Operating Models for AI Agents
To scale AI agents safely, define role boundaries, decision rights, escalation pathways, logging, and objective-driven loops. Pair automation with human oversight where judgement matters, and monitor performance and drift continuously.
"Agents thrive inside well-defined operating models—clear objectives, boundaries, escalation, and measurement."
Design checklist
- Clear objectives and constraints for each agent.
- Human escalation triggers and approval gates.
- Audit logs and metrics for behavior, quality, and safety.
Governance
Establish ownership, review cadences, and incident handling. Include red-teaming and periodic revalidation of prompts and policies.
Start by mapping target workflows and decomposing tasks into agent steps with clear inputs, outputs, and guardrails. Then define escalation triggers—when and how humans intervene—and the evidence agents must record (logs, artifacts) for auditability.
Finally, measure agent performance with KPIs aligned to your business outcomes (cycle time, accuracy, exceptions handled, downstream impact). Review and refine policies regularly as usage grows and contexts change.
