AI Agent Evaluation and Assurance Field Guide
This field guide helps a business leader choose the next evaluation task, from defining the decision through controlled testing, judge validation, regression and production review.
If the intended use is still unclear
Begin with the business process, the business task the agent will perform and the decision management must make. Define users, authority, evidence, tools, consequences and the simpler alternative. The AI Agent Evaluation Consulting guide explains the commissioning question. How Marketways evaluates AI agents explains the complete evidence path.
If the team needs a defensible test collection
Use How to Build an AI Agent Evaluation Test Set to represent ordinary work, consequential failures, false alarms and held-out evidence. Read Test Data Is Not the Agent's Operational Data before assuming that a dataset platform also reproduces business records, permissions and external state.
If the agent uses tools or changes systems
Use Tool Use, Permissions and Side Effects to define action evidence. Sandboxes, Mock Tools and Seeded Business Data covers safe environment design, injected failures and final-state assertions. These guides are central when the agent creates records, sends messages, changes bookings or initiates transactions.
If the workflow spans time or several agents
Use Memory and Multi-Step Follow-Through for delayed events, corrections and unresolved commitments. Use Multi-Agent Workflows, Handoffs and Coordination where agents hand tasks to one another. The combined question is whether the entire system remains accountable for the completed result.
If the scores themselves are in doubt
Use How to Validate an LLM Judge to build human-reviewed calibration evidence and investigate directional error. Use Metrics, Scorecards and Release Thresholds to keep critical failures visible and connect measures to a release, restriction or remediation decision.
If the agent is already in production
Use From Production Failure to Regression Test when a specific incident needs investigation. Continuous AI Agent Evaluation in Production covers sampling, drift and repeated release checks. Production evidence should improve the suite without being treated as automatic ground truth.
If a platform must be chosen
Read AI Agent Evaluation Platforms Compared for a documented capability review. Then use How to Select an AI Agent Evaluation Platform to define requirements for datasets, traces, graders, human review, sandboxes, exports and CI. The product should follow the evidence requirement rather than define it.
Example: evaluating a maintenance coordination agent
Define the release claim as preparing and coordinating work for one asset family without downgrading safety or closing work before verified restoration. Build scenarios from reviewed fault history and deliberately designed cases involving similar asset identifiers, intermittent alarms, unavailable technicians and unsuccessful work-order calls. Use deterministic checks for asset, qualification, permit and final state, with engineer review of diagnostic support. Repeat variable cases and keep every wrong-asset action visible. Begin in shadow mode and convert verified overrides or repeat faults into regression cases.
The field guide deliverables
A complete engagement produces an evaluation scope, scenario register, controlled environment design, grader specification, release scorecard and monitoring plan. Marketways also records coverage gaps and the system changes that require re-evaluation. The evaluation platform may store cases, run experiments and display traces, while the business test harness supplies operational records, mock tools and final-state assertions. Keeping those responsibilities separate is essential when a platform dataset does not reproduce the system the agent will actually change.