Agent Work Needs Receipts
Agent output without a trail is not a product experience; it is an unsupported claim.
The move toward searchable agent sessions points to a larger product requirement. When AI completes work across files, tools, tickets, or customer data, users need a lightweight way to inspect the path from request to result.
Traceability is what lets teams trust automation without slowing everything down. It supports review, debugging, compliance, onboarding, and customer support. It also teaches users how the system thinks, which is part of making them better product builders.
Design every agent run to leave receipts: goal, context used, tools called, files or objects changed, checks run, unresolved risks, and next recommended action. Put that summary where the user already reviews work, not in a hidden log page.
Scored evaluation set
Take an agent-assisted task from your product or workflow and write the review receipt you would want before approving it. Then compare that to what your system currently shows.
Open the related LLM Evaluation exercise →Keep Going