Design AI agent workflows so humans can stop and recover them
that do real work need more than a strong model. The risky moment comes when an agent is wrong, slow, or missing needed context but still keeps moving. A safer setup starts with one narrow workflow and a clear input and output contract, so everyone knows what the agent should receive and produce.
Operation logs should separate user input, model output, , and human edits, making it easier to trace mistakes. A named reviewer should be able to pause the . There should also be a manual fallback path that works without the agent.
A rollback checklist should cover prompts, tools, and data sources, so the team can return to a known good setup when something breaks. Common control choices include human approval before each action, review after a run, tool-level , and a .
Key points
- The hard part is the handoff when an AI agent is wrong, slow, or missing context.
- Start with one narrow workflow and make the input and output contract visible.
- Keep operation logs that separate user input, model output, , and human edits.
- Assign a reviewer who can pause and keep a manual fallback path ready.
- Use a rollback checklist for prompts, tools, and data sources.