Migrate a Workflow to Claude Managed Agents Safely
A safe rollout playbook for moving an existing workflow onto self-hosted Claude managed agents: shadow mode, canary, human-in-the-loop, and instant rollback.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
A safe rollout playbook for moving an existing workflow onto self-hosted Claude managed agents: shadow mode, canary, human-in-the-loop, and instant rollback.
Build an eval loop for self-hosted Claude managed agents: trajectory and outcome metrics, LLM-as-judge graders, golden datasets, and CI release gates.
Harden self-hosted Claude managed agents with sandbox isolation, least-privilege MCP tools, server-side secrets, and layered prompt-injection defense.
Make self-hosted Claude managed agents cheap and fast with prompt caching, batched tool calls, model routing, and context hygiene. Concrete tuning steps.
Trace and fix the three failure modes of self-hosted Claude managed agents: runaway loops, wrong tool calls, and hallucinated arguments over MCP.
What to put in a Claude managed agent's context and what to leave out: ordering, just-in-time retrieval, compression, and untrusted-content safety.
Auth, schemas, error handling, and idempotency for wiring MCP servers into Claude managed agents so tool calls survive retries safely.
Reusable patterns for tools, prompts, and context in Claude managed agents: verb tools, discovery-first, layered prompts, and context budgeting.
A hands-on walkthrough to stand up a self-hosted sandbox, run an MCP server, open an MCP tunnel, and drive a Claude managed agent.