Evaluation and Benchmarking of LLM Agents: A Survey
by Mohammadi et al. (arXiv)
A structured map of how to evaluate LLM agents — objectives, benchmarks, metrics, and tools.
Overview
This survey by Mahmoud Mohammadi, Yipeng Li, Jane Lo, and Wendy Yip (arXiv, July 2025) systematically reviews how LLM-based agents are evaluated and benchmarked along two axes: evaluation objectives (what to assess — agent behavior, capabilities, reliability, and safety) and the evaluation process (how to assess it — interaction modes, datasets and benchmarks, metric computation, and tooling). It surveys existing agent benchmarks and evaluation frameworks and, unlike much academic work, foregrounds practical enterprise concerns often missing elsewhere: data access controls, reliability requirements, long-horizon interactions, and regulatory compliance. The result is a structured reference an engineer can use to decide what dimensions of an agent to measure, which benchmarks and metrics fit a use case, and where current evaluation methods fall short.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Paper
- Cost
- Free
- Duration
- ~1 hour read
- Provider
- Mohammadi et al. (arXiv)
- Hands-on
- No
- Certificate
- None
What You’ll Learn
- ✓A two-dimensional framework (objectives vs. process) for reasoning about agent evaluation
- ✓The landscape of agent benchmarks, datasets, interaction modes, and metric-computation methods
- ✓How to evaluate agent reliability, safety, and behavior — not just task accuracy
- ✓Enterprise evaluation concerns: access controls, long-horizon tasks, and compliance
- ✓Where current agent-evaluation methods and benchmarks have gaps
Highlights
- •Comprehensive, recent (July 2025) synthesis of a rapidly evolving evaluation field
- •Bridges academic benchmarks with real enterprise reliability and safety requirements
- •A practical taxonomy you can apply directly when designing agent evals
Who It’s For
Best For
- ✓AI engineers designing evaluation suites for production agents
- ✓Researchers and technical leads mapping the agent-evaluation landscape
Prerequisites
- •Familiarity with LLM agents (tool use, planning, multi-step workflows)
- •Basic understanding of evaluation metrics and benchmarking
FAQ
What is Evaluation and Benchmarking of LLM Agents: A Survey?
A 2025 survey that organizes the fast-moving field of LLM-agent evaluation into a clear two-dimensional framework of what to evaluate and how. Aimed at AI engineers and researchers who need to design trustworthy evals for agents rather than rely on ad-hoc checks.
Is Evaluation and Benchmarking of LLM Agents: A Survey free?
Evaluation and Benchmarking of LLM Agents: A Survey is free to access.
What level is Evaluation and Benchmarking of LLM Agents: A Survey for?
Evaluation and Benchmarking of LLM Agents: A Survey is aimed at a intermediate audience. Recommended background: Familiarity with LLM agents (tool use, planning, multi-step workflows), Basic understanding of evaluation metrics and benchmarking.
How long does Evaluation and Benchmarking of LLM Agents: A Survey take?
Expect roughly ~1 hour read. Most learners work through it at their own pace.
What will I learn from Evaluation and Benchmarking of LLM Agents: A Survey?
You'll learn: A two-dimensional framework (objectives vs. process) for reasoning about agent evaluation; The landscape of agent benchmarks, datasets, interaction modes, and metric-computation methods; How to evaluate agent reliability, safety, and behavior — not just task accuracy; Enterprise evaluation concerns: access controls, long-horizon tasks, and compliance; Where current agent-evaluation methods and benchmarks have gaps.