Testing & evals for Claude agents: gating releases
Build an eval loop that measures Claude MCP agent quality and gates releases — task evals, LLM judges, regression suites, and CI integration that catch regressions.
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
Build an eval loop that measures Claude MCP agent quality and gates releases — task evals, LLM judges, regression suites, and CI integration that catch regressions.
Harden Claude MCP agents with sandboxing, least-privilege scoping, secrets discipline, and layered prompt-injection defense for safe production deployments.
Keep Claude MCP agents fast and cheap with prompt caching, batching, context pruning, and model routing — the levers that actually move your token bill.
Fix the failure modes that break production Claude MCP agents — runaway loops, wrong tool calls, and hallucinated arguments — with concrete defenses and tracing.
What to put in a Claude MCP agent's context and what to leave out: load-bearing rules, live schemas, compaction, ordering, and injection defense.
Safely wire MCP tools into Claude agents: scoped auth, strict schemas, structured error handling, and idempotency keys for production reliability.
Code-level patterns for Claude MCP agents: single-responsibility tools, read-then-write, layered prompts, plan-then-act, and context as a budget.
A hands-on walkthrough: define tools, build an MCP server, wire the Agent SDK loop, add idempotency, eval, and go live in stages with Claude.
The five planes of a Claude MCP agent that reaches production: model, harness, protocol, servers, and systems — plus where trust boundaries belong.