Prompt Builder Workflows Workflow Advanced

AI Agent Evaluation Workflow

Find out whether an AI agent behaves before users do — define what correct means, build test scenarios with expected outputs, catch failures and hallucinations, then regression-test each version.

The problem

An AI agent that looks impressive in a demo is not an agent you can trust in production — the demo never tried the inputs that break it. Agents fail in ways code doesn't: they're non-deterministic, they hallucinate, and a prompt tweak that fixes one case silently breaks three others. 'It worked when I tried it' is not evaluation. Knowing whether an agent behaves means deciding what correct looks like, testing it against real scenarios with expected outputs, catching the failures and inventions, and re-running the whole set every time you change something — so improvement is measured, not hoped for.

Recommended workflow

Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.

  1. Define what correct behavior means

    Before testing anything, decide what the agent must and must not do — the behaviors that count as success, the ones that count as failure. Anchor the model in a QA mindset so evaluation is measured against criteria, not vibes.

    Goal The behaviors that count as correct, defined as evaluation criteria.

    Resource QA Engineer Role Prompt
    Tool Role Prompt Generator
    Open this step in Role Prompt Generator

  2. Build test scenarios with expected outputs

    Turn the criteria into concrete scenarios — the normal cases, the edge cases, the adversarial inputs — each paired with the output a correct agent should produce. A test set with expected outputs is what makes a pass or fail meaningful.

    Goal A scenario set, each with the expected correct output.

    Resource Agent Test Scenario Prompt
    Tool Test Case Prompt Generator
    Open this step in Test Case Prompt Generator

  3. Run the eval and catch failures and hallucinations

    Run the agent against the scenarios and check each result against its expected output — flagging the wrong answers, the made-up facts, and the cases where the agent should have refused but didn't. Failure analysis is the point, not a green count.

    Goal Failures and hallucinations identified against expected outputs.

    Resource Hallucination Detection Prompt
    Tool AI Output Validator
    Open this step in AI Output Validator

  4. Regression-test every version

    Every time you change the prompt, the tools, or the model, re-run the scenario set and diff the results against the last known-good run — so an improvement to one case can't silently regress another.

    Goal A regression check that catches behavior drift between agent versions.

    Resource Agent Regression Test Prompt
    Tool AI Text Diff Checker
    Open this step in AI Text Diff Checker

Expected outcome

An agent you can trust because you tested it — correct behavior defined, a scenario set with expected outputs, failures and hallucinations caught, and a regression check that re-runs on every change — so you ship on evidence the agent behaves, not a demo that happened to work.

Best for

  • Verifying an AI agent behaves before shipping it
  • Catching hallucinations and failure cases an agent demo missed
  • Regression-testing an agent across prompt or model changes

Not for

  • Building the agent's instructions in the first place — use the AI Agent Instruction Workflow
  • Testing deterministic application code — use the AI Test Generation Workflow
  • Reviewing source code for correctness — use the AI Code Review Workflow

FAQ

How is this different from the AI Test Generation Workflow?

Test generation builds tests for deterministic code — same input, same output, pass or fail. This evaluates a non-deterministic agent: scenarios with expected behavior, hallucination detection, and regression across versions. Code tests assert exact returns; agent evaluation judges behavior against criteria.

How is this different from the AI Agent Instruction Workflow?

Instruction builds the agent — its role, system prompt, and step plan. This checks whether the agent it produced actually behaves. You'd instruct the agent first, then evaluate it, then loop back to fix what the evaluation exposed.

Can the AI fully judge its own agent?

It can run scenarios, flag mismatches against expected outputs, and surface likely hallucinations — but the criteria for what counts as correct, and the call on borderline cases, stay yours. The workflow makes evaluation systematic; it doesn't make it fully automatic.

Part of these blueprints

Complete build journeys that include this workflow as a stage.

Blueprint Build an AI Support Agent with AI The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live. View Blueprint → Blueprint Build a RAG System with AI The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works. View Blueprint → Blueprint Build an AI Workflow Automation System with AI The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy. View Blueprint → Blueprint Build a Customer Support System with AI The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy. View Blueprint →

Where to go next

Recommended next workflow AI Cost Optimization Workflow Cut what an AI feature costs without dumbing it down — price the prompt as it runs today, see where the tokens go, trim the waste, and re-measure to prove the saving holds at scale. Use when An AI feature's token bill is higher than it should be and you want to cut it without losing quality. Start this workflow

Related workflows

Prompt Builder Workflows AI Agent Instruction Workflow Instruct an AI agent that runs on its own without it wandering off — anchor it to a role, write the agent system prompt, then lay out the multi-step plan it works through. Use when You're building an autonomous or multi-step AI agent and need it to follow a reliable plan, not improvise. View Playbook → Prompt Builder Workflows AI System Prompt Design Workflow Design a system prompt that holds up in production — define the role precisely, engineer the behavior and guardrails on top of it, then check it reads clearly before you ship. Use when You're writing a system prompt for an assistant and want it reliable and clear, not a first draft you hope works. View Playbook → Coding Workflows AI Test Generation Workflow Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps. Use when A file or module has little or no test coverage and you want tests that catch real bugs. View Playbook →

Tip: Each step's resource opens its tool pre-filled — start at step one and carry the output forward.

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