A needs-first method for evaluating AI agents by task fit, controls, evidence and operational risk.
Define the job before evaluating the agent
Start with a bounded workflow that has a clear owner, recognizable inputs and a measurable result. An agent that looks impressive in a demonstration may still fail when records are incomplete, approvals are required or an exception needs human judgment. Document the real process before comparing products.
Test supervision and recovery
Useful agents expose what they did, preserve an audit trail and allow a person to pause, correct or reverse an action. Test common failures deliberately: missing permissions, ambiguous requests, unavailable systems and conflicting data. Recovery quality matters as much as completion speed.
Measure the complete operating cost
Include integration, monitoring, review time, model usage, security work and the cost of errors. A narrowly capable agent that behaves consistently can create more value than a broader system requiring constant supervision. Run a controlled pilot before granting access to sensitive systems.