Survey: AI agent evaluation is getting realistic but still fails to measure safety and reliability
burkov · x · 2026-10-16
Andriy Burkov shares a survey on evaluating AI agents. As AI shifts from single-response systems to agents that plan, use tools, and act in changing environments, answer accuracy alone is no longer a sufficient metric.
Key points
- The survey covers agents performing multi-step tasks in web use, software engineering, customer service, and scientific research
- Central conclusion: evaluation is becoming more realistic, but still does not adequately measure whether agents are safe, robust, efficient, or dependable in practice
- The authors review benchmarks and tools across core abilities — planning, tool use, self-correction, and memory — and compare application-specific vs general-purpose agent evaluation
The goal is to map the evaluation landscape and identify gaps that matter for developing and deploying capable agents.
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