AI Agent
Benchmark
Evaluate the work your AI agents are taking over.
Agent benchmark is about real work.
Why AI Agent Benchmark Matters
Agents are moving into real work. We need to know if the output can be trusted and checked.
What resources are useful?
Public benchmarks, papers, and tools help when you know what they measure.
How to Evaluate AI Agent with Benchmark in Practice?
Decide whether the agent's work is worth evaluating, how much to invest, and how to start from real workflow data.
Start with the frame.
Then test it.
Why AI Agent Benchmark Matters
"You probably are not short of demos. What you lack is judgment."
AI agent benchmark matters because agents are moving into real work. The question is no longer only whether they can do the job, but whether the work can be trusted.
How to Evaluate AI Agent with Benchmark in Practice?
"Is your agent worth evaluating? If so, how much should you invest, and how do you ensure the evaluation actually serves the business?"
Before spending on evaluation, decide whether the agent's work is worth evaluating, how much to invest, and how to start from real workflow data.
Benchmarking Real Work Case 2: Why Good Agent Evaluation Wasn't Enough for Production
"I evaluated the agent's processing capability, but not whether volunteers could use it to get their job done."
A real-world agent evaluation case study: a dialect interview workflow passed a real-data benchmark, then failed in volunteer operations until it was embedded into the existing Feishu workflow.
Curated resources,
with short notes on each one.
Benchmark resources
Public task sets for coding agents, browser agents, computer-use agents, and tool-heavy assistants.
Papers and leaderboards
Research context and scoreboards, with notes on what they do and do not prove for production use.
Evaluation tools
Frameworks for datasets, experiments, tracing, review loops, and post-launch monitoring.
What do you want reviewed?
Share one real workflow, result, or decision.
Every request is reviewed by hand.