Really interesting point. Passing tests doesn't always mean an AI agent is useful in real world situations. User experience and practical workflows matter just as much as accuracy.
Why AI Agents Pass Tests but Fail in the Real World — Yaoshen Luo | CoderLegion Developer Spotlight
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The gap between synthetic eval benchmarks and dirty production environments is huge. In sandboxes, tool schemas and return payloads are clean and deterministic, but in reality, API latency spikes, transient partial failures, and subtle schema drifts break multi-step agent trajectories fast. Incorporating resilience loops and state checkpoints matters way more than just passing isolated unit tests.
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Yaoshen's point about the volunteers rejecting a tool that worked is the part most evals miss. In my own multi-agent work, an agent can pass every task check and still fail because it adds steps to someone's workflow. I now score agents on workflow fit and whether every claim in a report traces back to evidence, not just task completion.
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