Testing and evals for Claude Cowork agents that ship
Build an eval loop that gates Claude Cowork releases — task-level metrics, deterministic and LLM-judge graders, regression suites, and CI that blocks regressions.
Deep dives into agentic AI, LLM evaluation, synthetic data generation, model selection, and production AI engineering best practices.
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Build an eval loop that gates Claude Cowork releases — task-level metrics, deterministic and LLM-judge graders, regression suites, and CI that blocks regressions.
A practical playbook for hardening Claude Cowork agentic AI — sandboxing, least privilege, secrets isolation, and layered prompt-injection defense.
Keep agentic Claude Cowork runs cheap and fast with prompt caching, batching, context trimming, and per-step model routing without sacrificing quality.
Fix the real failure modes of Claude Cowork agents — infinite loops, wrong tool selection, and hallucinated arguments — with grounded, practical techniques.
What to put in a Claude Cowork agent's context, what to leave out, and why — practical context engineering for reliable agentic knowledge work.
Connect MCP servers to Claude Cowork the right way — auth scopes, strict schemas, structured error handling, and idempotency keys for safe agentic workflows.
Reusable patterns for Claude Cowork agents — design prompts as contracts, shape tool interfaces, and engineer context so workflows stay reliable at scale.
A concrete, engineer-followable guide to building a real Claude Cowork workflow — scope the task, wire connectors, write a skill, and test the agentic loop.
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