Agent Evaluation
In one line
Agent evaluation is the test and metric framework for measuring how accurately and safely an agent completes its goals — distinct from plain LLM benchmarking.
Going deeper
Agent evaluation is not just a higher-precision accuracy score. It also tracks whether the right tool was picked, whether the number of steps was reasonable, and whether irreversible actions were handled safely. Benchmarks in this space include SWE-bench (coding), WebArena (browsing) and GAIA (general tool use).
Off-the-shelf benchmarks rarely fit marketing. Most teams build their own scenario sets — 'write GEO content', 'run a CRM automation' — and pair them with LLM-as-a-Judge scoring because there is no single correct answer.
The common gap is failure tracking. Looking only at the wins hides the real risk. Production-grade evaluation watches the negative cases too — bad tool calls, privilege escalation attempts, infinite loops — as first-class signals.
Related terms
Sandboxing
Sandboxing means running an agent in an isolated environment so its actions cannot reach the outside system — a baseline practice for any autonomous agent.
AI AgentPermission Model
A permission model defines which tools, data and actions an agent is allowed to touch — the core safety layer for any autonomous agent.
AI AgentHuman-in-the-Loop
Human-in-the-loop (HITL) is the design pattern where an agent runs autonomously but routes critical decisions through a human for review and approval.
AI AgentAI Agent
An AI agent is an LLM-driven system that takes a goal, plans the steps, calls the tools it needs and runs the task end-to-end with limited human input.
AI AgentAutonomous Agent
An autonomous agent runs with minimal human input — it decomposes the goal, executes, evaluates and iterates on its own until the task is done.
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