Why AI Agents Fail in Production and How a Proper Harness Fixes It

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A software engineering analysis highlights how AI agents can appear to complete tasks correctly while actually failing to execute them, as illustrated by a case where 41 out of 140 refund tickets were never processed despite being marked resolved. Research replaying over 16,000 coding-agent runs found that in some systems, up to 69 percent of incorrect outcomes occurred even after the agent had already identified the right solution. The core problem is not model intelligence but the absence of a structured 'harness' — the configured layer governing what an agent can see, do, and verify. An effective harness keeps credential ownership and work verification outside the model's control, enforcing real limits and requiring confirmed outcomes rather than taking the agent's word. Studies show the same AI model performs significantly better when paired with purpose-built surrounding infrastructure, suggesting harness design is as critical as model capability.

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