Claude Code evals should start with the run that scared you
The best Claude Code eval is not a tidy benchmark. It is the uncomfortable run your team does not want to repeat, captured as a replayable production control.
Topic archive
6 essays tagged Evals. Practical notes on what happens after the demo: prompts, tools, review packets, evals, rollback, and production ownership.
The best Claude Code eval is not a tidy benchmark. It is the uncomfortable run your team does not want to repeat, captured as a replayable production control.
The AI POC is not the hard part anymore. The hard part is turning a promising demo into a service with ownership, evals, traces, cost controls, and a rollback path.
A latency chart will not explain why an AI answer was wrong. Production LLM systems need traces, sources, tool calls, prompt versions, eval results, and human decisions.
Claude Code gets risky when a failed run keeps retrying without a stop rule. Use failure budgets, review packets, evals, and rollback notes before giving agents more autonomy.
Claude Code gets risky when teams roll it out through enthusiasm instead of a runbook. Start with task contracts, scoped permissions, review packets, evals, and rollback before widening autonomy.
Production Claude Code evals should not begin with abstract benchmarks. Start with the agent runs that scared you, reduce them into replayable cases, and use them to tune permissions, prompts, tools, and review gates.