Agent Benchmarks

Agent benchmarks are standardized evaluation suites that measure how well models and agent systems perform on specific task categories like coding, web navigation, tool use, and multi-step reasoning. Popular benchmarks include SWE-bench (real-world GitHub issue resolution), HumanEval (code generation), GAIA (general AI assistants), and Chatbot Arena (human preference rankings). Benchmarks provide a common language for comparing models and architectures, but they have significant limitations — performance on a benchmark doesn't always predict real-world utility, and systems can be optimized for benchmarks in ways that don't generalize. Understanding benchmarks matters for making informed model selection decisions, but relying exclusively on benchmark scores without building your own domain-specific evals is a common mistake that leads to poor production performance. This concept connects to model selection for using benchmarks to inform model choices, eval-driven development for building custom evals that matter for your specific use case, and quality metrics for defining what success looks like beyond benchmark scores.