Your data is protected by design — what reaches the LLM, what is stored where, who sees each part of your workflow.
I define these boundaries with you upfront, then enforce them on every run.
Built for enterprise trust.
Every implementation is designed around the controls you need before AI touches live work.
Access stays scoped. Agents reach your systems through bounded integrations, not open-ended data access.
They see only the records the workflow requires.
- Data permissions mapped to your business roles
- Boundaries inherited from your identity provider
Action Limit Policy
CRM — Update deal stage Deals under $25k value allow
Billing — Issue refund Up to $1k allow
HR — Read employee salary deny
Humans stay in control. High-risk decisions route through your approval chain first.
The agent prepares the action. Your team decides whether it executes, and nothing happens until human approvals clear.
01 Draft action auto
02 Risk classify auto
03 Awaiting your team's approval hold
04 Execute on approve
Every agent step is traced — inputs, reasoning, actions, escalations, and outcomes.
All logged so your team can inspect what happened and why.
- Replayable, queryable, exportable
10:42:07inputRefund request — $1,800
10:42:07reasonMatches refund policy 4.3 — Eligible
10:42:08tool callStripe — Issue refund
10:42:09escalateAmount over $1,500 — Routed to Operations
10:42:34approveMark Smith
10:42:34actionRefund issued
Agent behavior is evaluated continuously.
Regressions surface in testing, before they ever reach users.
Evaluation suite Coverage Result
Expected responses 98%
Edge cases 95%
Expert-reviewed set 99%
Re-run continuously, before & after launch
Failures have a path. Uncertain outputs, edge cases, and errors route to fallback paths instead of silently breaking.
- Confidence thresholds defined per action
- Edge cases go to the right human
- Errors trigger predefined recovery rules