How to Write Evals for AI Agents: A Practical Guide for Non-Engineers

Why AI Agents Fail in Ways Nobody Notices

The agent looks fine. It responds promptly, formats the output neatly, and never crashes. But the customer email it drafted was off-tone, the product description it generated had a hallucinated spec, and the support reply it sent didn’t actually answer the user’s question.

This is the silent failure problem. Writing evals for AI agents is how you solve it.

Evals — short for evaluations — are structured tests that encode your judgment about what “good” looks like into checks that run automatically. They’re not just for engineers. If you can write a rubric, you can write an eval. This guide walks through the mechanics: what evals are, when to run them, and how to write them without being a developer.

What Evals Actually Are

An eval is a test that measures whether an AI agent’s output meets a defined standard. Simple enough. The complication is that AI outputs are probabilistic and often subjective.

A traditional software test checks whether 2 + 2 = 4 — the answer is always the same. An eval for an AI agent might check whether a generated email is “professional in tone.” That requires judgment. Evals encode that judgment. They turn a human reviewer’s implicit criteria into explicit, repeatable checks.

How Evals Differ From Regular Software Tests

Software unit tests verify deterministic behavior: given input X, always produce output Y. AI evals are different in a few important ways:

They handle uncertainty. Because AI outputs vary, evals often use thresholds (e.g., “correct at least 90% of the time”) rather than exact matches.
They capture subjective quality. “Helpful,” “accurate,” and “appropriate” can all be operationalized into measurable criteria.
They run across many examples. A single eval pass isn’t meaningful. You need a dataset of test cases to get reliable signal.

The goal isn’t perfection — it’s consistent, measurable improvement over time.

The Three Moments When Evals Run

One of the most useful frameworks for thinking about evals is when they happen in the agent lifecycle. Understanding this changes how you design them.

Before the Agent Goes Live

Pre-deployment evals are your baseline check. Before your agent handles real users or real data, you run it against a test dataset and compare the outputs against your quality criteria.

This answers: “Does this agent do what I built it to do, consistently enough to trust it?”

Each test case has an input, an expected output (or the key qualities a good output should have), and criteria for judging success. You’re building a test suite — and running it before anything goes live.

During Active Use

Live evals, also called online evals, run while the agent is operating in the real world. Instead of reviewing every output manually, you sample a portion of live outputs and evaluate them — with automated checks, an LLM-as-judge, or periodic human review.

This answers: “Is the agent still performing well as conditions change?”

Real-world inputs are messier than test data. Users phrase things unexpectedly. Context drifts. Online evals catch degradation that pre-deployment testing misses.

After Something Goes Wrong

Post-hoc evals are retrospective. When a user complains, an output looks suspicious, or a metric spikes, you go back and evaluate what happened.

This answers: “Why did this fail, and how do I prevent it from happening again?”

Post-hoc analysis almost always reveals gaps in your test cases — edge cases or failure modes you hadn’t anticipated. Those gaps feed back into your pre-deployment suite, making it stronger over time.

A Step-by-Step Framework for Writing Evals

Here’s the practical process — the same whether you’re using a visual no-code tool or working with a developer.

Step 1: Define What “Good” Looks Like

Before you can test anything, you need a clear definition of success. This is harder than it sounds, because the definition has to be specific enough to be measurable.

Start by asking: “If a thoughtful person reviewed this output, what would make them say ‘yes, that’s right’ versus ‘no, that missed the mark’?”

Write that out in plain language. Don’t worry about turning it into code yet. For example:

“The response should answer the customer’s question without adding unrequested information.”
“The summary should include all three key points from the source document.”
“The tone should be direct, friendly, and jargon-free.”

These are the seeds of your eval criteria.

Step 2: Build Your Test Case Library

A test case is one example of what the agent will encounter in the real world. A good library covers four types:

Happy path cases — the typical, well-formed inputs your agent sees most often.

Edge cases — unusual inputs: very short prompts, very long documents, ambiguous questions, inputs in unexpected formats or languages.

Adversarial cases — inputs designed to trip the agent up. Trick questions, contradictory information, requests that are slightly out of scope.

Failure recovery cases — situations where the correct behavior is to say “I don’t know” or ask for clarification, not fabricate an answer.

Start with 20–30 test cases. That’s enough to get meaningful signal. For each one, write down: the input, the ideal output (or the key qualities a good output should have), and what would count as a failure.

Step 3: Choose Your Eval Method

Different outputs call for different evaluation approaches. Here are the main ones:

Exact match — The output must contain a specific string or value. Works well for structured outputs like JSON, classification labels, or numeric answers.

Contains check — The output must include certain keywords or facts. Useful when wording can vary but specific information must be present.

Format check — The output must follow a required structure (a bulleted list, valid JSON, a word-count range). Easy to automate.

Rubric scoring — A human reviewer rates the output on a scale (1–5) across defined dimensions. Captures nuance but doesn’t scale.

LLM-as-judge — A second AI model reviews the output and scores it based on your criteria. Faster than human review, scales well, and the method most teams use once they grow beyond manual spot-checking.

Reference comparison — The output is compared to a “golden” reference answer using semantic similarity, not exact wording.

For most non-engineers building practical AI agents, a combination of format checks (automated), rubric scoring (periodic human spot-check), and LLM-as-judge (for scale) covers the vast majority of cases.

Step 4: Write Your Eval Criteria

Eval criteria are the specific rules that define pass/fail for each dimension you’re testing. Good criteria are:

Specific — “The response is under 150 words” is better than “The response is concise.”
Binary or scaled — Either something passes or fails, or you define a scale with clear descriptors at each point.
Tied to user value — Criteria should reflect what actually matters to the end user or the business outcome.

Here’s an example of turning a vague goal into concrete eval criteria:

Vague goal: “The email should sound professional.”

Concrete criteria:

No slang or informal contractions (pass/fail)
Average sentence length under 25 words (pass/fail)
No spelling or grammar errors (pass/fail)
Tone is warm but formal — scored 1–5, where 5 = perfectly calibrated, 3 = acceptable, 1 = clearly off

You can write these criteria in a spreadsheet. No code required.

Step 5: Run, Review, Iterate

Evals aren’t one-and-done. The cycle is:

Run the agent against your test cases
Score the outputs using your criteria
Identify where it fails and why
Adjust the agent (prompt, context, model, workflow logic)
Re-run and compare

Track scores over time. A simple spreadsheet works. The goal is to see whether changes to the agent improve performance on the metrics that matter — and don’t break the ones that are already working.

This cycle also catches regressions: when fixing one problem accidentally breaks something else.

The Six Eval Dimensions Every Agent Needs

Different agents need different evals, but these six dimensions apply to almost every use case. Use them as your starting checklist.

1. Task Completion

Did the agent actually do what was asked?

This is the most fundamental eval. For a summarization agent: did it summarize the document? For a support agent: did it attempt to answer the user’s question? For a data extraction agent: did it return all the requested fields?

Task completion is usually binary — yes, it attempted the task; no, it responded with something irrelevant.

2. Accuracy

Is the information in the output correct?

This is harder to evaluate automatically because it requires comparing the output to a ground truth. Options:

For factual outputs, compare to a verified reference
For RAG (retrieval-augmented generation) agents, check whether claims are supported by the retrieved documents
For domain-specific tasks, use a subject matter expert for periodic spot-checks

Accuracy is arguably the most critical dimension for any agent that produces information users act on.

3. Format Compliance

Does the output follow the required structure?

If you need JSON, is it valid JSON? If you need a three-paragraph summary, did you get three paragraphs? If the output feeds into another system, format errors cause downstream failures.

Format checks are among the easiest to automate — they’re deterministic. Write them first.

4. Tone and Style

Does the output match your brand voice and the appropriate register for the context?

This is inherently subjective, which makes it a good candidate for rubric scoring or LLM-as-judge evals. Define your tone criteria explicitly so the criteria do the work, not your gut.

5. Safety and Policy Compliance

Does the output avoid content that’s harmful, inappropriate, or off-limits for your use case?

For consumer-facing agents, this means checking for toxic content, sensitive topic handling, and appropriate scope. For internal business agents, it might mean verifying the agent isn’t revealing confidential data or making claims it shouldn’t.

Safety evals often run on every output, not just a sample.

6. Faithfulness to Context

If the agent uses external data — retrieved documents, user-provided context, memory — does the output stay faithful to that data?

Hallucination is the specific failure mode here: the agent fabricates information rather than grounding its response in what it was actually given. Faithfulness evals check for this explicitly. For RAG-based agents in particular, evaluating faithfulness and answer relevance is an active area of research with practical frameworks you can adapt.

Common Eval Mistakes (and How to Fix Them)

Most teams writing their first evals make the same handful of mistakes.

Testing Only the Happy Path

It’s tempting to build test cases from inputs you expect. But agents fail on unexpected inputs — the edge cases, the adversarial prompts, the things users inevitably try that you didn’t anticipate.

Fix: Deliberately design for failure. What’s the weirdest input someone could send? What’s the most ambiguous phrasing of the task? Add those to your test suite.

Writing Vague Criteria

“The response should be helpful” isn’t a useful eval criterion. It’s not measurable, it’s not consistent across reviewers, and it’s not actionable when the agent fails.

Fix: For every criterion, ask: “Could someone who has never seen my agent apply this consistently?” If the answer is no, the criterion is too vague.

Trying to Test Everything at Once

A single eval check that covers ten criteria at once tells you nothing when it fails. You don’t know which criterion broke, or why.

Fix: One criterion per check. Bundle related criteria into a rubric with clearly defined sub-scores, so failures point to something specific.

Not Updating Evals Over Time

Your agent evolves. Your users’ needs evolve. Evals written at launch can become stale — testing for things that no longer matter and missing new failure modes.

Fix: Review your eval suite quarterly. Every time you catch a real-world failure, add a test case for it. This is how your evals get smarter over time.

Treating Eval Scores as Absolute Truth

An agent that scores 85% on your evals isn’t necessarily better than one at 75%. The score only means something relative to your criteria — and your criteria might be incomplete or measuring the wrong things.

Fix: Triangulate. Combine automated evals with periodic human review and real-world feedback signals (support tickets, user ratings, engagement data). Let each inform the others.

How MindStudio Makes Eval-Driven Building Accessible

Writing evals is conceptually straightforward. The friction is usually in execution — setting up infrastructure to run tests, collect results, and iterate quickly. For non-engineers, that friction is often the reason evals don’t happen in practice.

MindStudio’s visual agent builder removes that friction. You can build an AI agent, wire in eval logic, and iterate on both — without writing code.

Here’s what that looks like in practice:

Build a dedicated evaluator agent. Create a separate MindStudio agent whose job is to score outputs from your primary agent. Feed it your eval criteria as a system prompt, pass in the outputs you want evaluated, and have it return structured scores. This is the LLM-as-judge pattern, built visually.

Use conditional logic as format checks. MindStudio’s workflow builder lets you add conditional branches — essentially “if the output contains X, proceed; if not, route to the error handler.” These act as inline format evals, catching structural failures before output reaches the end user.

Log outputs automatically. MindStudio integrates with tools like Airtable, Google Sheets, and Notion without code. You can pipe agent outputs directly into a tracking spreadsheet, add a scoring column, and build your eval dataset over time without any custom plumbing. This connects naturally to broader AI workflow automation patterns that teams use to operationalize agents at scale.

Iterate quickly. The average MindStudio agent takes 15 minutes to an hour to build. When an eval reveals a problem, you can adjust the prompt, swap the model, or restructure the workflow — and re-run your tests immediately. The cycle time is short enough that eval-driven iteration actually happens in practice.

If you want to put these principles to work without writing code, you can try MindStudio free at mindstudio.ai.

Frequently Asked Questions

What’s the difference between an eval and a unit test?

A unit test checks for a specific, deterministic output: given input A, always expect output B. AI evals are designed for non-deterministic systems where the exact wording varies but the quality should stay consistent. Evals test whether outputs meet defined quality standards — not whether they’re identical to an expected string.

How many test cases do I need to start?

Twenty to thirty is a reasonable starting point for most agents. That’s enough to cover typical inputs, edge cases, and failure modes without becoming unmanageable. Grow the library over time by adding a new test case every time you catch a real-world failure.

Can I use AI to evaluate AI outputs?

Yes — this is the LLM-as-judge pattern, and it’s widely used. You give a second AI model your eval criteria and ask it to score the primary agent’s outputs. It scales well and agrees with human reviewers often enough to be genuinely useful. The caveat: the judge model has its own biases, so it works best when paired with periodic human spot-checks.

What’s an LLM-as-judge eval, exactly?

You write a prompt that says: “You are an expert evaluator. Here is the task the agent was given. Here is the agent’s output. Score this output on the following criteria: [your criteria]. Return a score from 1–5 for each, with a brief explanation.” Then you run that prompt through a capable model and collect the scores. OpenAI’s documentation on evals walks through this pattern in more depth if you want to explore further.

How often should I run evals?

Run your pre-deployment test suite every time you make a significant change — new prompt, new model, new workflow step. For live agents, spot-check a sample of outputs weekly or whenever volume increases significantly. After any user complaint or reported failure, run a retrospective eval to understand what went wrong.

What if my agent produces a different output every time?

That’s expected — and it’s exactly why eval criteria need to be about quality, not exact wording. Instead of “the output must say X,” your criterion is “the output must convey X accurately.” Run each test case multiple times (at least 3–5) and check whether all results meet your criteria. If some pass and some fail, that inconsistency is itself important signal — and a prompt engineering problem worth fixing.

Key Takeaways

Evals encode human judgment into structured, repeatable tests — they’re how you catch AI failures that look invisible from the outside.
Run evals at three stages: before deployment (set a baseline), during active use (catch drift), and after failures (learn and improve).
Start by defining what “good” looks like in plain language, then make those descriptions specific enough to be measurable.
Build a test case library covering happy paths, edge cases, and adversarial inputs. Twenty to thirty cases is enough to start.
Use a combination of format checks (automated), rubric scoring (periodic human review), and LLM-as-judge (for scale).
Cover the six key dimensions: task completion, accuracy, format compliance, tone and style, safety, and faithfulness to context.
Every real-world failure is a test case you didn’t have yet — add it to the library and keep iterating.

If you’re building AI agents and want to apply these principles without writing code, MindStudio gives you a visual environment to build agents, test outputs, and iterate quickly — all in one place.