Agents Makers

Outcome cluster — reduce risk

AI agents that drive Reduce Risk.

Reduce-risk deployments are the ones your CFO, General Counsel, and Head of Security sign. Compliance-critical work, audit-sensitive workflows, high-consequence decisions: SLA protection, fraud triage, vendor due diligence, access reviews, dispute resolution, regulatory disclosure. The wrong AI deployment makes the risk worse. The right one is the only cost-effective way to raise coverage without hiring a second reviewer for every sensitive step.

This is the outcome cluster where governance is not an afterthought — it's the entire value proposition. Per-turn enforcement of policy, full audit trail per action, human-on-exception routing with named owners, escalation rules signed at scoping. The roles below are built for that posture.

The operating model in Reduce Risk.

  • Policy enforcement, not policy suggestion

    High-criticality rules (regulatory thresholds, escalation triggers, safety constraints) are evaluated by the runtime on every iteration. They fire regardless of where the user is in the conversation. The role cannot 'forget' a policy — it's structurally enforced.

  • Audit trail per action

    Every action the role takes is logged with timestamp, input state, decision rationale, and output. The log is exportable in the format your auditor expects. There is no AI black box for compliance-critical work.

  • Human-on-exception, with named owner

    Every exception has a named human owner, set in scoping. The role queues exceptions with full context attached; the owner receives them with a defined SLA. No exception sits in a queue without an accountable party.

  • Policy changes reviewed in writing

    When a rule changes (regulatory update, new risk threshold, new escalation path), the policy is edited in natural language, reviewed by the responsible function, and deployed. Change log is automatic. Your auditor sees exactly who changed what and when.

How it rolls out

The playbook a real Operating Partner runs.

  1. Phase 1

    Map the risk surface

    Pick the workflow where a missed exception costs the most. Collections where missed escalations hit bad-debt, access reviews where a missed recertification hits SOX, dispute resolution where a missed SLA hits chargeback rate. Document the current policy + exception routing.

  2. Phase 2

    Author policy thresholds with the responsible function

    Your controller / DPO / GC / CISO sets every threshold and signs. Auto-action below threshold, queue above. The role does not move thresholds on its own.

  3. Phase 3

    Dry-run on closed periods

    Replay the last 60-90 days of work. Audit the role's decisions against what actually happened. Any discrepancy blocks go-live. This is the non-negotiable gate for any reduce-risk deployment.

  4. Phase 4

    Launch with daily review for week 1

    First week, the responsible function reviews every decision daily. Week 2+, weekly review of exceptions. Policy is tuned in writing. Audit log is exported end of week 1 as a dry-run of the monthly audit pattern.

  5. Phase 5

    90-day KPI review, audit trail export

    Read the contracted KPI (missed-exception rate, SLA breach rate, audit-cycle time). Export the full audit trail. If hit, scope the adjacent risk surface.

Reduce Risk works when governance is the first-class design consideration, not a report generated after the fact. Every role below is structured around policy-first, audit-always, named-owner escalation.

Every role scoped to this outcome

6 roles

90-day operational guarantee. We agree on the outcome KPI before launch. If we haven't hit it by day 90, we keep working free until we do.

How it works →

Pick a role. Start deployment.

Every role in this view is hireable, governed, and anchored to the fully-loaded cost of the equivalent hire.