Claude Managed Agents: What They Are, What They Do, and How Eigent Offers a Local Alternative

On April 1, 2026, Anthropic quietly launched one of the most significant additions to the Claude platform since the model itself: Claude Managed Agents. This is not a new model — it is a new way to run Claude. Instead of sending prompts and getting responses through the Messages API, you can now deploy Claude as a fully autonomous agent inside managed cloud infrastructure, with persistent sessions, built-in tools, and multi-agent orchestration.

For developers building AI-powered products, this is a meaningful shift. For teams evaluating how to integrate autonomous agents into their workflows, it raises important questions about control, cost, and vendor lock-in.

In this post, we break down what Claude Managed Agents actually is, what it supports, who it is built for, and — at the end — how Eigent's Add Worker feature with CAMEL-AI's open-source framework provides a local-first alternative that delivers similar capabilities without cloud dependency.

What Is Claude Managed Agents?

Claude Managed Agents is a fully managed runtime for building and deploying autonomous AI agents powered by Claude. Instead of building your own agent loop, tool execution layer, sandbox, and orchestration logic, Anthropic provides all of that infrastructure as a service.

Think of it this way: the Messages API gives you a model you can talk to. Claude Managed Agents gives you a model that can act — reading files, running shell commands, searching the web, and executing code inside a secure cloud container that Anthropic provisions and manages on your behalf.

The service is currently in beta, requiring the managed-agents-2026-04-01 header on all API requests. It is available to all Anthropic API accounts by default, with certain advanced features (outcomes, multiagent, and memory) in research preview with gated access.

Core Concepts: How It Works

Claude Managed Agents is built around four primitives that you compose to create autonomous workflows.

Agents

An agent is a reusable configuration that defines the model (e.g., claude-sonnet-4-6), the system prompt, the available tools, any MCP server connections, and skills. You create an agent once and reference it by ID across multiple sessions. Agents are versioned, so you can iterate on your configuration without breaking existing sessions.

Environments

An environment is a cloud container template. You configure it with pre-installed packages (Python, Node.js, Go, and more), network access rules, and mounted files. When a session starts, Anthropic provisions a fresh container from your environment template. This is where your agent actually runs — its sandbox.

Sessions

A session is a running instance of an agent within an environment. It is the actual execution context where Claude takes action, generates files, runs commands, and produces outputs. Sessions are stateful: the file system persists across interactions, and the full conversation history is retained server-side.

Events

Events are the communication layer between your application and the running agent. You send user messages as events. Claude streams back responses, tool invocations, and status updates via server-sent events (SSE). You can steer or interrupt the agent mid-execution by sending additional events — redirecting its work without killing the session.

What Tools Does It Support?

Claude Managed Agents ships with a comprehensive built-in toolset that covers the most common agent actions.

The default toolset (agent_toolset_20260401) includes Bash for executing shell commands in the container, Read for reading files from the local filesystem, Write for writing files, Edit for string replacement in files, Glob for file pattern matching, Grep for regex-based text search, Web Fetch for retrieving content from URLs, and Web Search for searching the web.

All tools are enabled by default, but you have granular control. You can disable specific tools (e.g., turn off web_fetch for a coding-only agent) or flip the default to off and enable only the tools you need. This is done through the configs array in your agent definition.

Beyond built-in tools, Claude Managed Agents supports custom tools — the same pattern as user-defined tools in the Messages API. You define the tool contract (name, description, input schema), and Claude emits structured requests when it wants to call your tool. Your application executes the operation and sends the result back. The model never executes custom tools directly; you retain full control over what runs.

MCP (Model Context Protocol) servers are also supported, giving agents access to external tool providers and data sources through the standardized MCP interface.

Multi-Agent Orchestration

Perhaps the most powerful capability — currently in research preview — is multi-agent sessions. This allows one coordinator agent to delegate work to other specialized agents, each running in its own isolated thread within the same container and filesystem.

Here is how it works in practice. You create multiple agents — say, an "Engineering Lead" coordinator, a "Code Reviewer" agent, and a "Test Writer" agent. When defining the coordinator, you list the other agents as callable_agents. When you start a session with the coordinator and give it a task, it can autonomously spawn threads for the reviewer and test writer, delegating specific subtasks to each.

Each agent runs with its own model, system prompt, and tools. Threads are persistent — the coordinator can send follow-ups to an agent it called earlier, and that agent retains its full context. The session-level event stream gives you a condensed view of all activity, while individual thread streams let you drill into a specific agent's reasoning and tool calls.

There is one constraint worth noting: only one level of delegation is supported. The coordinator can call other agents, but those agents cannot delegate further. This keeps the orchestration graph flat and predictable.

Who Is Claude Managed Agents For?

Claude Managed Agents is designed for a specific profile of builder and workload.

It is built for developers integrating autonomous Claude agents into products and services. If you are building a product where Claude needs to act — not just respond — and you want Anthropic to handle the infrastructure (containers, tool execution, sandboxing), this is the service.

It excels at long-running, asynchronous tasks. Sessions can run for minutes or hours with multiple tool calls, making it suitable for complex workflows like code generation, research pipelines, data processing, and automated analysis.

It is ideal for teams that want minimal infrastructure overhead. Instead of building your own agent loop, provisioning sandboxes, and handling tool execution, you get all of that out of the box. The tradeoff is vendor dependency — your agents run on Anthropic's cloud, your data flows through their infrastructure, and you are locked to Claude models.

It suits teams building on the Claude ecosystem. If you are already using the Anthropic API and want to extend into agentic workflows without managing additional infrastructure, Managed Agents is the natural next step.

What It Costs

Claude Managed Agents uses the same token-based pricing as the Messages API, plus infrastructure costs for the managed containers. Standard organization-level spend limits and tier-based rate limits apply. Create endpoints are limited to 60 requests per minute, and read endpoints to 600 requests per minute per organization.

The Tradeoffs: What to Consider

Claude Managed Agents is a well-engineered service, but it comes with inherent tradeoffs that matter for certain teams and use cases.

Cloud dependency. Your agents run on Anthropic's infrastructure. Every prompt, file, and tool execution flows through their cloud. For teams with strict data governance, compliance requirements, or proprietary data, this can be a dealbreaker.

Model lock-in. Managed Agents only supports Claude models. You cannot mix in GPT for code generation, Gemini for multimodal tasks, or local models via Ollama. If model diversity matters to your workflow, you are constrained.

Pricing at scale. Token costs plus container infrastructure costs can add up quickly for long-running, multi-agent sessions. The cost model favors lower-volume, higher-value tasks rather than high-throughput automation.

Beta limitations. Multi-agent orchestration, outcomes, and memory are all in research preview with gated access. The core product is solid, but the most powerful features are not yet generally available.

No local option. There is no self-hosted or on-premises deployment. Managed means managed — you cannot run this on your own servers.

Eigent's Add Worker: A Local-First Alternative Built on CAMEL-AI

For teams that want similar capabilities — creating, configuring, and orchestrating autonomous AI agents — without cloud dependency or vendor lock-in, Eigent offers a fundamentally different approach.

Eigent is an open-source (Apache 2.0) multi-agent AI cowork platform built on top of CAMEL-AI, the industry-standard open-source multi-agent framework. Where Claude Managed Agents provides agent infrastructure as a cloud service, Eigent provides it as a desktop application that runs entirely on your machine.

The Add Worker Feature

Eigent's Add Worker feature is the local equivalent of creating an agent in Claude Managed Agents. Here is how it works:

You navigate to the Workforce screen in Eigent and click "Add Worker." You name your worker, provide a description, and assign an Agent Tool — typically an MCP server that defines the worker's capabilities. Save it, and you have a specialized AI agent ready to execute tasks using that toolset.

For example, you might create a "GitHub Worker" backed by a GitHub MCP server, a "Database Worker" connected to your PostgreSQL instance, or a "Research Worker" with web browsing capabilities. Each worker is a specialized agent persona that can be invoked individually or orchestrated as part of a coordinated multi-agent workflow.

The key difference from Claude Managed Agents: everything runs locally. Your files never leave your machine. Your prompts are not sent to a third-party orchestration service. The agent execution, tool calls, and file operations all happen on your desktop.

CAMEL-AI: The Multi-Agent Engine

Where Anthropic built a proprietary orchestration runtime, Eigent leverages CAMEL-AI — an open-source multi-agent framework designed for reliability and extensibility. CAMEL-AI handles the hard parts of multi-agent coordination: task decomposition, subtask routing, dependency management, and parallel execution.

When you give Eigent a complex task, the CAMEL-AI engine breaks it into subtasks and distributes them across your configured workers. A Developer worker writes code while a Browser worker gathers research while a Document worker formats outputs — all running in parallel, coordinated automatically.

This is architecturally similar to Claude Managed Agents' multi-agent sessions, but with three critical differences: it runs locally on your infrastructure, it supports any LLM provider (Claude, GPT, Gemini, Ollama, and more), and the orchestration framework itself is open source and fully auditable.

Side-by-Side: Claude Managed Agents vs Eigent Add Worker

When Eigent Makes More Sense

If your team needs data sovereignty — regulated industries, proprietary codebases, sensitive business data — running agents locally with Eigent eliminates the compliance conversation entirely.

If you want model flexibility, Eigent's model-agnostic architecture means you can assign different models to different workers. Use Claude Opus for complex reasoning tasks, GPT for code generation, and a local Ollama model for privacy-sensitive operations — all within the same multi-agent workflow.

If you prefer to own your infrastructure, Eigent's open-source stack (FastAPI, Electron, CAMEL-AI) means you can inspect, modify, and extend every layer. No waiting for a vendor to ship a feature. No beta access gates.

And if cost matters at scale, Eigent is free. You pay only for the API calls your workers make to model providers — and if you run local models via Ollama, you pay nothing at all.

The Bottom Line

Claude Managed Agents is a strong product for developers who want Anthropic to handle the infrastructure of autonomous agent execution. It simplifies the hard parts — sandboxing, tool execution, session management, and multi-agent coordination — into a clean API that works out of the box.

But managed infrastructure comes with managed tradeoffs: cloud dependency, model lock-in, and data flowing through a third party. For teams that need control, privacy, and flexibility, these tradeoffs matter.

Eigent's Add Worker feature, powered by CAMEL-AI's open-source multi-agent framework, delivers the same core capability — creating, configuring, and orchestrating autonomous AI agents — without those constraints. You build and manage your agents locally, on your own machine, with any model provider, and with full source-code transparency.

Both approaches have their place. If you want the fastest path to a cloud-hosted Claude agent, Managed Agents is excellent. If you want to own your agent infrastructure and keep your data local, Eigent is the open-source alternative worth evaluating.