Evaluating AI Agents In Practice: Benchmarks, Frameworks, And Lessons Learned
InfoQ, Monday, March 16th, 2026
You may have seen teams in your organization leveraging AI agents for demos, experiments, testing workflows where everything works perfectly. The agent plans, reasons, picks the right tool, and executes flawlessly during experiments. In production, the system fails or exhibits suboptimal behavior, and no one is quite sure whether the "smart" agent is actually reliable.
Agents are systems not models - evaluate them accordingly. AI agents plan, call tools, maintain state, and adapt across multiple turns. Single-turn accuracy metrics and classical natural language processing (NLP) benchmarks like bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE) don't capture how agents fail in practice. Evaluation must target the full system's behavior over time.
Behavior beats benchmarks. Task success, graceful recovery from tool failures, and consistency under real-world variability matter more than scoring well on curated test sets. An agent that works perfectly in a sandbox but silently misreports a failed refund in production hasn't passed any evaluation that counts.