Can You Prove Your AI Agent Works as Required?

We design the evaluations and harnesses that turn AI requirements into testable behavior and defensible evidence.

  • Evaluation criteria
  • Test and edge cases
  • Expected behavior
  • Evidence
Tell Us What You Need to Prove
The problem

A Good Answer Is Not Proof.

Your agent can give the right answer for the wrong reason.

It can pass the happy path and fail the edge case.

It can work today and regress tomorrow.

It can clear every case in your eval set while violating a requirement that nobody turned into a test.

The hard part is not running an eval.

The hard part is deciding what the eval must prove.

See the evaluation model
The core idea

Before You Evaluate the Agent, Define What Correct Means.

A reliable evaluation starts with the requirement.

  1. Requirement

    What should the agent actually do?

  2. Evaluation Criteria

    What counts as acceptable behavior?

  3. Test Cases

    What situations must it handle?

  4. Edge Cases

    Where could the requirement fail?

  5. Expected Behavior

    What does a correct result look like?

  6. Evaluation

    How did the agent perform?

  7. Evidence

    What can you actually demonstrate?

That is the evaluation layer between an AI system and a defensible claim that it works. It is also why an agent evaluation framework is worth more than a score.

Evaluation coverage
CriteriaTest casesEdge casesRequirement
Covered by the eval setNever turned into a test
What makes this different

Most Evals Start with the Output.We Start with the Requirement.

Nugalaxy turns intended behavior into something you can actually test.

We define what correct behavior means, then build the criteria, test cases, edge cases, and expected behavior needed to evaluate it.

The result is not just a score. It is evidence that the system behaved as required.

Explore the methodology
What we do

We Build the Evaluation Layer Around Your AI System.

The goal is not another score.

The goal is knowing what the score means.

How it works

From “We Think It Works” to “We Can Show Why.”

  1. 01 · Define

    Identify the requirement and intended behavior.

  2. 02 · Design

    Translate the requirement into evaluation criteria, test cases, and edge cases.

  3. 03 · Build the Harness

    Create the environment needed to exercise, observe, and measure the agent.

  4. 04 · Evaluate

    Run the system against meaningful cases and failure conditions.

  5. 05 · Capture Evidence

    Record what happened, what failed, and what the results demonstrate.

  6. 06 · Improve

    Use the evidence to strengthen the agent, the evaluation, or both.

Tell Us What You Need to Prove

Talk through the evaluation problem you are facing.

Who this is for

How Do You Know It Works?

If you are responsible for an AI system, sooner or later you have to answer that question in front of someone who did not build it.

  • AI Teams Building Agents

    You need evaluations that reflect the behavior your product actually requires.

  • Teams Deploying AI Into Real Workflows

    You need more than a successful demo before trusting the system with real users.

  • Governance / Risk / Assurance

    You need technical evidence behind claims about AI behavior.

  • AI Implementation & Advisory Firms

    You need a technical evaluation layer you can bring into client engagements.

Different teams ask the question for different reasons. The answer requires the same thing: evidence.

Proof

We Don’t Ask You to Trust a Claim.We Show You How We Think.

Field guide

How Do You Know Your AI Agent Actually Works?

A practical framework for designing meaningful agent evaluations.

Evaluation framework · DOI-backed

Read the guide
The dugalaxy planet logo
Open source

See the methodology in the open.

Dugalaxy

The evaluator designer behind the methodology.

A desktop application for the design half of AI evaluation: correctness, criteria, test cases, edge cases, and answer keys.

We kept doing this work by hand. Dugalaxy turns that design process into a repeatable workflow.

The library it grew out of is open source and on PyPI.

Explore Dugalaxy
The bigger picture

AI Trust Is Not a Promise.It Is an Evidence Problem.

You don’t make AI trustworthy by declaring that it is reliable.

You make it trustworthy by defining what should happen, testing what does happen, understanding the difference, and producing evidence you can stand behind.

Nugalaxy builds the engineering layer between AI requirements and evidence.

AI Requirements

What should happen

The engineering layer

  • Criteria
  • Cases
  • Harness
  • Evaluation

Nugalaxy

Evidence

What you can stand behind

Have an AI Agent You Are Not Completely Sure You Can Trust?

That is exactly the problem we want to look at. Tell us three things:

  • What your system is supposed to do.
  • How you evaluate it today.
  • Where you are uncertain.
Tell Us What You Need to Prove

Or write to info@nugalaxy.ai

No demo. No sales deck. Start with the system and the question you need answered.