Agent Evaluation Benchmarks

The evaluation landscape for LLM agents has matured rapidly, growing from isolated function-call correctness tests to comprehensive interactive benchmarks. Ferrag et al. consolidate approximately 60 benchmarks developed between 2019 and 2025 into a unified taxonomy spanning 8 domains: general knowledge reasoning, mathematical problem-solving, code generation, factual grounding, multimodal tasks, and interactive assessments.

Hu et al. propose a complementary three-tier evaluation framework specific to tool use: tool-call validity (does the model produce syntactically correct tool invocations?), task completion (does the tool chain achieve the goal?), and interactive performance (how well does the agent perform in realistic, multi-turn environments like WebArena and OSWorld?).

A critical finding across both surveys: benchmark performance does not reliably predict real-world deployment robustness. This gap represents one of the field's most pressing challenges.

Key Claims

• ~60 benchmarks exist across 8 evaluation domains — Developed 2019-2025, covering reasoning, math, code, factual, multimodal, interactive. Evidence: strong (From LLM Reasoning to Autonomous Agents)
• Three-tier evaluation framework for tool use — Validity → completion → interactive performance. Evidence: strong (Agentic Tool Use in LLMs)
• Benchmark-to-deployment gap persists — Performance on benchmarks doesn't fully transfer to real-world scenarios. Evidence: moderate (From LLM Reasoning to Autonomous Agents)

Benchmarks & Data

• ~60 benchmarks taxonomized (Ferrag et al.)
• Key interactive benchmarks: WebArena, OSWorld (Hu et al.)
• 11 real-world application sectors documented (Ferrag et al.)

Open Questions

• How to build benchmarks that reliably predict real-world agent performance?
• Can evaluation keep pace with rapidly expanding agent capabilities?
• How to benchmark multi-agent collaborative scenarios fairly?
• What metrics capture safety failures, not just capability?

Related Concepts

• Agentic Reasoning — Benchmarks measure agentic capabilities
• LLM Tool Use — Three-tier evaluation framework specific to tool use
• Multi-Agent Systems — Interactive benchmarks increasingly test collaboration

Backlinks

Pages that reference this concept:

• OpenAI