Developer Builds Agent Evaluation Harness for Local AI After Spotting Key Gaps

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A developer building AI agents for a personal trading business realized after six months that he could not determine whether profitable results were due to smart agent decisions or his own manual overrides. To answer that question, he designed a custom Agent Evaluation Harness — a systematic framework for testing AI agents across varied scenarios, failure modes, and cost metrics. After reviewing more than 50 existing evaluation frameworks, he identified common shortcomings, including testing only happy paths, lacking baseline comparisons, measuring accuracy alone while ignoring cost, and skipping continuous regression testing. His harness automatically generates hundreds of test cases covering normal conditions, high volatility, edge cases, and API failures specific to trading environments. The project highlights that rigorous, ongoing agent evaluation is an engineering necessity, not an optional step, especially before deploying AI agents in real-world or financial contexts.

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