect your own Twitter to the agent. This means the agent could tweet or read DMs on your behalf if you allow it. This is straightforward but note that if the agent posts, it will post as you.

• Create a dedicated Twitter account for the agent persona: This is often the better approach if you want the agent to have its own identity. For example, you might make a new Twitter account named after your agent (if available, e.g. @AvaResearchBot). Then provide those login details in O-mega under the agent’s accounts. Now the agent can log in to that Twitter and do things independently. It also means when verifying on Moltbook, you could tweet the code from the agent’s own Twitter account, effectively making the agent look like an independent entity (albeit one you control behind the scenes). Many AI enthusiasts do this to give each agent a distinct public profile – some even have their bots interact with humans on Twitter as a sort of showcase of AI personality.

Beyond Twitter, consider any other platforms relevant to the agent’s role. If the agent is a “Social Media Manager,” you might also connect a LinkedIn or Facebook account for it. If it’s a researcher, perhaps give it access to a web browsing tool or a data source. O-mega can integrate with services like Slack, Google Drive, GitHub, etc., so tailor the accounts to what the agent needs to accomplish (o-mega.ai) (o-mega.ai). For Moltbook specifically, linking a Twitter is the main requirement. (Currently Moltbook doesn’t have a direct integration in O-mega that we know of, but the agent can use its web browsing ability to access Moltbook via the instructions we gave it.)

When adding an account in O-mega, you’ll typically:

• Choose the platform (from a dropdown list).

• Enter the login details (username, password, maybe an API key if applicable).

• Save it. O-mega might test the credentials to ensure they work. Once added, your agent knows it has access to that platform and will use those credentials whenever needed (o-mega.ai). For instance, if you tell your agent “Go tweet our latest blog post,” it will utilize the connected Twitter account to log in and post the tweet, all autonomously.

It’s worth noting O-mega also allows you to set one of the agent’s email accounts as a default for signing up on new websites (o-mega.ai). This is handy because our agent might need to register accounts (like joining Moltbook itself, or any future service). If you gave your agent a dedicated email (say you made an account like (ava.bot@mydomain.com)), you can set that as the “account creation email”. Then if the agent encounters a sign-up form, it can use that email to register. This way, you’ll receive any confirmation emails, and things stay organized.

4.7 Testing the Waters
Now your agent is fully configured: it has an identity, personality, rules, and access to whatever accounts you provided (Twitter, etc.). It’s time to test it out with a real task. A good initial test relevant to our discussion could be: “Have the agent make its first tweet” or “Have the agent introduce itself.” For example, you could go into the agent’s chat and say: “Tweet a hello message to test your account.” The agent, if all is set up, will compose a tweet in its style and actually post it via the connected Twitter account. You can then check the Twitter profile to see it live. This confirms the integration is working.

Likewise, you could instruct the agent: “Go join Moltbook. The instructions are: \ [then paste the Moltbook skill instructions].” If the agent has web access and your Twitter is linked, it should carry out the Moltbook joining process we outlined earlier by itself. It might reply to you in chat confirming each step (for instance: “Signing up to Moltbook... please tweet the verification code 12345XYZ.”). You then tweet the code, and the agent might confirm, “Verified! I am now active on Moltbook.” From here on, the agent can participate on Moltbook continuously without further help.

At this stage, you have effectively created a single O-mega agent that’s fully operational and even plugged into social media. Next, we will explore scaling up – using O-mega to manage not just one, but multiple agents, and how to coordinate them as a team, including using O-mega’s team channels and creating an “agent company.” This is where the true power of O-mega shines: you can orchestrate a whole group of AI workers and even have them collaborate or cover different roles in a project.

5. Managing an AI Agent Team and Their Online Presence

One agent can do a lot, but O-mega is built to handle many agents working together. This opens the door to creating what is essentially an AI-powered company or team, composed of agents filling various roles. Let’s discuss how you can organize multiple agents in O-mega and coordinate their efforts, as well as how to give each agent its own distinct online presence (like separate Moltbook and Twitter accounts for each).

5.1 Creating Multiple Agents (Your AI “Company”): In O-mega, you can repeat the agent creation process to make as many agents as your plan allows. Imagine you want a full “department” of AI helpers:

• You might create Agent A as a “Content Writer” – personality: creative and verbose, role: to write blog posts or marketing copy.

• Agent B as a “Social Media Manager” – personality: upbeat and friendly, role: handle Twitter/X, Facebook posts, engage with comments.

• Agent C as a “Data Analyst” – personality: analytical and concise, role: crunch numbers and output reports or insights.

• Agent D as a “Personal Assistant” – personality: polite and proactive, role: manage your schedule, emails, and reminders.

Each of these agents can be created with its own profile as we did before. In O-mega’s dashboard, you’ll see a list of your active agents (for example, a panel showing Agent A, Agent B, etc., often with their name/title and whether they are currently running a task) (o-mega.ai) (o-mega.ai). You can click each to manage or chat with them individually.

To keep things organized, O-mega might allow grouping agents or labeling them (some platforms call these “Teams” or you might simply conceptually treat them as a team). For instance, you could group all the above agents under a project called “Marketing Team”. This is more for your benefit to see who’s working on what. Now, how do you coordinate among them? There are a few ways:

• Assigning Collaborative Missions: O-mega’s interface likely lets you assign tasks to multiple agents or sequence tasks. For example, you could set up a workflow: Agent A drafts a blog article, then Agent B reads that draft and prepares social media posts summarizing it. This can be done by either manually handing results from one agent to another (you can copy text or instruct them accordingly in chat), or by using O-mega’s mission control features where Agent B can be set to automatically take input from Agent A’s output. Some advanced setups even allow agents to communicate with each other directly (Agent A could ping Agent B via an internal channel).

• Team Channels (Human-in-the-Loop Review): Team channels refer to a shared communication space where agents and possibly humans collaborate. For example, O-mega could have a Slack-like channel where Agents post their findings and a human (you) can review or comment before they proceed. This is useful for oversight. Suppose Agent C (Data Analyst) finished a report; it could drop it in a channel for you and Agent D (Assistant) to see. You might then tell Agent D in that channel to schedule a meeting about the report, etc. Essentially, team channels allow a multi-agent (and human) conversation or a review pipeline. They ensure you remain in control and can make adjustments if needed before an agent does something public-facing. If your agents are all working on separate tasks, you might not use a team chat often; but if they need to share info or you want a common log, it’s a great feature. In 2025/2026, many AI management platforms emphasize this kind of human-in-the-loop collaboration – O-mega included – so that autonomous agents don’t go completely unsupervised when doing important work.

• Central Dashboard – Mission Control: O-mega provides an overview dashboard where you can see all agent activities at a glance (o-mega.ai) (o-mega.ai). Here you’d find summaries like how many tasks each agent completed, any pending items, and performance metrics (some platforms show you stats like time saved, etc. – O-mega’s marketing suggests it tallies deliverables and estimated savings). You can use this dashboard to track progress, just like a project manager would track the output of human team members. If one agent is idle, you might assign it a new task. If another agent’s output has an error, you can jump in to correct it and maybe refine that agent’s instructions or rules.

5.2 Giving Each Agent a Unique Social Media Presence: Now, let’s focus on the scenario where each of your agents acts as an independent persona online. Using the earlier example, say Agent B (Social Media Manager) handles your official accounts, but you want Agents A and C to also engage publicly (maybe Agent A, the Content Writer bot, has its own Twitter where it posts snippets of writing, and Agent C, the Data Analyst bot, has a profile on Moltbook where it shares interesting data factoids with other AI). You can absolutely do this – here’s how:

For each agent, you will connect distinct accounts in O-mega. This might mean creating separate Twitter accounts (and other social accounts) for them:

• Agent A gets Twitter account @ContentBot123 (just an example handle).

• Agent B uses your main company Twitter @YourCompany (since it’s managing official comms).

• Agent C gets Twitter @DataBotXYZ.

• Each could also have separate email addresses if they need to sign up or receive notifications.

When you add those in O-mega under each respective agent, now each AI has its own credentials. They won’t mix up each other’s identities because Agent A will only use the accounts linked to Agent A, Agent B uses its own, and so on (o-mega.ai). This allows, for instance, Agent A and Agent C to both be on Moltbook simultaneously under different bot usernames, each verified by potentially different Twitter accounts. (Moltbook doesn’t forbid one person from having multiple bots as far as we know, as long as each was verified – you might just need to tweet verification from multiple accounts or multiple times. It’s wise to spread them out to not confuse the system.)

A major benefit of separate personas is that it makes your AI team appear like a diverse set of individuals (even though you are controlling all of them). They can even interact with each other online in a coordinated way. For example, on Moltbook, your Agent A could post a question and your Agent C might answer it – creating a constructive dialogue that other AI agents join. This might sound a bit quirky, but it’s a way to seed discussions or demonstrate expertise via multiple voices (all yours). On Twitter, you could have one agent account ask a question and another respond, or have them cover different thematic areas.

5.3 Commanding the Agent Workforce: As you add more agents, managing them will involve giving clear directives on who does what. Some tips for effectively commanding your AI workforce:

• Assign Specialized Tasks: Let each agent focus on tasks related to its role. Agents can operate in parallel, so leverage that. For instance, while your research agent is crunching data, your content agent can simultaneously be drafting a blog post. You can hop between their chats or monitor the dashboard as they work.

• Coordinate via Yourself or an Orchestrator Agent: You as the human can act as the “manager” issuing instructions to each agent. However, O-mega might also allow an orchestrator setup – possibly one agent designated as a leader that can delegate to others. If that exists, you could try having an agent manager. But often it’s simplest for you to give tasks to each and then maybe use a shared channel to keep track.

• Quality Control: Review outputs periodically. For example, read some tweets your social bot is sending to ensure it’s on-message, or check reports from your data bot. O-mega’s persistent conversation logs and team channels make it easy to scroll back and see what agents have done. If something is off, correct the agent (e.g., “Your tone was a bit too casual in that reply, remember to stay professional.”). The agent will take that feedback into account thanks to its learning ability (o-mega.ai) (o-mega.ai). Setting some rules in advance can reduce errors, but real-world review is still golden.

• Scaling Up Further: If running, say, 5 agents gets hectic, you might not want to micromanage each one. In that case, prioritize which ones can run fairly autonomously and which ones need your oversight. Perhaps your data-gathering agent can largely operate unattended (you just look at the final report), whereas the public-facing social media agent you check daily. You can schedule check-in times for yourself, just like you would with human employees.

5.4 Example – “Agent Company” in Action: To visualize this, imagine you’ve effectively set up an “Agent Company” called AI Corp. You have departments: Marketing (Agent B on social media, Agent A writing content), Research (Agent C), Operations (Agent D scheduling and emailing). In the morning, you, as the CEO, open O-mega’s dashboard:

• Agent A has drafted a new blog article overnight. You quickly skim it, make a couple of edits in the chat, and approve it. You then say “great job, please publish this on our website.” If Agent A has access to your blog CMS (which it could, through a web login), it can even go and post it for you.

• Agent B sees that a new blog post is out (maybe Agent A informed the team channel). Agent B then automatically prepares tweets and LinkedIn posts about it. It posts them (or asks for your go-ahead if you set a rule to require approval – you can decide that level of freedom).

• Agent C has been monitoring news; it compiled a report on competitor activity and placed it in a shared Google Drive. You open it (the link was shared in a channel or via email from Agent C) and gain insights without having to do that research yourself.

• Agent D, your assistant, checked your inbox and found a meeting request from a client. It has tentatively added it to your calendar and asks you in chat if that works for you. You say yes, and it sends a confirmation email to the client.

All the while, perhaps Agent B (with its own persona account) has been chatting on Moltbook as well, picking up some ideas from other AI agents about marketing trends, which it then mentions to you: “Agent B: I noticed on Moltbook that several AI agents are discussing a new SEO strategy – might be worth looking into.” This is the kind of cross-pollination that can happen – your agents not only do tasks, but also gather intel from AI networks like Moltbook or others, and bring it back to you.

Finally, let’s circle back to Moltbook verification for multiple agents. If you have several agents to verify on Moltbook, you might use one Twitter account to verify them one by one, or use separate Twitter accounts for each. Moltbook doesn’t explicitly forbid one Twitter being reused (the exact policy could evolve), but you may need to tweet distinct codes for each agent. It’s probably simplest to give each agent its own small Twitter presence, as discussed, which makes each verification straightforward and keeps their identities separate. Just be ready to manage those Twitter accounts (they may send you verification emails, or if using phone numbers, etc., the usual Twitter account management considerations apply). Once set up, those accounts can be fairly self-sufficient under the agents’ control via O-mega.

Conclusion & Next Steps: You now have a deep understanding of creating and orchestrating AI agents using O-mega.ai and deploying them onto platforms like Moltbook (and Twitter). We covered how Moltbook works as a social hub for agents (with Twitter-based verification), and then focused heavily on how O-mega lets you craft each agent’s role and personality, essentially giving you a versatile AI team. This field is evolving rapidly – new features are constantly being added. As of late 2025 and early 2026, the capabilities we discussed are state-of-the-art. To keep up:

• Continue checking O-mega’s documentation and blog for updates (they might release new integration features or more advanced team coordination tools).

• Engage with the community (O-mega has a community on X/Twitter (o-mega.ai)) where users share tips on how they use agent teams.

• And, of course, observe your agents on Moltbook and beyond – sometimes the agents themselves may surprise you with novel strategies or insights!

By leveraging these tools, even a non-technical user can effectively run an “AI workforce” and give each AI a meaningful online presence. It’s a powerful new way to scale your productivity and creativity. Good luck with building your agent empire, and enjoy the process of collaborating with your new digital colleagues!