Building Your First Claude Managed Agent: From Zero to Production Beta

Anthropic’s Claude Managed Agents entered public beta on April 8, 2026. If you’ve been waiting for a managed platform to deploy Claude-powered agents without standing up your own infrastructure, this is it. Here’s how to get started.

Prerequisites

Before you begin, you’ll need:

• An Anthropic API account (platform.anthropic.com)
• Access to the Managed Agents beta (apply at the developer platform)
• Basic familiarity with REST APIs or the Anthropic Python/TypeScript SDK
• Python 3.10+ or Node.js 18+ for the examples below

Step 1: Apply for Beta Access

Navigate to platform.claude.com and look for the Managed Agents section. As of the public beta launch, this is open to teams — not just individual developers. You’ll want to apply with your organization’s use case described, as Anthropic appears to be prioritizing enterprise workloads.

Once approved, you’ll receive:

• A managed agents endpoint URL
• Agent fleet credentials (separate from your standard API key)
• Access to the observability dashboard

Step 2: Define Your Agent’s Role and Tools

Claude Managed Agents uses a declarative configuration model. Before writing any code, you define what your agent does:

{
  "agent_name": "research-assistant",
  "description": "Searches and summarizes information from web sources",
  "tools": [
    {
      "type": "web_search",
      "config": {
        "max_results": 10,
        "safe_search": true
      }
    },
    {
      "type": "document_reader",
      "config": {
        "max_pages": 20
      }
    }
  ],
  "system_prompt": "You are a research assistant. When given a topic, search for recent, authoritative sources and provide a concise summary with citations.",
  "max_tokens": 4096,
  "model": "claude-sonnet-4-6"
}

The key difference from direct API calls: you’re defining the agent’s capabilities and identity once, not wiring up tools on every request.

Step 3: Deploy Your Agent via the SDK

With the Python SDK:

import anthropic

client = anthropic.Anthropic()

# Create a managed agent
agent = client.managed_agents.create(
    config_path="./research-assistant.json"
)

print(f"Agent deployed: {agent.id}")
print(f"Status: {agent.status}")
# Output:
# Agent deployed: agent_01AbCdEfGhIj
# Status: active

The agent.id is your persistent reference to this agent. Unlike standard API calls, the agent persists between invocations — you don’t recreate it on every request.

Step 4: Invoke Your Agent

# Run a task on your managed agent
response = client.managed_agents.run(
    agent_id="agent_01AbCdEfGhIj",
    task="Summarize the key developments in AI agent infrastructure from the past 30 days."
)

print(response.output)
print(f"Tokens used: {response.usage.total_tokens}")
print(f"Tools called: {response.tool_calls}")

Notice what you don’t have to handle:

• Tool execution retry logic
• Rate limit backoff
• Credential rotation for tool access
• Trace logging (handled automatically)

All of that is managed by the platform.

Step 5: Monitor in the Observability Dashboard

One of the biggest advantages of the managed platform is built-in observability. Navigate to platform.claude.com/managed-agents/[your-agent-id]/traces to see:

• Execution traces — every tool call, with inputs and outputs
• Latency breakdowns — where time is being spent (model inference vs. tool calls)
• Error rates — automatic alerting when your agent starts failing tasks
• Token usage over time — for cost forecasting

This is infrastructure you’d otherwise spend weeks building yourself.

Step 6: Set Up Auto-Scaling

For production workloads, configure scaling policies in your agent definition:

{
  "scaling": {
    "min_instances": 1,
    "max_instances": 10,
    "scale_up_trigger": {
      "queue_depth": 5,
      "latency_p95_ms": 30000
    },
    "scale_down_delay_minutes": 5
  }
}

This tells the managed platform to spin up additional agent instances when queue depth exceeds 5 tasks or P95 latency exceeds 30 seconds, and to scale back down after 5 minutes of reduced load.

Claude Managed Agents vs. Self-Hosted OpenClaw: When to Use Each

This is the practical question for teams evaluating both approaches.

Use Claude Managed Agents when:

• You’re building a new agent and want to reach production quickly
• Your team doesn’t have DevOps capacity for infrastructure management
• You’re already committed to the Claude model family
• Built-in observability is a meaningful time-saver for your team

Use self-hosted OpenClaw when:

• You need multi-model flexibility (Gemini, GPT-4o, open-source models)
• Data residency or compliance requirements mandate on-premise execution
• You need deep customization of the execution environment
• You’re building critical infrastructure where vendor dependency is a liability

The pragmatic answer for most teams: start with Claude Managed Agents, validate your agent’s value, then migrate to self-hosted if you hit constraints the managed platform can’t accommodate.

Common Gotchas in Beta

A few things to watch out for based on the platform’s early days:

Agent state persistence is session-scoped by default. If you need state to persist across user sessions, you’ll need to explicitly configure external memory storage. Don’t assume the agent “remembers” previous conversations automatically.

Tool timeout defaults are conservative. Web search and document reading have default timeouts that may not accommodate slow external APIs. Check the timeout configuration in your agent definition.

The observability dashboard has a ~60 second lag. Don’t expect real-time traces — useful for post-hoc debugging, less useful for live monitoring.

Rate limits apply per agent, not per account. Each agent has its own rate limit bucket, which is actually helpful for multi-agent architectures — one busy agent won’t throttle your others.

Next Steps

Once your first agent is running:

1. Write evals — check out LangChain’s Better-Harness (also released this week) for a systematic approach to agent evaluation
2. Test failure modes — deliberately trigger error conditions to verify your agent’s retry and fallback behavior
3. Review the cost model — understand how the managed platform fee scales with your usage before committing to production workloads

The platform is in beta, which means things will change. Stay close to the Anthropic docs and changelog as this evolves.

Sources

1. Anthropic — Claude Managed Agents documentation (platform.claude.com)
2. TechRadar — Claude Managed Agents launch coverage
3. The New Stack — Claude Managed Agents analysis
4. OpenClaw documentation — self-hosted agent setup

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260409-0800

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