Evals for Claude Agents: Measuring Quality & Gating Ship
Build an eval loop for agentic Claude systems: task-level checks, LLM-as-judge, trajectory metrics, and CI gates that block regressions before they ship.
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 for agentic Claude systems: task-level checks, LLM-as-judge, trajectory metrics, and CI gates that block regressions before they ship.
Harden Claude agents against prompt injection: sandbox tool execution, enforce least privilege, keep secrets host-side, and bound the blast radius.
Harden agentic Claude systems with sandboxing, least-privilege scoping, secret handling, and prompt-injection defense. Break the lethal trifecta.
Cut Claude agent costs with prompt caching, the Batches API, effort tuning, and model routing — keep runs cheap and fast without losing quality.
Keep agentic Claude runs cheap and fast with prompt caching, per-step model routing, batching, and context trimming — without losing quality.
Diagnose and fix the failure modes Claude agents hit in production: runaway loops, wrong tool calls, and hallucinated arguments — with concrete fixes.
Fix the three failure modes that break Claude agents in production: runaway loops, wrong tool calls, and hallucinated arguments. A practical debugging guide.
Design context for Claude agents: what to keep, what to load on demand, what to leave out, and why cutting content sharpens reasoning and cuts cost.
Context engineering for Claude agents: the least-context principle, what earns a place in the window, what to evict, and compaction for long tasks.