Google、AI コーディングエージェントの評価・反復・防御手法を解説

While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down.

To solve this, developers should adopt behavioral evaluations—fast, local, unit-style tests that assert on discrete intermediate actions

By building these inexpensive micro-checks alongside macro benchmarks, engineering teams can confidently iterate on system prompts and upgrade models without the risk of regressions.