Claude Managed Agents cookbooks
Claude Managed Agents is Anthropic's hosted runtime for stateful, tool-using agents. You define an agent and a sandboxed environment once, then run them in sessions that persist files, tool state, and conversation across turns. These tutorials show it end to end.
Overview
Claude Managed Agents cookbooks
Claude Managed Agents is Anthropic's hosted runtime for stateful, tool-using agents. You define an agent and a sandboxed environment once, then run them in sessions that persist files, tool state, and conversation across turns. These tutorials show it end to end.
Applied cookbooks
- data_analyst_agent.ipynb builds an analyst that turns a CSV into a narrative HTML report using pandas and plotly. You'll configure an environment and agent, mount a dataset, stream the run, and retrieve the generated artifacts.
- slack_data_bot.ipynb wraps that agent in a Slack bot. Mention it with a CSV to get the report in-thread; replies continue the same session.
- sre_incident_responder.ipynb puts Managed Agents on the on-call path: a pager alert starts a session, the agent investigates and opens a PR, then pauses for human approval before merging. You'll wire the alert webhook, attach a Skill and custom tools, and review the full run in the Console.
Guided tutorials
End-to-end tutorials that teach the Managed Agents API surface
through realistic workflows. There's no strict reading order,
but CMA_iterate_fix_failing_tests.ipynb is a good entry point,
it introduces every API shape the others build on.
| Notebook | What it teaches |
|---|---|
CMA_iterate_fix_failing_tests.ipynb | Do → observe → fix loop on a failing test suite. The entry-point notebook: introduces agent / environment / session, file mounts, and the streaming event loop through the lens of getting a buggy package to green. |
CMA_orchestrate_issue_to_pr.ipynb | Issue → fix → PR → CI → review → merge through a mock gh CLI. Multi-turn steering, mid-chain recovery from a CI failure and a review comment. Sidebar shows how to swap the file mount for a github_repository resource against a real repo. |
CMA_explore_unfamiliar_codebase.ipynb | Grounding in an unfamiliar codebase, with a planted stale-doc trap. Sidebar shows how to add resources to a running session via sessions.resources.add. |
CMA_gate_human_in_the_loop.ipynb | Human-in-the-loop expense approval via custom-tool decide() / escalate(). Covers the custom-tool round-trip pattern, the requires_action idle bounce, and parallel-tool-call dedupe. |
CMA_prompt_versioning_and_rollback.ipynb | Server-side prompt versioning: create v1, evaluate against a labelled test set, ship v2, detect a regression, roll back by pinning sessions to version 1. Covers agents.update, version pinning on sessions.create, and where the review gate moves when prompts are not code. |
CMA_operate_in_production.ipynb | Production setup: MCP toolsets, vaults for per-end-user credentials, the session.status_idled webhook pattern for HITL without long-lived connections, and the resource lifecycle CRUD verbs. |
The streaming event loop is walked through line by line in the
iterate notebook and then factored into
utilities.stream_until_end_turn so the other notebooks can
gate notebook is the exception: it keeps the loop inline because
custom-tool agents need to handle requires_action idle bounces
in addition to end_turn, which the helper doesn't cover.
Getting started
Set ANTHROPIC_API_KEY in your environment, then open
data_analyst_agent.ipynb in Jupyter and run the cells top to
bottom. Each notebook installs its own dependencies and prompts
for any credentials it needs. The orchestrate-to-PR sidebar in
CMA_orchestrate_issue_to_pr.ipynb and the vault-backed MCP
example in CMA_operate_in_production.ipynb additionally need
GITHUB_TOKEN set (a fine-grained PAT with public-repo read is
enough).
All cookbook fixture data — input CSVs and supporting assets for
the applied cookbooks, plus the planted-trap fixtures the guided
tutorials read from — lives under example_data/. See
example_data/OVERVIEW.md for the
directory map.