Before running an agent pilot, name its owner and decide who may use it, what it may access, which results need review, and when to stop it. Keep evidence that another person can check. These decisions form the basis of governance.
This path supports pilot owners, operations teams, and reviewers. The guides offer questions and working templates; specific enforcement behavior belongs in the documentation for the systems you use.
Prepare for a pilot decision
- Evaluate an agent before production. Define representative cases, expected outcomes, and release criteria before judging the pilot.
- Review permissions and boundaries. Check information access and action authority separately.
- Design human review. Name the reviewer and define how work pauses or escalates.
- Plan governance and cost controls. Assign ownership for usage, spending, changes, and incidents.
Keep a small decision record
A practical record identifies the agent and version, the job owner, allowed sources and actions, evaluation evidence, known limits, and the person who accepted the pilot scope. Add a date for the next review and a way to stop the workflow.
For example, a limited internal policy Q&A pilot could record:
- Owner: The policy owner maintains the approved source set and reviews exceptions.
- Evaluation evidence: Include questions answered by current policy, a missing source, and conflicting versions. Proceed only when supported answers cite a current source and the other cases are routed clearly. See evaluation guidance.
- Permission: Read approved policy material for this job; no employee-record access or edits. Review the boundary with permissions and boundaries.
- Limit and decision: Draft explanations only. If evidence is missing or conflicts, pause and route the question to the named owner. Keep the pilot within this scope until the owner reviews its results.
Do not collapse quality, spending, and permission failures into one overall score. An inexpensive run may still produce an unusable answer; a persuasive answer may still exceed the workflow's authority. Give serious boundary failures their own decision rule.
Review after a meaningful change
Revisit the evidence when instructions, source material, models, tools, or access change. Repeat the cases affected by the change and retain enough context to explain the decision.
For AgentShelf product descriptions, see trust boundaries and governance and cost controls. Return to agent design when the job itself needs to change.
All guides in this topic
- Evaluate an AI Agent: Evaluate an agent by testing representative tasks, tool use, failure handling, and approval boundaries before widening access.
- AI Agent Permissions and Boundaries: Set an agent’s boundary across identity, information, capability, surface, and oversight, then check each limit in the connected systems.
- Governance and Cost Controls for AI Agents: Govern an agent by naming its owner, limiting data and actions, reviewing outcomes, and tracking cost per completed, human-checked task.
- AI Agent Failure Modes and Recovery: Diagnose an agent failure from evidence, then choose a bounded retry, correction, safe stop, or human handoff.
- AI Agent Observability: What to Track: Track workflow activity, errors, handoffs, and resource signals so an owner can investigate a run and decide what needs review.
- AI Agent Security Review Checklist: Review an agent’s users, information flows, identities, tools, dependencies, tests, and response owners before expanding its access or use.
- An AI Agent Governance Checklist for Teams: Give every agent workflow a named owner, approved scope, review path, operating contact, and decision for changing or stopping it.
- Define Who Can Use and See an Agent’s Work: Set separate audience, workspace, and administrator boundaries so people can invoke, maintain, and review an agent in the right context.
- Manage Agent Changes with Review and Rollback: Record changes to an agent’s instructions, sources, tools, model, and access, then retest affected cases before widening use.
- Manage Unapproved AI Use in Your Team: Make AI use visible through clear guidance, a safe reporting path, proportionate risk review, and an approved way to do useful work.
- Review an Agent’s Tools and Data Access: Inspect each connected tool for its identity, data scope, allowed operations, side effects, returned information, and failure behavior.