claude-managed-agents

name

claude-managed-agents

description

Build applications with the Claude Managed Agents API — Anthropic's managed infrastructure for running Claude as an autonomous agent in a cloud container. Trigger this skill whenever the user wants to build with managed agents, asks about agent sessions, wants to stream agent events, uses client.beta.agents / client.beta.sessions / client.beta.environments, asks about the managed-agents-2026-04-01 beta, wants Claude to autonomously run tools in a sandbox, or asks about outcomes, multiagent orchestration, or persistent agent memory. Also trigger when the user asks how to run Claude for long-running tasks, async work, or when they want Claude to execute bash commands, write files, or browse the web autonomously.

Claude Managed Agents

Managed Agents is Anthropic's hosted infrastructure for running Claude as an autonomous agent. Instead of building your own agent loop, you get a cloud container where Claude can run bash, read/write files, search the web, and execute code — all managed by Anthropic. Built-in prompt caching, context compaction, and performance optimizations are included.

Official API documentation (always up to date):

• Overview & concepts: https://platform.claude.com/docs/en/managed-agents/overview
• Quickstart: https://platform.claude.com/docs/en/managed-agents/quickstart
• API reference: https://platform.claude.com/docs/en/managed-agents/api-reference
• Events reference: https://platform.claude.com/docs/en/managed-agents/events

If you need the latest details on any endpoint, parameter, or behavior not covered in this skill's reference files, fetch the relevant documentation page above.

Beta header required on every request:

anthropic-beta: managed-agents-2026-04-01

The SDK sets this automatically. Raw HTTP calls must include it manually.

---

The 4 Core Concepts

Agent       → reusable config: model + system prompt + tools + skills
Environment → reusable container template: packages + networking
Session     → live running instance of Agent inside Environment
Events      → the communication layer: you send events in, agent sends events out

Create Agent and Environment once. Reuse them across many Sessions. Each session gets its own isolated container — no shared filesystem between sessions.

---

Setup

pip install anthropic          # Python
npm install @anthropic-ai/sdk  # TypeScript

from anthropic import Anthropic
client = Anthropic()  # uses ANTHROPIC_API_KEY env var

ant CLI — Anthropic's official CLI for interactive agent/environment/session management. Install: brew install anthropics/tap/ant (macOS) or download from GitHub releases. The Python/TS SDK is sufficient for application code.

---

Step 1 — Create an Agent

agent = client.beta.agents.create(
    name="Coding Assistant",
    model="claude-sonnet-4-6",          # claude-opus-4-6 or claude-haiku-4-5 also valid
    system="You are a helpful coding assistant. Write clean, well-documented code.",
    tools=[{"type": "agent_toolset_20260401"}],  # enables all 8 built-in tools
)
# Save agent.id — you'll reference it in every session

Agent fields:

Field	Notes
model	Required. All Claude 4.5+ models. Fast mode: {"id": "claude-opus-4-6", "speed": "fast"}
system	Agent persona/behavior. Distinct from user messages (which describe the task).
tools	agent_toolset_20260401 = all built-in tools. See references/tools.md for fine-grained control.
skills	Anthropic pre-built: xlsx, pdf, docx, pptx. Or custom skills by ID. Max 20/session.
callable_agents	Research preview. Other agent IDs this agent can delegate to.
metadata	Arbitrary key-value pairs for your own tracking.

Attaching skills:

agent = client.beta.agents.create(
    name="Financial Analyst",
    model="claude-sonnet-4-6",
    system="You are a financial analysis agent.",
    skills=[
        {"type": "anthropic", "skill_id": "xlsx"},               # Anthropic pre-built
        {"type": "custom", "skill_id": "skill_abc123", "version": "latest"},  # your own
    ],
)

Versioning: Every update() call increments version. Always pass the current version back:

agent = client.beta.agents.update(agent.id, version=agent.version, system="new prompt")

Omitted fields are preserved. Arrays (tools, skills) are fully replaced on update. Archive = read-only.

---

Step 2 — Create an Environment

environment = client.beta.environments.create(
    name="my-env",
    config={
        "type": "cloud",
        "networking": {"type": "unrestricted"},  # or "limited" — see below
        "packages": {
            "pip": ["pandas", "numpy", "requests"],
            "npm": ["express"],
            "apt": ["ffmpeg"],
        },
    },
)
# Save environment.id

Networking modes:

• unrestricted — full outbound access (fine for dev)
• limited — allowlist only (recommended for production)

# Production-safe networking
"networking": {
    "type": "limited",
    "allowed_hosts": ["api.example.com", "storage.googleapis.com"],
    "allow_mcp_servers": True,       # lets MCP server endpoints through
    "allow_package_managers": True   # lets pip/npm registries through at runtime
}

Supported package managers: apt, cargo, gem, go, npm, pip Packages declared here are cached across all sessions sharing this environment.

For container specs, pre-installed runtimes, and filesystem layout, see references/container-reference.md.

---

Step 3 — Create a Session

session = client.beta.sessions.create(
    agent=agent.id,                # uses latest version
    # agent={"type": "agent", "id": agent.id, "version": 2},  # pin a specific version
    environment_id=environment.id,
    title="My task",               # optional, human-readable label
    vault_ids=["vault_abc"],       # optional — attach vaults for MCP auth credentials
    resources=[                    # optional file/repo mounts
        {
            "type": "github_repository",
            "url": "https://github.com/org/repo",
            "authorization_token": "ghp_...",
            "checkout": {"type": "branch", "name": "main"},
            "mount_path": "/repo",
        },
        {"type": "file", "file_id": "file_abc123", "mount_path": "/data/input.csv"},
    ],
)
# Save session.id

Session statuses:

Status	Meaning
idle	Waiting for input, or between turns
running	Actively processing
rescheduling	Transient error; auto-retry in progress
terminated	Unrecoverable error — stop your loop

Important: You cannot delete a running session — interrupt first, then delete. Files, environments, and agents are NOT deleted when a session is deleted — only the session history and container are gone. Use archive to preserve history in read-only form.

For MCP setup with vaults, see references/vaults-and-mcp.md. For session CRUD, usage/stats, and mid-session resource management, see references/session-management.md. For production patterns, cost monitoring, debugging, and testing strategies, see references/production-patterns.md.

---

Step 4 — The Event Loop

Open the stream first, then send the message. If you send before opening, the API buffers events and you may miss the start. In Python, always open the stream first to eliminate any race conditions.

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": "Write a fibonacci script and test it"}],
        }],
    )

    for event in stream:
        match event.type:
            case "agent.message":
                for block in event.content:
                    print(block.text, end="", flush=True)

            case "agent.tool_use":
                print(f"\n[{event.name}]", flush=True)

            case "agent.custom_tool_use":
                # You must execute this and send back the result — agent is waiting
                result = my_execute_tool(event.name, event.input)
                client.beta.sessions.events.send(session.id, events=[{
                    "type": "user.custom_tool_result",
                    "custom_tool_use_id": event.id,
                    "content": [{"type": "text", "text": str(result)}],
                    "is_error": False,
                }])

            case "session.status_idle":
                # Could be done, or could need your input — always check stop_reason
                break

            case "session.status_terminated":
                raise RuntimeError("Session terminated")

TypeScript:

const stream = await client.beta.sessions.events.stream(session.id);

await client.beta.sessions.events.send(session.id, {
  events: [{ type: "user.message", content: [{ type: "text", text: "..." }] }],
});

for await (const event of stream) {
  if (event.type === "agent.message") {
    for (const block of event.content) process.stdout.write(block.text);
  } else if (event.type === "session.status_idle") {
    break;
  }
}

---

Steering Mid-Execution

# Send a follow-up while the agent is running (or when idle)
client.beta.sessions.events.send(session.id, events=[{
    "type": "user.message",
    "content": [{"type": "text", "text": "Also add unit tests"}],
}])

# Stop the agent immediately
client.beta.sessions.events.send(session.id, events=[{
    "type": "user.interrupt",
}])

Sessions are stateful — history is persisted server-side. You can reconnect to an idle session and continue without recreating anything.

---

Tool Permission Handling

Tools have two permission modes: always_allow (auto-runs) or always_ask (pauses for your approval). When a tool needs approval, the agent emits session.status_idle with stop_reason.type = "requires_action".

for event in stream:
    if event.type == "session.status_idle":
        if hasattr(event, "stop_reason") and event.stop_reason.type == "requires_action":
            for event_id in event.stop_reason.event_ids:
                client.beta.sessions.events.send(session.id, events=[{
                    "type": "user.tool_confirmation",
                    "tool_use_id": event_id,
                    "result": "allow",          # or "deny"
                    # "deny_message": "...",    # optional explanation on deny
                }])
        else:
            break  # genuinely done

For fine-grained tool configuration (disable tools, whitelist mode, per-tool policies), see references/tools.md.

---

Session Event Quick Reference

Full reference with all fields and patterns: references/events.md

Event	Key Fields	What to do
agent.message	content[].text	Print the text
agent.tool_use	name, input	Observe — runs automatically
agent.custom_tool_use	id, name, input	Execute it, send user.custom_tool_result
agent.thinking	content	Extended thinking — log or ignore
session.status_idle	stop_reason	Check stop_reason.type before acting
session.status_running	—	New turn started — informational
session.status_terminated	error	Unrecoverable — raise and stop
session.error	error.retry_status	Check if retrying / exhausted / terminal
session.deleted	—	Stream is over — break immediately
span.model_request_end	model_usage	Token counts per inference call

---

Common Patterns

Reconnect to an existing session

# Fetch full event history
events = client.beta.sessions.events.list(session.id)
for event in events.data:
    print(event.type, event.processed_at)

# Then open a new stream and continue
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": "Continue where you left off"}],
    }])
    ...

Retrieve output files

files = client.beta.files.list(scope_id=session.id)
for f in files.data:
    content = client.beta.files.download(f.id)
    content.write_to_file(f.filename)

Archive vs delete

client.beta.sessions.archive(session.id)  # read-only, history preserved
client.beta.sessions.delete(session.id)   # permanent — cannot delete a running session

---

Research Preview Features

Three features require a separate beta header and access approval:

anthropic-beta: managed-agents-2026-04-01-research-preview

• Outcomes — Define a rubric; a grader evaluates the output and the agent iterates until done.
• Multiagent — One orchestrator delegates work to specialist subagents (one level deep).
• Memory Stores — Persistent document collections that survive across sessions.

See references/research-preview.md for full setup and code examples. Request access →

---

Rate Limits

Operation type	Limit
Create (agents, sessions, environments, vaults, etc.)	60 req/min
Read (retrieve, list, stream, etc.)	600 req/min

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

---

Branding Guidelines

When building a product powered by Claude Managed Agents:

Allowed: "Claude Agent", "{YourName} Powered by Claude" Not permitted: "Claude Code", "Claude Code Agent", or any branding that mimics Claude Code products

---

Common Mistakes

Mistake	Fix
Missing beta header in raw HTTP	Add anthropic-beta: managed-agents-2026-04-01 to every request
Sending message before opening stream	Open stream first — race conditions are real in Python
Not checking stop_reason.type on idle	session.status_idle fires for both completion AND required actions
Not handling agent.custom_tool_use	Agent hangs indefinitely waiting for user.custom_tool_result
Updating agent without passing version	Will fail — always pass the current version
Assuming environments share filesystems	Each session gets its own container — no shared state
limited networking without allow_package_managers	Pip/npm installs at runtime will fail
Putting MCP auth token on the agent	Wrong — URL only on agent; auth goes in a Vault via vault_ids on the session
Deleting a running session	Interrupt first, wait for idle, then delete
Expecting session delete to clean up files/agents/envs	Those are separate resources — session delete only removes history and container