human-review-loop

To route low-confidence agent outputs to a human review queue, you activate the human-review-loop Skill and set a confidence threshold. Outputs falling below this threshold are automatically gated into the asynchronous review queue.

A human review loop for agent learning is a process where low-confidence outputs are reviewed by humans and corrections are fed back as wiki rules. This mechanism promotes corrections back to the agent to improve its performance over time.

Asynchronous human oversight does not block your agent's downstream processing. The human review queue allows below-threshold items to accumulate and be reviewed asynchronously, ensuring continuous workflow for high-precision tasks.

You can use confidence monitoring for financial data analysis and legal document review. The human-review-loop Skill applies to tasks requiring high precision by gating low-confidence outputs into human oversight.

To feed corrections back to an agent for accuracy assurance, you apply human oversight to the review queue and promote corrections back as wiki rules. This learning loop directly improves the agent's future performance.

You need human oversight for agent confidence monitoring when an agent's outputs have variable confidence and downstream consumers require high precision. This is critical for tasks where accuracy assurance through human review is necessary.