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
A Good Answer Is Not Proof.
The hard part is not running an eval. The hard part is deciding what the eval must prove.
- 01
Your agent can give the right answer for the wrong reason.
- 02
It can pass the happy path and fail the edge case.
- 03
It can work today and regress tomorrow.
- 04
It can clear every case in your eval set while violating a requirement that nobody turned into a test.
Before You Evaluate the Agent, Define What Correct Means.
A reliable evaluation starts with the requirement.
Requirement
What should the agent actually do?
Evaluation Criteria
What counts as acceptable behavior?
Test Cases
What situations must it handle?
Edge Cases
Where could the requirement fail?
Expected Behavior
What does a correct result look like?
Evaluation
How did the agent perform?
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.
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 methodologyWe Build the Evaluation Layer Around Your AI System.
Evaluation Review
Review existing tests for blind spots in coverage and pass criteria. Turn real incidents into regression cases with clear conditions and expected behavior, so repeat failures are tested explicitly.
Explore Failure AutopsiesAI Agent Evaluation
Evaluate agent outputs, decisions, tool use, workflows, and failure conditions against the behavior that actually matters. This is where LLM evaluation stops being a score and starts being an answer.
Explore Evaluation CasesHarness Engineering
Build the surrounding structure that makes agent evals repeatable, observable, and useful throughout development.
Evidence
Turn evaluation results into evidence that can support engineering, governance, risk, compliance, and assurance decisions.
The goal is not another score.
The goal is knowing what the score means.
From “We Think It Works” to “We Can Show Why.”
01 · Define
Identify the requirement and intended behavior.
02 · Design
Translate the requirement into evaluation criteria, test cases, and edge cases.
03 · Build the Harness
Create the environment needed to exercise, observe, and measure the agent.
04 · Evaluate
Run the system against meaningful cases and failure conditions.
05 · Capture Evidence
Record what happened, what failed, and what the results demonstrate.
06 · Improve
Use the evidence to strengthen the agent, the evaluation, or both.
Talk through the evaluation problem you are facing.
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.
We Don’t Ask You to Trust a Claim.We Show You How We Think.
Evaluation Harness
The engineering layer around agent evaluation, in four decisions.
ReadAI Evals & Ownership
Evaluation as an organizational responsibility, not just an engineering one.
ReadHarness Engineering
The discipline underneath repeatable evaluation, written out in full.
Explore
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 DugalaxyAI 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.
Have an AI Agent You Are Not Completely Sure You Can Trust?
Choose the service you need and leave your email. We'll contact you to discuss your system and the question you need answered.
Or write to info@nugalaxy.ai