Four glowing overlapping spheres, representing evaluating a chain of steps end to end.
A workflow is a chain of hand-offs, so you measure the whole arc, not one catch at a time.

Workflow evaluation tests a chain of AI steps and hand-offs from start to finish, treating the whole pipeline as one unit under test. A workflow strings together several stages: retrieve some data, call a model, parse the output, call a tool, then summarise. Workflow evaluation checks whether the full sequence produces the right final result, not whether any single stage looks correct on its own.

A real-world analogy

Think of a relay race. Each runner can be fast, but the team still loses if a baton hand-off is fumbled. Timing each runner in isolation tells you nothing about the drops between them. You only learn the truth by watching the whole race, baton passes included. Workflow evaluation watches the whole race. It scores the finish line and inspects every hand-off along the way.

Why per-step correctness is not enough

Small errors compound. Suppose each of five steps is 95% reliable on its own. If the errors are independent, the full chain succeeds only about 77% of the time (0.95 raised to the power of 5). Each step looks healthy in isolation, yet the pipeline fails one run in four. A wrong value produced early gets passed downstream, and later steps build confidently on the mistake. This is why a stack of high per-step scores can still hide a broken workflow.

Step 1 Retrieve Pull the source documents or records the task needs.
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Step 2 Reason The model interprets the input and decides what to do.
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Step 3 Act Call a tool, run a query, or transform the data.
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Step 4 Compose Assemble the final answer the user receives.

How it works

You run two complementary checks at once.

  • End-to-end scoring: feed a realistic input, let the whole chain run, and judge the final output against what a correct result should be. This is the number that matches what users actually experience.
  • Failure localisation: when an end-to-end run fails, inspect the intermediate outputs at each step to find where the chain first went wrong. A single end-to-end score tells you that something broke; it does not tell you where. Localisation traces the failure back to the offending step or hand-off.

Good workflow evaluation captures the trace: the input, every intermediate output, every tool call, and the final result. You build a test set of representative tasks, run the chain against each one, and track both the end-to-end pass rate and per-step diagnostics over time. Nondeterminism makes this harder, because sampling and variable tool responses can produce different traces for the same input, so you often run each case several times.

Workflow evaluation is one facet of AI evaluation , the parent discipline of measuring whether AI does what you need. It sits between two neighbours that people often confuse with it.

  • Agent evaluation focuses on a single autonomous agent that decides its own next step inside an agentic loop . A workflow, by contrast, is often a fixed, author-defined sequence of stages rather than a self-directed agent. Agent traces branch unpredictably; workflow traces usually follow a known path.
  • System evaluation is broader still. It covers the whole deployed product, including latency, cost, safety guardrails, and user outcomes, not only the correctness of one pipeline.

For a chain that ends in retrieval-augmented generation, pair workflow evaluation with RAG evaluation to score retrieval quality as its own step. Together these give you both the end-to-end verdict and the component detail behind it.

Further reading