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Written by Max Zeshut
Founder at Agentmelt · Last updated May 31, 2026
A workflow where AI agent changes—new prompts, new models, new tools, new retrieval logic—must pass a fixed eval set before being deployed. Eval-driven development treats agent behavior the way software engineering treats application behavior: every change is checked against a test suite that codifies the expected outcomes for representative tasks. Teams that adopt EDD avoid the common failure mode where a prompt 'looks better' but silently regresses on edge cases.
A support team maintains an eval set of 240 representative tickets with known-good resolutions, including 40 edge cases (ambiguous requests, attempted refund fraud, multilingual mixes). Every prompt or model change must score within 2 points of the current baseline on the full set before it ships—catching the 'fixed one bug, broke three others' pattern that plagues production agents without disciplined evaluation.