AI agents are most useful when they reduce uncertainty—not when they silently replace judgment. T...AI agents are most useful when they reduce uncertainty—not when they silently replace judgment. T...
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Alessandro's avatar
Alessandro Federici
pro
• Sep 6
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?
AI Agent DevelopmentNext.jsGitHub
Jaden's avatar
Jaden Green
• Sep 13
Where it removed the most drag for me was real-estate contract deadlines - a chain of dates where one addendum moves a date and nothing throws an error. The agent is not there to judge, it is there to show that something moved.
The second-agent challenge is the bullet I would...
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