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.

MongoDB on Managed Agents

Want to give an agent a real database — for retrieval, graph traversal, and as its system of record? CMA_with_mongodb_atlas.ipynb is the consolidated cookbook: three ways to connect MongoDB to a CMA agent (and where the credential lives on each path), the four retrieval patterns (vector / full-text / hybrid / graph) as liftable building blocks, and an end-to-end human-in-the-loop fraud-review agent with Atlas as the system of record and audit backbone.

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_with_mongodb_atlas.ipynb	MongoDB Atlas end to end: three connection paths (host-side custom tool, self-hosted sandbox, self-hosted MCP), the four retrieval patterns (vector / full-text / RRF hybrid / $graphLookup), and a human-in-the-loop fraud-review agent that records decisions and an append-only audit trail in the same cluster. Pauses on risky cases via the requires_action gate; shows an auto-resolving gate for CI and the webhook handoff for production.
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 and session.budget_reached webhook patterns for HITL and cost supervision without long-lived connections, inference_geo pinning for residency, and the resource lifecycle CRUD verbs.
CMA_remember_user_preferences.ipynb	Memory stores: a shopping agent that learns a customer's preferences in one session and recalls them in the next. Covers memory_stores.create, the resources attachment with per-attachment instructions, inspecting and seeding memories from your own application, and combining a per-customer read-write store with a brand-wide read-only store.
CMA_coordinate_specialist_team.ipynb	Heterogeneous team via the multiagent coordinator config: a coordinator runs three specialists (web-search researcher, file-reading librarian, rules-based pricer) with scoped toolsets to assemble a sales proposal. Covers the multiagent field, the thread_created / thread_message_received event types, why per-role tool scoping matters, and an advisor roster entry the coordinator consults before writing.
CMA_watch_subagents_live.ipynb	A curriculum-planning team you can watch in real time. Covers per-thread event_deltas so subagent text streams live, initial_events on session create, per-agent model effort as a cost lever, and versionless agents.update. Builds on the coordinate-specialist-team shapes.
CMA_plan_big_execute_small.ipynb	Coordinator-pattern economics: a frontier coordinator delegates the token-heavy web reading to cheap parallel workers, measured against a rigor-matched solo-frontier control with per-thread usage.list_cost metering and a session budget as the fan-out guardrail.
CMA_verify_with_outcome_grader.ipynb	Build a grade-and-revise loop with Outcomes: a writer drafts a cited research brief, a stateless grader fetches every URL and checks every quote against a rubric, and feedback drives revisions until the brief passes. Covers user.define_outcome, the span.outcome_evaluation_* events, and how to write a rubric the grader can act on.
CMA_consult_an_advisor.ipynb	A mid-tier working agent that consults a stronger model mid-turn via an advisor roster entry. Covers the \{"type": "advisor"\} entry in multiagent.agents, the anthropic.advisor thread lifecycle on the primary stream, per-consultation cost from the advisor thread's usage, and the redacted delivery arm.
CMA_cap_session_spend.ipynb	Enforced spend ceiling on one session. Covers the budget field on sessions.create, the session.usage snapshot events and usage.list_cost, the budget_reached pause, and raising, lowering, and removing the cap with sessions.update.
CMA_use_skills_from_a_repo.ipynb	Skills discovered from a mounted repository's root .claude/skills/ at session start, with no skills field on the agent. Covers the layout the scanner expects, the read-then-follow protocol, and the lifecycle rules (skill cap, single scan, lazy nested discovery).
CMA_pin_inference_geo.ipynb	Data residency on the agent definition: model.inference_geo pinning, where the pin is enforced, its interplay with the workspace allowed_inference_geos policy, and a one-session geography override with agent_with_overrides.

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 import it instead of repeating the match ev.type: block. The gate notebook and the MongoDB cookbook are the exceptions: they keep 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, the vault-backed MCP example in CMA_operate_in_production.ipynb, and CMA_use_skills_from_a_repo.ipynb additionally need GITHUB_TOKEN set (a fine-grained PAT with public-repo read is enough). The MongoDB cookbook in CMA_with_mongodb_atlas.ipynb additionally needs MONGO_URI set to an Atlas SRV connection string (an M0 free cluster works); a provider key — MDB_ATLAS_API_KEY or VOYAGE_API_KEY — is optional and only enables the live-embedding and reranker paths.

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.