Claude Managed Agents

What is Claude Managed Agents?

Claude Managed Agents is a suite of composable APIs for building and deploying cloud-hosted AI agents at scale. Instead of requiring teams to build agent infrastructure from scratch (such as secure execution, state handling, permissions, and model-specific loop changes), Managed Agents pairs a performance-tuned agent harness with production infrastructure so prototypes can move to launch more quickly.

With Managed Agents, you define an agent’s tasks, tools, and guardrails, and the service runs it on Claude’s infrastructure. An orchestration harness decides when to call tools, how to manage context, and how to recover from errors.

Key Features

• Production-grade agent runtime with secure sandboxing and tool execution: The platform handles secure code execution, authentication, and tool calls so you can focus on what your agent should do.
• Long-running autonomous sessions with persistent progress and outputs: Agents can operate for hours, continuing across disconnections while retaining progress and produced outputs.
• Multi-agent coordination for parallelized pipelines (research preview): Agents can spin up and direct other agents to parallelize complex work; access is indicated as available via request.
• Trusted governance with scoped permissions, identity management, and execution tracing: When agents access real systems, the platform includes built-in governance controls and end-to-end tracing.
• Claude Console observability (session tracing, integration analytics, troubleshooting guidance): Trace and inspect tool calls, decisions, and failure modes directly in the Claude Console.

How to Use Claude Managed Agents

1. Start with the Claude Platform: Managed Agents is available publicly in beta on the Claude Platform.
2. Define your agent’s task and constraints: Specify the agent’s tasks, tools, and guardrails.
3. Connect tools and set up guardrails: Provide the tools the agent can use and constrain behavior via guardrails.
4. Deploy and monitor: Run the agent through the production infrastructure provided by Managed Agents, then use the Claude Console to trace tool calls and troubleshoot failures.
5. Iterate on outcomes (where available): For agentic workflows, Managed Agents supports an iterative self-evaluation approach (noted as research preview) and can also support traditional prompt-and-response workflows.

Use Cases

• Coding agents that produce a patch and open a pull request: An agent can read a codebase, plan a fix, write changes, and open a PR as part of a guided developer workflow.
• Productivity agents that execute multiple tasks with team collaboration: Agents can join a project, pick up tasks, and deliver outputs alongside a team’s existing workflow.
• Finance and legal document processing: Agents can process documents and extract key information tailored to the defined task and guardrails.
• Long-running, open-ended content generation with persistence: For examples like generating slides or spreadsheets over time, long-running sessions help keep progress and outputs even if a connection drops.
• Debugging and repair flows: A workflow can start from a flagged bug, have an agent write a patch, and then open a reviewable fix in one end-to-end flow.

FAQ

Is Claude Managed Agents available now, or is it only planned? It is available today in public beta on the Claude Platform.

What does Managed Agents handle for me versus what I define? You define the agent’s tasks, tools, and guardrails. Managed Agents runs the agent on its infrastructure and includes production infrastructure for secure sandboxing, authentication, tool execution, and orchestration.

How do long-running sessions work? The platform supports autonomous sessions that can run for hours, with progress and outputs persisting even through disconnections.

Can Managed Agents coordinate multiple agents? Yes, multi-agent coordination is available in research preview (access appears to require a request).

Where can I inspect what the agent did? The Claude Console includes session tracing, integration analytics, and troubleshooting guidance to inspect tool calls, decisions, and failure modes.

Alternatives

• Build your own agent infrastructure: Teams can assemble sandboxing, permissioning, state management, and orchestration themselves. This offers full control but requires significant engineering effort similar to what Managed Agents is designed to abstract away.
• Use traditional prompt-and-response agent loops without managed production runtime: For tightly controlled workflows, you can keep a more manual loop and handle monitoring and reliability outside a managed service. This may reduce infrastructure complexity but can increase work for long-running or production-grade behaviors.
• Use other agent orchestration platforms and frameworks: Instead of a managed production runtime, these solutions focus on coordinating agent steps and tools. You’d typically still need to address execution security, permissions, and operational concerns depending on the platform’s scope.