Evaluate your LLM agents like software systems

LLM agent evaluation

Evaluate your LLM agents like software systems

From agent hallucinations to planning failures, Openlayer helps you test and debug LLM agents with precision and visibility.

Why evaluating agents is challenging

Agents aren't just prompts, they're systems

LLM agents operate through chaining, tool use, and reasoning steps. Evaluating them requires tracing not just final outputs, but every decision and interaction along the way.

What LLM agent evaluation involves

Track. Test. Trust.

Tool call correctnessVerify the agent selects the right tool and passes accurate parameters on every invocation.

Prompt response relevanceCheck that each reply addresses the user's intent and stays on topic.

Reasoning trace reviewInspect intermediate thoughts or chain steps to confirm logical, policy-aligned decision making.

Failure mode tagging (loops, hallucinations, dead ends)Label recurring issues so infinite loops, invented facts, or stalled flows get fixed fast.

Cost, latency, and token usage insightsSurface spend and speed metrics at the span level to keep budgets in check and SLAs tight.

Version regression tests for multi-turn interactionsDiff back-and-forth conversations across releases to catch subtle degradations before they reach production.

Openlayer's approach

Make agent evaluation repeatable and reliable

Trace each step of an agent’s reasoning process

Evaluate tool outputs, transitions, and outcomes

Compare runs across chains, tools, and prompt versions

Tag failures and surface reproducible bugs

Compatible with LangChain, LlamaIndex, custom agents

FAQs

Your questions, answered

What is LLM agent evaluation?

How do you evaluate agents?

Does Openlayer support agent debugging?

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LLM agent evaluation