How Do You Know an AI Workflow Is Actually Working?
A working demo is not the same as a reliable AI system. Evals, traces, failure analysis and regression testing help reveal what happened inside a workflow, where it failed and whether a change actually improved it.
An AI workflow can produce a convincing answer and still have failed somewhere inside.
It may have retrieved the wrong document, selected the wrong tool, ignored an important field, repeated an action, or reached the right answer through an unreliable path.
That is why evaluating an AI system is not only about asking:
“Does the final answer look good?”
A better question is:
“Did the workflow behave correctly from input to outcome?”
A simple evaluation loop looks like this:
Input ↓ Workflow ↓ Trace ↓ Eval ↓ Failure analysis ↓ Improvement ↓ Regression test
1. What is an eval?
An eval is a structured test used to check how well an AI system performs.
For a simple AI feature, that may mean checking whether an answer is relevant or whether its output follows the expected format.
For a multi-step workflow, we can ask:
Did it complete the task? Did it retrieve the right information? Did it use the correct tool? Did the output follow the required schema? Did validation succeed? Did the workflow reach the expected final state?
The important part is defining what “good” means before measuring it.
2. Traditional tests and AI evals
Traditional software tests are often exact.
Given the same input, a deterministic function is normally expected to produce the same result.
AI behaviour is less rigid. Several different responses may all be acceptable.
So an AI eval can measure properties such as factual correctness, relevance, completeness, schema validity, task completion and correct tool use rather than expecting one exact sentence.
This does not replace ordinary testing.
A practical AI product usually needs both:
Traditional tests for deterministic software logic.
Evals for model and workflow behaviour that can vary.
3. What is a trace?
A trace is the recorded path the system followed during one execution.
For example:
User request ↓ Retrieval ↓ Model call ↓ Structured output ↓ Validation ↓ Tool call ↓ State update ↓ Final response
The final answer tells us what the user saw.
The trace tells us how the system got there.
Suppose an AI assistant gives a customer the wrong order information.
Maybe retrieval fetched the wrong order.
Maybe the correct data reached the model but was interpreted incorrectly.
Maybe the model made the right decision but the API call failed.
Without a trace, these can all look like:
“The AI gave a bad answer.”
With a trace, we can investigate where the failure actually occurred.
4. Failure analysis
Not every AI-system failure is a model failure.
Retrieval failure: The wrong or insufficient information reached the model.
Reasoning failure: The model received useful context but interpreted it incorrectly.
Schema failure: The model returned data in an unexpected structure.
Validation failure: Invalid information was allowed to continue through the workflow.
Tool failure: The intended action was correct, but an API or external service failed.
State failure: The workflow lost or incorrectly updated information between steps.
Each failure requires a different fix.
Changing a prompt will not repair a broken database query.
Replacing a model will not fix an API timeout.
Finding the actual weak layer leads to better engineering.
5. What should we measure?
There is no single metric that defines a good AI workflow.
Useful measurements may include:
Task success — Did the workflow complete the intended job?
Retrieval quality — Did it fetch the information needed for the task?
Tool selection — Did it choose the correct tool or API?
Structured-output validity — Did the result satisfy the expected schema?
Latency — How long did the full workflow take?
Retries — How often did the system need another attempt?
Human intervention — How often did someone need to correct or approve the workflow?
Cost — How much model and tool usage was required?
The useful metrics depend on the product and the job the system is expected to perform.
6. Regression testing
Suppose a workflow fails on a difficult case.
We diagnose the problem and fix it.
That case should not simply disappear.
It can become a regression test.
Later, if the model, prompt, retrieval logic, schema or workflow changes, we run the case again.
The question becomes:
“Did this change improve the system without breaking something that already worked?”
Over time, real failures become reusable test cases.
This gives the evaluation set something valuable: memory of the system’s past weaknesses.
7. From “it works” to “we can verify it”
Multi-step AI systems involve more than model responses.
They can include retrieval, validation, state, tools, APIs, databases and human checkpoints.
Reliability therefore depends on understanding the complete workflow.
Evals measure behaviour.
Traces show the path.
Failure analysis identifies the weak layer.
Regression tests help prevent old failures from returning.
Together, they move AI development from:
“It seems to work.”
toward:
“We can test how well it works, see where it fails, and check whether a change actually made it better.”
That is an important step between demonstrating an AI feature and engineering an AI system.