AI agents are most useful when they reduce uncertainty—not when they silently replace judgment.

The Next.js team just used a research agent to review a backlog of 2,244 GitHub issues. In roughly three weeks, 1,462 issues were closed and only 3 were reopened.

What made the workflow credible: • every investigation ran in an isolated sandbox • the agent checked code, versions, PRs, commits, releases, and docs • it actively searched for evidence against its first conclusion • risky actions stayed read-only until humans reviewed the evidence • the clearest cases required a second-agent challenge before automation could close them

That is the pattern I trust for production AI automation: evidence first, structured outputs, bounded permissions, and reversible actions.

Where could an evidence-driven agent remove the most operational drag in your workflow?