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?

Here's one thing from my workflow that saves hours. If you push Kaggle kernels from Windows PowerShell 5, it adds a hidden BOM byte to your kernel-metadata.json and you get Kaggle Error. I switched to PowerShell 7.6.5, ran kaggle kernels push from D:\Dev\kaggle_push, and it pushed clean. Version 24 completed in 7 seconds on P100, wrote submission.parquet to kaggle working, and let me hit Submit Prediction. One fix — no more BOM errors. That's the Cinder Bubble Agent v18 shield.