"When evaluating agents, we need to think beyond "did the agent get the right answer?" There are three dimensions that matter: Outcome: Did the agent succeed? This is where rubrics vs verifiable outcomes come in. Sometimes you can verify success objectively (the SQL query returned correct data). Sometimes you need rubrics (was the customer support response helpful and did the user respond positively?). Trajectory: Was the path reasonable?"
"An agent can take two paths to the right answer, but one may have been slower and/or more expensive. Getting to the right answer inefficiently is still a failure in production. Behavior: Did it stay within bounds? Does the agent follow instructions? If you told it to look up evidence before running SQL, did it? Or did it skip steps and get lucky?"
Autonomous AI agents are often unnecessary and frequently do not guarantee product success. Many organizations pursue agent development during an agent gold rush, but agents do not inherently correlate with success and success criteria vary widely. Evaluation must include three dimensions: outcome (objective verification versus rubric-based judgments), trajectory (efficiency, speed, and cost of reaching results), and behavior (adherence to instructions and operational bounds). Identical final results can hide inefficient or risky processes. Practical deployment requires measuring verifiable outcomes, monitoring trajectories for cost and latency, and enforcing behavioral constraints to prevent shortcuts or unsafe actions.
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