Claude Managed Agents: The 2026 Guide

The practical guide to Anthropic's new cloud-hosted agent infrastructure: what it does, how it works, pricing, how it fits into the Claude ecosystem, and how it compares to agent platforms like O-mega and Lindy.

Anthropic just launched Claude Managed Agents in public beta, and it changes how developers build AI agents. Instead of spending months building your own agent loop, sandboxing, tool execution, and state management, you define an agent in YAML or natural language, point it at Anthropic's infrastructure, and it runs autonomously in a cloud container with built-in security, credential management, and session persistence. The pricing: standard Claude API token rates plus $0.08 per session-hour - Anthropic Pricing.

Early adopters include Notion, Rakuten, and Asana. Rakuten deployed enterprise agents across product, sales, marketing, finance, and HR within one week per deployment. Asana built what they call AI Teammates that pick up project management tasks and draft deliverables, reporting that advanced features shipped "dramatically faster" than previous approaches allowed - SiliconANGLE.

This guide breaks down exactly what Managed Agents is (and what it is not), how it fits into Anthropic's expanding product ecosystem, real pricing calculations, and how it compares to full agent platforms like o-mega.ai and Lindy.

Contents

1. What Claude Managed Agents Actually Is
2. How It Works: The Four Core Concepts
3. Where It Fits in the Claude Ecosystem
4. Tools, MCP, and What Agents Can Do
5. Research Preview Features: Outcomes, Multiagent, Memory
6. Real-World Use Cases and Early Adopter Examples
7. Pricing: What It Actually Costs
8. What Managed Agents Is NOT
9. Comparison: Managed Agents vs O-mega vs Lindy vs Agent SDK
10. Getting Started

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1. What Claude Managed Agents Actually Is

Claude Managed Agents is a suite of composable APIs that lets you deploy AI agents on Anthropic's cloud infrastructure without building the operational scaffolding yourself. It is not a chatbot. It is not another way to call the Messages API. It is a managed runtime where Claude operates as an autonomous agent: reading files, running shell commands, browsing the web, executing code, and persisting state across long-running sessions.

The core problem it solves is infrastructure overhead. Building a production-grade AI agent requires significantly more than just calling a language model. You need containerization for secure code execution, state management to track what the agent has done, tool orchestration to route between different capabilities, credential management so the agent can access external services without exposing secrets, error recovery so the agent can resume after failures, and observability so you can see what the agent is doing and why. Most development teams spend months building this scaffolding before writing any user-facing agent logic.

Managed Agents abstracts all of this away. You define what your agent should do, what tools it has access to, and what guardrails to apply. Anthropic's infrastructure handles the rest: spinning up isolated containers, managing sessions, routing tool calls, recovering from errors, and persisting state through disconnections.

The engineering architecture behind this is described in Anthropic's technical blog post " Scaling Managed Agents: Decoupling the brain from the hands," which borrows a concept from operating systems design. The system virtualizes three components into stable abstractions: the session (an append-only event log), the harness (the orchestration loop calling Claude), and the sandbox (execution environments where Claude runs code). By decoupling these three layers, each can scale, fail, and recover independently.

The practical result: time-to-first-token dropped roughly 60% at p50 and over 90% at p95 compared to earlier coupled architectures. Inference can begin immediately upon fetching pending events from the session log, without waiting for container initialization.

For a deeper look at how Anthropic has evolved its infrastructure approach since the Agent SDK launch, our guide to the Anthropic ecosystem covers the full platform strategy.

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2. How It Works: The Four Core Concepts

Managed Agents is built around four concepts that map directly to how you configure and run agents - API Docs.

Agent

The agent definition includes the model (Opus 4.6, Sonnet 4.6, Haiku 4.5, etc.), system prompt, tools, MCP servers, and skills. You create an agent once and reference it by ID across multiple sessions. Think of the agent as the template: it defines what the agent can do and how it should behave.

Environment

An environment is a configured container template. It specifies which packages are pre-installed (Python, Node.js, Go, etc.), what network access rules apply, and what files are mounted. Environments are reusable across sessions, so you can define a "data analysis" environment with pandas and matplotlib pre-installed, or a "web development" environment with Node.js and npm packages ready to go.

Session

A session is a running agent instance within an environment. It is the actual execution: the agent receives a task, starts working, and generates outputs. Sessions persist their file systems and conversation history across multiple interactions. If a session disconnects or the harness fails, a new harness can resume from the last recorded event via wake(sessionId).

This statefulness is one of the key differentiators from the standard Messages API. A session can run for hours, making dozens of tool calls, generating and modifying files, and streaming results back to your application, all without you managing any of the infrastructure.

Events

Events are messages exchanged between your application and the agent. You send user turns (task descriptions, follow-up instructions) and receive tool results, status updates, and agent responses via server-sent events (SSE). Event history is persisted server-side and can be fetched in full, which means you always have a complete audit trail of what the agent did and why.

The workflow follows five steps: create an agent, create an environment, start a session, send events and stream responses, and optionally steer or interrupt the agent mid-execution. The ability to interrupt and redirect is important for production deployments where user requirements change during long-running tasks.

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3. Where It Fits in the Claude Ecosystem

Anthropic now has a surprisingly extensive product lineup, and understanding where Managed Agents fits requires mapping the full ecosystem. Each product serves a different user and use case, though they share underlying technology.

The Full Claude Product Map

The relationships between these products follow a clear hierarchy:

Messages API is the foundation. Everything else is built on top of it. If you want maximum control over every prompt, every tool call, and every response, this is what you use. You build the entire agent loop yourself.

Agent SDK sits one level above the Messages API. It provides the agent loop, tool management, and context handling that Claude Code uses internally. The SDK was originally called the "Claude Code SDK" before being renamed to reflect its broader purpose. You get the same tools and orchestration that power Claude Code, programmable in Python and TypeScript, but you run everything on your own infrastructure.

Managed Agents sits one level above the Agent SDK. It takes the agent loop and adds managed infrastructure: cloud containers, session persistence, credential management, and error recovery. You define the agent; Anthropic runs it. The key tradeoff is control versus convenience. With the Agent SDK, you own the infrastructure and can customize anything. With Managed Agents, you trade some customization for dramatically faster deployment.

Claude Code is a ready-to-use product built on the Agent SDK, optimized specifically for software development. It is not a general-purpose agent platform. It is a coding agent.

Claude Cowork brings Claude Code's agentic capabilities to non-developers. It runs on Claude Desktop and operates on local files: organizing documents, drafting reports, analyzing data. Think of it as Claude Code for knowledge work instead of coding - VentureBeat.

For a detailed guide to how Claude Desktop, Cowork, and Code fit together in practice, our complete guide to the Claude desktop ecosystem breaks down the workflows.

The Decision Tree

Choosing between these products comes down to three questions:

1. Are you a developer? If no, use Claude Desktop/Cowork for local work, or Claude.ai for chat.
2. Are you building a product with agents? If yes, choose between Agent SDK (you manage infrastructure) and Managed Agents (Anthropic manages infrastructure).
3. Do you need custom infrastructure, VPC integration, or unique execution environments? If yes, use the Agent SDK. If you want speed and simplicity, use Managed Agents.

For most teams building their first agent-powered product, Managed Agents is the right starting point. You can always migrate to the Agent SDK later if you need more control.

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4. Tools, MCP, and What Agents Can Do

A Managed Agent without tools is just a chatbot. The tools define what the agent can actually do in the world. Managed Agents comes with a set of built-in tools and supports external tools via MCP (Model Context Protocol) - Tools Docs.

Built-In Tools

For most agents, the simplest configuration is the agent_toolset_20260401 bundled tool type, which enables all built-in tools with a single line: {"type": "agent_toolset_20260401"}. This is what Anthropic recommends in their quickstart guide. For production deployments where you want to restrict agent capabilities, specify individual tools instead.

An important pricing detail: code execution inside Managed Agents sessions is billed as part of the $0.08/session-hour runtime fee, not separately. This replaces the standalone code execution container-hour billing model used with the Messages API (which charges $0.05/hour per container after 1,550 free hours per month). For agents that execute code frequently, Managed Agents can be more cost-effective.

The built-in tools cover most common agent tasks: writing and executing code, reading and writing files, searching the web for information, and fetching content from URLs. The computer use tool enables GUI interaction via screenshots, which allows agents to interact with web applications and desktop software that do not have APIs.

MCP Integration

For capabilities beyond the built-in tools, Managed Agents supports Model Context Protocol (MCP) servers. MCP is Anthropic's open standard for connecting AI models to external tools and data sources. You can connect your agent to any MCP server, which means access to databases, CRMs, project management tools, communication platforms, and custom internal services.

The MCP ecosystem has grown substantially since its launch. Thousands of MCP servers are available for common business tools: Slack, GitHub, Salesforce, Google Workspace, Notion, Jira, and many more. For custom integrations, you can build your own MCP server in any language that supports JSON-RPC.

The security model is important here. Credentials never reach code execution sandboxes. OAuth tokens for MCP tools live in secure vaults, and Claude calls tools through a dedicated proxy that fetches credentials without exposing them to the harness or sandbox. For Git repositories, access tokens are used during initialization and wired into local remotes, so push and pull work without the agent handling tokens directly.

For teams already using MCP with Claude Code or Claude Desktop, the same MCP servers work with Managed Agents. The protocol is the same; only the execution environment changes.

Our guide on Anthropic's MCP launch covers the protocol design and its role in the broader AI tool ecosystem.

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5. Research Preview Features: Outcomes, Multiagent, Memory

Three features are available in research preview (requiring separate access approval): outcomes, multiagent coordination, and memory. These are not yet in the public beta but represent where Managed Agents is heading.

Outcomes

The outcomes feature lets you define success criteria for agent tasks, and the agent self-evaluates and iterates until it meets those criteria. In internal testing around structured file generation, this approach improved task success by up to 10 points over a standard prompting loop, with the largest gains on the hardest problems.

This is significant because it shifts the agent from "do what I say" to "achieve what I want." You define the outcome, not the steps. The agent figures out the path, evaluates its own work, and iterates until the result meets your criteria.

Multiagent Coordination

Multiagent lets agents spawn child agents for complex tasks. A parent agent can delegate subtasks to specialized child agents that run in parallel, then aggregate results. This enables workflows where one agent handles research, another handles data processing, and a third handles report generation, all coordinated automatically.

The tradeoff is cost: multi-agent workflows use roughly 4-7x more tokens than single-agent sessions. The additional token consumption comes from inter-agent communication, context sharing, and the overhead of each agent maintaining its own conversation state.

Memory

Agent memory provides a persistent knowledge layer across sessions. Agents can store and retrieve architectural decisions, implementation patterns, project-specific knowledge, and learned preferences. This enables continuity across coding sessions: the agent remembers what it did yesterday and can build on it today.

Memory is implemented through the MCP protocol, which means it integrates with the same server infrastructure used by Claude Code and Claude Desktop.

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6. Real-World Use Cases and Early Adopter Examples

Understanding what Managed Agents is best at requires looking at what early adopters are actually building with it. The pattern that emerges is clear: Managed Agents excels at tasks that are too complex for simple API calls but too standardized to justify building custom infrastructure.

Notion: AI Agents Inside the Workspace

Notion built Custom Agents (currently in private alpha) using Managed Agents infrastructure. Users can delegate complex tasks, including coding, generating presentations, and building spreadsheets, directly from shared Notion task boards. Claude picks up the full context of each task (docs, decisions, meeting notes, connected data) and reports back as it works.

The key insight from Notion's implementation: agents work best when they have access to the full context of a task, not just the task description. By connecting Managed Agents to Notion's workspace via MCP, the agent can read relevant documents, understand project context, and produce deliverables that actually fit the team's needs.

Rakuten: Enterprise Deployment at Scale

Rakuten deployed enterprise agents across five departments (product, sales, marketing, finance, HR) within one week per deployment. The agents plug into Slack and Teams, accept task assignments from team members, and return deliverables like spreadsheets, slide decks, and applications.

The speed of deployment is the notable data point here. Traditional enterprise AI projects take months. Rakuten went from zero to production agents in five departments in roughly five weeks, because Managed Agents eliminated the infrastructure work.

Asana: AI Teammates in Project Management

Asana built AI Teammates using Managed Agents. These are agents that work alongside humans inside project management workflows: picking up tasks, drafting deliverables, turning brainstorm conversations into structured launch plans, and translating strategy into executable project plans with milestones and owners.

Asana reported adding advanced features "dramatically faster" than previous approaches allowed, which aligns with Anthropic's claim that Managed Agents reduces time-to-production by roughly 10x - Anthropic Blog.

Sentry: Debugging Agents

Sentry paired a debugging agent (Seer) with a PR-writing agent, reducing the time from bug detection to fix submission from weeks-to-months to significantly shorter cycles.

Vibecode: Prompt-to-Deployed App

Vibecode built a workflow where users describe an application in natural language and get a fully deployed app. Managed Agents handles the infrastructure spinup, code generation, testing, and deployment, reportedly 10x faster than their previous infrastructure.

Additional Adopters

Beyond the headline partners, several other companies have integrated Managed Agents into their products:

• Sentry paired a debugging agent (Seer) with a PR-writing agent. The debugging agent identifies the root cause of an error, then the PR-writing agent produces a fix. This reduced the time from bug detection to fix submission from a cycle that previously took weeks to a dramatically shorter turnaround.
• Vibecode built a prompt-to-deployed-app workflow where users describe an application in natural language and get a fully deployed product. Managed Agents handles infrastructure spinup, code generation, testing, and deployment, reportedly 10x faster than their previous setup.

These examples share a pattern: Managed Agents is most valuable when the agent needs to perform multi-step work that spans code execution, file generation, and external service interaction. Single-step tasks are better served by the Messages API.

Common Patterns Across Adopters

Three patterns emerge from these examples:

• Context is everything. Agents produce better results when connected to the full context of a task via MCP, not just given isolated instructions.
• Department-by-department rollout works. Rakuten's approach of deploying one department per week is replicable and reduces risk.
• Agent pairs outperform single agents. Sentry's combination of a debugging agent and a PR-writing agent produced better results than either would alone.

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7. Pricing: What It Actually Costs

Managed Agents pricing has two components: token usage and session runtime. There are no monthly subscriptions, no per-agent fees, and no minimum commitments.

Token Pricing

All tokens consumed within a Managed Agents session are billed at standard Claude API rates:

Prompt caching applies: cache reads cost 10% of standard input price (90% discount). Web search costs $10 per 1,000 searches. Web fetch is free beyond token costs.

Session Runtime

Runtime is measured to the millisecond and only accrues while the session status is running. Time spent idle (waiting for input), rescheduling, or terminated does not count.

Worked Example: One-Hour Coding Agent

A realistic one-hour coding session involves dozens of tool calls, reading files, writing code, running tests, and iterating on errors. A session like this using Claude Sonnet 4.6 typically consumes around 2M input tokens (reading files, tool results, accumulated context) and 500K output tokens (code generation, explanations, commands):

With prompt caching (1.6M of 2M input tokens cached, which is typical for sessions with repeated context):

The session runtime fee ($0.08/hour) is almost negligible compared to token costs. For a one-hour Opus 4.6 session with similar token volumes, the total would be roughly $22-35 depending on caching. The model you choose matters far more than the runtime fee.

Processing 10,000 Support Tickets

A realistic support ticket interaction involves the customer's message, the agent reading relevant knowledge base articles and past tickets, and generating a detailed response. A typical conversation runs 15,000-25,000 input tokens (customer message plus retrieved context) and 2,000-5,000 output tokens (response). Using Claude Sonnet 4.6 at the midpoint (~20K input, ~3.5K output):

With aggressive prompt caching (shared system prompt and knowledge base cached across tickets), input costs could drop by 60-80%, bringing the total to roughly $400-700 for 10,000 tickets. This is still dramatically cheaper than human agents (typically $5-15 per ticket), but it is not the pennies-per-ticket figure that toy examples suggest.

What Does NOT Apply

Several Messages API discounts and modifiers do not apply to Managed Agents sessions:

• Batch API discount (50% off) does not apply because sessions are stateful and interactive
• Fast mode premium (6x rates) does not apply because inference speed is managed by the runtime
• Data residency multiplier does not apply because inference_geo is a Messages API parameter
• Third-party platform pricing does not apply because Managed Agents is only available through the Claude API directly (not through AWS Bedrock or Google Vertex AI)

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8. What Managed Agents Is NOT

Understanding the boundaries of Managed Agents is as important as understanding its capabilities. Here is what it does not do.

It is not a no-code agent builder. You need developer skills to use Managed Agents. You are writing API calls, configuring YAML definitions, and streaming server-sent events. If you want a no-code experience for building AI agents, look at platforms like Lindy or o-mega.ai.

It is not a multi-model platform. Managed Agents runs Claude models only. If you need agents that use GPT-4, Gemini, or open-source models, you need a different platform or need to build on the Agent SDK with your own model routing.

It is not a complete business automation platform. It does not include pre-built templates for specific industries, drag-and-drop workflow builders, or out-of-the-box integrations with hundreds of business tools. It is infrastructure. You build the business logic on top of it.

It is not available on third-party clouds. Unlike the Messages API (available on AWS Bedrock, Google Vertex AI, and Microsoft Foundry), Managed Agents is only accessible through the Claude API directly. This matters for enterprises with strict cloud provider requirements.

It does not replace the Agent SDK for custom infrastructure needs. If you need VPC integration, custom sandboxing, unique execution environments, or infrastructure that runs on your own servers, the Agent SDK gives you that flexibility. Managed Agents trades customization for convenience.

It is not a consumer product. There is no UI. There is no dashboard for business users to create agents. Everything is API-driven. The Claude Console provides session tracing and analytics, but the creation and management of agents is entirely programmatic.

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9. Comparison: Managed Agents vs O-mega vs Lindy vs Agent SDK

The AI agent platform landscape has fragmented into distinct categories. Managed Agents sits at the intersection of "developer infrastructure" and "managed runtime," which makes it different from both no-code platforms and raw SDK frameworks.

Comprehensive Comparison Table

When to Choose Each

Choose Claude Managed Agents when:

• You are a developer building a product that needs agent capabilities
• You want Anthropic's infrastructure without building your own
• You need white-label agents (your branding, not Claude's)
• You are already invested in the Claude/MCP ecosystem
• Your use case requires custom agent logic that pre-built platforms cannot handle

Choose O-mega.ai when:

• You need a complete AI workforce platform, not just agent infrastructure
• Your team is non-technical or prefers a UI over API integration
• You need multi-model support (GPT-4, Claude, Gemini, DeepSeek auto-routed to the right model)
• You want pre-designed agents for specific roles (marketing, sales, research, customer support)
• You need native browser automation with persistent profiles and verified email identities
• You want agents that coordinate as a team, with lead agents delegating to sub-agents

O-mega operates at a higher level of abstraction than Managed Agents. Where Managed Agents gives you the infrastructure to build agents, O-mega gives you the agents themselves: configured, deployed, and ready to work. Each agent gets its own virtual browser, tools, and identity. The platform handles multi-agent orchestration natively, with lead agents planning tasks and dispatching them to specialized sub-agents.

For teams that want AI agents handling real business tasks without writing code, O-mega provides a more complete solution. For developers building custom agent-powered products, Managed Agents provides more flexibility.

Our guide to multi-agent orchestration covers the architectural patterns that both O-mega and Managed Agents implement.

Choose Lindy when:

• You want the simplest possible setup for business automation
• Your needs are covered by Lindy's 50+ pre-built templates
• You need extensive app integrations (5,000+ via Lindy's marketplace)
• Budget is a primary consideration ($49.99/month for Pro)
• Your use cases are primarily email, calendar, CRM, and meeting workflows

Lindy is the most accessible option for individual business users. It excels at specific workflow automation: email triage, meeting scheduling, CRM updates, and follow-ups. It does not provide the developer-level control of Managed Agents or the full workforce management of O-mega, but for solopreneurs and small teams with standard automation needs, it offers the fastest time-to-value.

For a broader survey of agent platforms and frameworks, our guide to AI agent frameworks and alternatives compares the full landscape.

Choose Claude Agent SDK when:

• You need maximum control over every aspect of agent behavior
• You need to run on your own infrastructure (VPC, on-premise, air-gapped)
• You are building something fundamentally different from what Managed Agents supports
• You need to integrate models from multiple providers in the same agent
• You want to start with the Agent SDK and potentially migrate to Managed Agents later

For a detailed analysis of Claude Code pricing (which is built on the Agent SDK), see our Claude Code pricing guide.

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10. Getting Started

Getting started with Claude Managed Agents requires a Claude API key (available from the Claude Console) and the beta header managed-agents-2026-04-01 on all requests. The SDK sets this header automatically - Quickstart Docs.

The CLI: Fastest Path to a Running Agent

Anthropic released a dedicated CLI tool called ant alongside Managed Agents. It is the fastest way to create agents, environments, and sessions without writing application code.

Install via Homebrew (macOS):

brew install anthropics/tap/ant
xattr -d com.apple.quarantine "$(brew --prefix)/bin/ant"

Install via curl (Linux/WSL):

VERSION=1.0.0
OS=$(uname -s | tr ' [:upper:]' ' [:lower:]')
ARCH=$(uname -m | sed -e 's/x86_64/amd64/' -e 's/aarch64/arm64/')
curl -fsSL "https://github.com/anthropics/anthropic-cli/releases/download/v${VERSION}/ant_${VERSION}_${OS}_${ARCH}.tar.gz" \
  | sudo tar -xz -C /usr/local/bin ant

Create an agent with one command:

ant beta:agents create \
  --name "Coding Assistant" \
  --model claude-sonnet-4-6 \
  --system "You are a helpful coding assistant. Write clean, well-documented code." \
  --tool '{type: agent_toolset_20260401}'

The agent_toolset_20260401 tool type is a bundled tool set that enables all built-in agent tools (bash, file operations, web search, web fetch, text editor, and more) with a single configuration line. This is the recommended starting point for most agents. You can also specify individual tools for fine-grained control.

SDK Support: 8 Languages

Managed Agents SDKs are available in eight languages, each of which sets the beta header automatically:

The breadth of SDK support is notable. Most competing agent platforms offer Python and TypeScript only. Having native SDKs in Java, Go, C#, Ruby, and PHP means enterprise teams working in non-JavaScript/Python stacks can integrate Managed Agents without wrapping REST calls manually.

Quick Start with Python (Official Example)

The following code from Anthropic's quickstart docs creates an agent, environment, and session, then sends a task and streams the response:

from anthropic import Anthropic

client = Anthropic()

# Step 1: Create an agent
agent = client.beta.agents.create(
    name="Coding Assistant",
    model="claude-sonnet-4-6",
    system="You are a helpful coding assistant. Write clean, well-documented code.",
    tools= [
        {"type": "agent_toolset_20260401"},
    ],
)

# Step 2: Create an environment (cloud container with unrestricted networking)
environment = client.beta.environments.create(
    name="quickstart-env",
    config={
        "type": "cloud",
        "networking": {"type": "unrestricted"},
    },
)

# Step 3: Start a session
session = client.beta.sessions.create(
    agent=agent.id,
    environment_id=environment.id,
    title="Quickstart session",
)

# Step 4: Stream events and send a task
with client.beta.sessions.events.stream(session.id) as stream:
    client.beta.sessions.events.send(
        session.id,
        events= [
            {
                "type": "user.message",
                "content": [
                    {
                        "type": "text",
                        "text": "Create a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt",
                    },
                ],
            },
        ],
    )

    for event in stream:
        match event.type:
            case "agent.message":
                for block in event.content:
                    print(block.text, end="")
            case "agent.tool_use":
                print(f"\n [Using tool: {event.name}]")
            case "session.status_idle":
                print("\n\nAgent finished.")
                break

The output looks like this:

I'll create a Python script that generates the first 20 Fibonacci numbers and saves them to a file.
[Using tool: write]
[Using tool: bash]
The script ran successfully. Let me verify the output file.
[Using tool: bash]
fibonacci.txt contains the first 20 Fibonacci numbers (0 through 4181).

Agent finished.

What happens under the hood when you send a user event: Managed Agents provisions a container from your environment configuration, runs the agent loop where Claude decides which tools to use, executes tool calls inside the container, streams events back to your application in real-time via SSE, and emits a session.status_idle event when the agent has nothing more to do.

Key Configuration Options

Model selection determines cost and capability. Use Haiku 4.5 for simple routing tasks ($1/$5 per MTok), Sonnet 4.6 for balanced workloads ($3/$15), and Opus 4.6 for complex reasoning ($5/$25).

Environment networking can be unrestricted (agent can access any URL) or locked down with allowlists. For security-sensitive deployments, restrict network access to only the domains your agent needs.

Tools define what the agent can do. Use agent_toolset_20260401 for the full built-in set, or specify individual tools for fine-grained control. Add MCP servers for specific integrations (Slack, GitHub, Salesforce, etc.).

System prompt should be specific about the agent's role, capabilities, and constraints. Vague instructions produce vague results. Include examples of expected output format, decision-making criteria, and edge case handling.

Rate Limits

Organization-level spend limits and tier-based rate limits also apply.

Community Reception

The Hacker News discussion generated 108 points and 51 comments - HN Discussion. Early developer feedback highlighted the distinction between agents that genuinely operate autonomously and those that simply chain API calls with human approval at each step. Testers reported agents handling 5-10 tasks in a row without intervention. Pricing was flagged as the key adoption variable: if Anthropic prices aggressively enough, it could pull smaller teams away from DIY solutions and competitor platforms.

What to Build First

The lowest-risk starting point is an internal tool: a research agent that gathers information, a code review agent that analyzes pull requests, or a data processing agent that transforms files. Internal tools let you experiment with agent capabilities without exposing incomplete work to customers.

Once you have confidence in the agent's performance and reliability, extend to customer-facing use cases. The session tracing in the Claude Console gives you full visibility into every tool call, decision, and failure mode, which is essential for debugging before going to production.

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Yuma Heymans is the founder of o-mega.ai, where he builds AI workforce infrastructure that deploys, manages, and scales teams of AI agents for business operations. He has worked with agent orchestration systems since 2024 and runs production agents on both Claude and multi-model stacks daily.

This guide reflects the Claude Managed Agents public beta as of April 8, 2026. The product is actively evolving: outcomes, multiagent, and memory features are in research preview and may change. Verify current capabilities and pricing at platform.claude.com.