AI Agent Evaluation Scorecard for Founders
A practical scorecard for deciding whether an AI agent belongs in production, needs a sandbox, or should stay a demo.
AI
7 min
The quick answer
Founders should evaluate AI agents across seven dimensions before production: task fit, input quality, autonomy level, failure severity, review path, unit economics, and ownership. If any dimension is unknown, the agent belongs in a sandbox.
The scorecard
Give every proposed agent a score from 1 to 5 for repeatability, data readiness, decision risk, customer impact, time saved, cost per successful output, and observability. The best first agents usually score high on repeatability and time saved, but low on irreversible risk.
Autonomy levels matter
Level 1 drafts. Level 2 recommends. Level 3 executes after approval. Level 4 executes inside strict limits. Level 5 acts independently. Most B2B teams should live at levels 2 and 3 until they have enough reviewed runs to trust the edge cases.
What to measure weekly
Track accepted outputs, rejected outputs, edits per output, exception rate, time saved, cost per accepted output, and the top three recurring failure modes. A production agent without a weekly review loop is just an unobserved workflow.
Internal link map
This connects to /topics/ai-agents-for-operators, /thoughts/every-ai-workflow-needs-an-eval-before-a-seat, and /thoughts/agent-trust-starts-with-sandboxes-not-permissions.
