Claude Code permissions are where agent safety becomes concrete. If a run needs production data, billing config, deploy access, or a wider MCP tool, the default should be stop, explain, and wait for a human decision.
Passing tests are a useful signal, but they are not enough for production Claude Code work. Ask for a review packet that shows scope, evidence, boundary pressure, remaining risk, and rollback before merge.
New models matter. They change what is possible. But a serious AI strategy cannot be rebuilt around every launch. The hard work is deciding what should change in your products, teams, controls, and habits.
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.
Before giving Claude Code wider access, define what each run may read, edit, call, spend, and merge. A permission budget keeps agent speed inside a reviewable boundary.
MCP tools make Claude Code far more useful, but broad access turns a weak prompt into a production risk. Treat every tool as blast radius, not convenience.
Claude Code can waste more than tokens when it keeps retrying a weak task. Production teams need budgets, stop rules, and evidence before another agent attempt is allowed.
Claude Code can produce a working patch and still leave the next human with a weak handoff. Production teams need run records that show scope, evidence, risk, and rollback before review turns into archaeology.
I published Securing Enterprise AI Agents, a practical book on bounded AI autonomy, AgentSecOps, MCP security, RAG governance, identity, evals, policy, and evidence.
Human review matters, but it cannot fix every bad Claude Code boundary after the run. Production teams need scoped permissions, MCP limits, hard stops, and evidence before widening access.