Human approval boundaries
Protected commitments — payments, cancellations, anything sensitive — stay behind explicit human approval and policy boundaries. No agent grants itself permission to cross that line.
Under the hood of a verified outcome.
Trelomar is built around one rule: an AI agent's claim is not the same as a verified business outcome. This is where we show our work.
A representative execution path
REQUEST→AUTHORIZE→EXECUTE→EVIDENCE→VERIFY→OUTCOME
Failures and exceptions are surfaced for repair or human escalation — not silently marked complete.
Evidence-backed execution
A task being reported complete is not treated as proof. Where Trelomar is connected to a real system, completion can require independent, downstream evidence before it is accepted.
Independent verification
Execution, challenge and verification can be separated across specialized AI roles, rather than a single model grading its own work.
Role-based access control
Every agent and integration operates under a defined permission boundary — what it can read, what it can write, and what always requires escalation.
Execution state & auditability
Every execution is tracked — status, duration, failures and cost — instead of operating as a black box you have to take on faith.
Failure handling
Failures and exceptions are surfaced for repair or human escalation. Nothing is silently marked complete when it isn't.
Duplicate prevention
Retries and repeated events are designed not to create duplicate business actions — the same request should not become two bookings or two charges.
Founder-controlled protected actions
The most sensitive classes of action are gated behind founder-level approval by design, not merely by convention.
Curious how this looks for your business?
See it applied to your industry, or start scoping a controlled pilot.