The value is not in the model — it is the same one underneath — but in the harness, the guardrails, the evidence and the packaging. These are the arguments, strongest to weakest.
Every run produces a cryptographically signed audit trail, tied to a versioned procedure with validated frontmatter. An external evaluator (Common Criteria, national security schemes, fintech or healthtech audits) accepts it as a file annex. A chat session is not reproducible, not signed and references no procedure: the evaluator does not accept it.
A closed action vocabulary validated against a schema (everything else is rejected), protected paths in two layers, and hard budgets for actions, tokens and time. Exceeding them triggers an abort, never an infinite loop. You can ask a generic agent to respect this; TecnicaiT cannot violate it.
It runs the application the way real users do, through the browser and with roles coordinating (operator detects, command authorizes, admin audits). That catches defects code review never sees: the app works but the documented procedure is not executable, or the handoff between roles breaks.
The value is in validating every release, dozens of cycles, with a memory bank between iterations so it does not re-discover the same thing. It assigns the model to each task tier and reports the real cost per run. "Just ask an AI" does not scale to a catalog of procedures across releases across projects.
The user is a QA lead or an auditor: they open a web app, launch a run (or trigger it from a PR comment) and get findings with evidence or a mergeable PR with tests. No engineer needed to prompt and judge the result every session.
The product is measured by accepted PRs over proposed ones. It is a system with accountability, not an assistant handing over suggestions that someone has to review by hand.