# Mirror Node Reconnaissance > Source: > Published: 2026-09-22 06:03:33+00:00 SGAIL Labs · AI agent evaluation # We test AI where conventional benchmarks stop. 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. [What we test Rubric Catalog Real-world situations and the expert-authored criteria they are graded against — written by people who were there when it went wrong. Rubrics →](https://sgaillabs.com/rubrics/) [Where we test Simulation Raccoon Ridge, a simulated town with businesses, roles and consequences, so a decision happens somewhere instead of in a vacuum. Simulation →](https://sgaillabs.com/simulation/) [How we develop Training Matrix Controlled training where every change is tied back to an evaluation that can show whether it helped. Training →](https://sgaillabs.com/training/) [What we discover next Hive Mind A sensor on AI-to-AI interaction that flags new behaviours and failure modes as candidates for a rubric — reviewed by a person first. Hive Mind →](https://sgaillabs.com/hive-mind/) 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 1. Ask a question 2. Measure the answer 3. Repeat SGAIL evaluation 1. Give the AI a **situation** and an environment 2. Let it **act** 3. Inject incomplete, conflicting, misleading or changing information 4. Observe the **decisions** 5. Measure the **consequences** 6. Grade against an **expert-authored rubric** 7. Record new failure modes 8. 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. [Evaluate your AI](https://sgaillabs.com/evaluate/) ### 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.