Managed Agents Overview

Anthropic Managed Agents — the mandatory flow

Managed Agents is Anthropic's hosted agent runtime. The agent loop runs on Anthropic's orchestration layer; per-session containers host the workspace where the agent's tools execute. You don't run the loop, the model, or the container — you create the configuration and the sessions, then consume the event stream.

This skill is the foundation for every other Managed Agents skill in the bundle. Internalize it first.

The flow that's not optional

┌─ setup (once) ─────────┐     ┌─ runtime (every invocation) ─┐
│ agents.create()        │     │ sessions.create(             │
│   → store agent_id     │ ──→ │   agent={type:..., id: ID}   │
│     in config/env/db   │     │ )                            │
└────────────────────────┘     └──────────────────────────────┘

If your code calls agents.create() at the top of every script run, that's the #1 anti-pattern — it accumulates orphaned agents and pays create latency for nothing. Hoist agent creation to a one-time setup step (the nuvel scaffold's setup.py does this) and persist the ID.

Why agents are versioned

Every POST /v1/agents/{id} (an update) creates a new immutable version. Sessions can pin to a version: {type: "agent", id, version: 7}. This buys you:

• Reproducibility. A reported bug in production session #4392 came from agent version 7. You can spin up a new session pinned to version 7 and reproduce.
• Safe iteration. Deploy a new system prompt; existing sessions keep running on the old version, new sessions use the new one. If the new prompt regresses, pin new sessions back while you debug.
• A/B testing. Two cohorts on two versions; same agent ID.

If you bare-string the agent field (agent="agent_abc123"), sessions track the latest version at creation time — fine for most uses, not fine when you need reproducibility.

Control plane vs data plane

A useful split for thinking about deployment:

The nuvel-scaffolded project encodes this split: agent.yaml + environment.yaml + setup.py are control plane; server.py + orchestrator.py are data plane.

For team workflows, prefer the ant CLI for the control plane — it handles create / update --version N / archive lifecycle from CI against the YAML files.

Beta headers

managed-agents-2026-04-01 is required and the SDK sets it automatically for all client.beta.{agents,environments,sessions,vaults,memory_stores}.* calls. You don't pass it manually. Two exceptions:

• client.beta.files.list({scope_id: session.id}) — pulls session output files. The SDK adds the Files header (files-api-2025-04-14) automatically; you must explicitly add betas=["managed-agents-2026-04-01"] since it's the Files endpoint accepting a Managed Agents parameter.
• Skills API (client.beta.skills.*) — uses skills-2025-10-02, set automatically.

The pieces in one diagram

                       ┌─────────────────────────────────────┐
                       │  Anthropic orchestration layer      │
agents.create() ──────▶│  (agent loop: Claude + tool calls)  │
(config + version)     └──────────────┬──────────────────────┘
                                      │ tool calls
                                      ▼
environments.create() ─▶ Container ───┤
(template)                            │
                                      │
                                      └── sessions.create()
                                          (event stream in/out)

You author the agent and environment as YAML, apply them once via setup.py (or ant), and persist the IDs. Your application code only ever calls sessions.create() and consumes the event stream.

Common confusions

• "Why can't I pass model to sessions.create()?" Because the agent owns the model. Update the agent (creates a new version) or pin to a different agent.
• "Where do API keys for Stripe / GitHub REST / Slack go?" Not in the container — there's no env var injection, and vaults are MCP-only. The right answer is a custom tool handled host-side. See managed-agents-tools.
• "My session never ends." You're probably breaking on session.status_idle alone. Idle is transient when the agent is awaiting tool confirmations or custom-tool results. See managed-agents-events.
• "The first events of my session arrived as one batch." Stream-first ordering — open the SSE stream before sending the kickoff event. See managed-agents-events.
• "Archive feels safe — let me clean up." Archive on agents, environments, and memory stores is permanent with no unarchive. Existing sessions continue but new sessions cannot reference an archived resource. Sessions and vault credentials archive freely; the persistent resources don't. Always confirm before archiving production resources.

Quick reference

from anthropic import Anthropic

client = Anthropic()  # reads ANTHROPIC_API_KEY

# ── setup (once) ──────────────────────────────────────────────
env   = client.beta.environments.create(name="my-env", config={"type": "cloud", "networking": {"type": "unrestricted"}})
agent = client.beta.agents.create(
    name="My Agent",
    model="claude-opus-4-7",
    system="You are a helpful assistant.",
    tools=[{"type": "agent_toolset_20260401"}],
)
# Persist agent.id and env.id somewhere durable.

# ── runtime (every request) ───────────────────────────────────
session = client.beta.sessions.create(agent=agent.id, environment_id=env.id)
client.beta.sessions.events.send(
    session_id=session.id,
    events=[{"type": "user.message", "content": [{"type": "text", "text": "Hello"}]}],
)
with client.beta.sessions.events.stream(session_id=session.id) as stream:
    for event in stream:
        ...  # process events

Read managed-agents-events next for the streaming gotchas, then managed-agents-tools to learn how to wire prebuilt tools, MCP servers, and host-side custom tools.