Agents are moving into real work
Organizations are giving AI agents access to customers, records and decisions. The question has shifted from "can it do this?" to "can we rely on it?"
ABOUT US
Independent thinking. Practical evaluation.
Test2.ai is an independent professional services practice focused on testing and evaluating AI agents against the work businesses need them to do. Test2.ai is operated by Test2 AI Inc.
Why Test2.ai exists
Demos aren't evidence
MIT research in 2025 reported that most enterprise generative AI pilots showed no measurable P&L impact. Too many decisions still rest on impressive demonstrations rather than measured results.
Evaluation is a discipline
Good evaluation needs clear criteria, representative tests, calibrated judges and honest reporting. It deserves dedicated expertise, independent of the tools and models being tested.
What guides our work
Business outcomes first
A benchmark is useful when it supports a real decision. We connect test coverage and findings to customer workflows and release criteria.
Evidence over impressions
We preserve inputs, expected results, actual behavior and configuration so findings can be understood and reproduced.
Customer-owned capability
Engagements leave your team with reusable evaluation assets and a clear maintenance process.
The right judge for the question
Code, AI judges and human experts each have strengths and limits. We combine them deliberately and measure their errors.
Honest limits
We state what was tested, what wasn't and how confident the conclusion is. We don't sell guarantees.
Independence
We have no platform to sell and no provider to favor. Recommendations follow the evidence.
How we work
A bounded scope
We agree objectives, responsibilities, inputs and deliverables before execution. Additional workflows are scoped explicitly.
A collaborative review
Engineering, product and domain experts help define success and resolve ambiguous cases. Their judgment calibrates ours.
A focused practice
Evaluation assessment is our primary service, supported by implementation and recurring evaluation engagements.
The broader context
Standards are still forming
Stanford's AI Index has noted the lack of standardized evaluations across the AI industry. Practical, transparent methods matter while standards mature.
Guidance we draw on
We use the NIST AI Risk Management Framework and published evaluation research as reference points, without claiming certification or compliance.
From pilots to workflows
McKinsey describes a gap between broad AI adoption and limited bottom-line results. Reliable agents in real workflows are how that gap closes, and evaluation is how you know you're there.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.