Your AI agent passed the evals. That's the problem. Output-based evaluation is built for a single response - but an agent is a chain of decisions, tool calls, and retries. When all your points at the end, you risk missing where the agent actually went wrong. Here's what to measure instead.
Why single-turn evals give false confidence
Most LLM evaluation starts with one input and one output. Score the response, compare it to a golden answer, and record a pass or fail. That works well for a chatbot or a summarizer. An agent is different: it plans, calls tools, observes results, and repeats. A "correct" final answer can hide bad reasoning, wasted tool calls, or a step that silently failed and was papered over by retries.
Evaluate the agent the way you evaluate a workflow, not a single answer. That means scoring at the step level and the completion level, then using traces to turn a pass into a diagnosis.
The agent evaluation stack
- Tool-calling accuracy - did the agent invoke the right tool, with the right arguments, at the right time?
- Task completion - did the end goal get achieved, not just a plausible-sounding answer produced?
- Multi-step reasoning - is each intermediate step coherent, or does the agent jump to conclusions?
- Turn boundaries - does the agent know when to call a tool versus respond to the user?
- Cost & latency - how many tokens and calls did it take to get there?
Measure the path, not just the destination
An output-based eval returns "pass." A trace-based eval returns "passed, but it called the search tool three times, hallucinated a SQL schema, and only recovered on the final retry." That second sentence is what you actually need in production.
Trace-aware judges can score each span, attribute a failure to the exact step, and feed that step back into your dataset. Over time the evaluation stops being a gate that says "good or bad" and becomes a map of exactly where your agent breaks:
# Treat agent evaluation as a workflow, not a single probe
# A trace records every tool call, span, and retry so a judge
# can score steps independently and pinpoint the failure.
# 1. Score each span (tool call, reasoning step)
# 2. Score overall task completion
# 3. Attribute regressions to a specific stepAttributing failures with traces
Traces make evaluation actionable. When an eval fails, the trace shows you the tool arguments, the retrieved context, and the intermediate reasoning that led to the mistake. This is the difference between knowing that your agent failed and knowing where it failed. Infere's LLM traces capture multi-step agent workflows end to end, so every evaluation failure points back to a resolvable step instead of a mystery score.
Passing evals means your tests didn't catch it - track the steps to see what actually happened.
Build the feedback loop
The most reliable signal for agent quality is production feedback looped back into your offline evaluation set. Capture failed steps from live traffic, promote them into your golden dataset, and rerun your evals. Now every release proves you didn't just stay green - you covered the failures that actually happened.
Next steps
Start with a small dataset of real task completions, score per-step with trace-aware judges, and wire the failures back into your test set. Combine agent evaluation with LLM traces and your existing AI eval pipeline to close the loop.