SGAIL Labs · AI agent evaluation
We build environments, rubrics and training systems for measuring how AI behaves when it has to actually do things: investigate a problem, decide with incomplete information, act, and sometimes recognise that it should stop.
Test AI by putting it somewhere it has to actually do something.
The platform
Four parts, one loop. #
Why this matters
A correct answer is not a correct decision. #
Static benchmarks measure what a model knows when it is asked. Deployed agents fail somewhere else: in the gap between knowing a fact and noticing that it applies, in information that arrives late or contradicts itself, and in the pressure to close the ticket. Conventional benchmark
- Ask a question
- Measure the answer
- Repeat
SGAIL evaluation
- Give the AI a situation and an environment
- Let it act
- Inject incomplete, conflicting, misleading or changing information
- Observe the decisions
- Measure the consequences
- Grade against an expert-authored rubric
- Record new failure modes
- Feed validated findings into controlled training
This is not a solved problem. What we build is the infrastructure and the method for this kind of evaluation — and the honest status of each piece is marked on the page that describes it.
How it works
The loop. #
Everything on this site sits somewhere on one cycle. A failure in the real world becomes a rubric; the rubric runs in the simulation; the run is evaluated and observed; what is learned goes into controlled training; the changed behaviour is tested again — and anything new it does becomes the next rubric.
Who uses it
Two kinds of team, one question: will it hold up? #
For AI developers
You are shipping an agent and want to know where it breaks before your users find out.
- Agent evaluation — scenario runs against situations your model has not seen
- Failure discovery — a written account of where it went, and where it should have
- Regression testing — re-runs draw fresh variations, so a fix has to be real
- Red teaming — adversarial pressure drawn from people who do it for a living
Starts with one free run. Paid audits and drill packs are priced on the evaluation page.
For enterprises & government
You are deploying, buying or regulating AI and need evidence of operational behaviour, not a leaderboard position.
- Operational AI assessment — evaluated against the disciplines your deployment touches
- Deployment readiness — does it stop, escalate and ask when it should
- Continuous evaluation — scheduled or per-build runs API in development
- Procurement evidence — results tied to a tamper-evident log by arrangement
Training
If a behavior matters, we should be able to test it. #
The Training Matrix is how evaluation results become development: controlled material, a hard wall between what trains and what tests, and every change measured against the evaluation that motivated it. Parts are built; the training runs themselves are under development — the page says which is which.
Research & infrastructure
What sits underneath. #
The evaluation platform stands on security, evidence, adversarial-testing and reasoning infrastructure built during earlier stages of the project — a firewall with a tamper-evident witness log, published adversarial-input detectors, chain-of-custody tooling — and on research that is still open.
For contributors
Know the work? Trade it in. #
The hard material comes from people who were there when it went wrong — electricians, plumbers, landlords, red teamers. If that is you, there is a door for it.
About SGAIL Labs
A security lab that kept finding the same gap. #
SGAIL Labs started on Oʻahu's North Shore building AI security infrastructure: firewalls, detectors and evidence logs. The failures that mattered kept turning out to be the ones nobody had written down — the kind a tradesperson recognises and a benchmark never asks about. The evaluation platform is what came of writing them down.