AI agents are beginning to perform real organizational work, but giving an agent tools and a goal is not an operating model. Human-Agent Operating Model provides a practical framework for defining agent roles, permissions, autonomy, human handoffs, exceptions, supervision, accountability, and reauthorization.
A practical framework for designing AI decision authority, human judgment, evidence, review, escalation, override, and accountability.
An Autonomy Assignment Framework for choosing between AI assistants and AI agents, with criteria, a decision sequence, and a 30-day evaluation plan.
Compare prompt libraries and AI workflow systems across triggers, inputs, integrations, approvals, outputs, ownership, and reporting.
A framework using purpose, classification, minimum necessity, vendor terms, permissions, retention, and audit to control AI data access.
Assign AI governance tiers using data sensitivity, decision impact, autonomy, external exposure, and reversibility.
Learn where AI workflows need human review and how to assign full, conditional, sampled, or exception-only oversight.
A Review Pressure Score using business impact, confidence, reversibility, audience, regulation, and exceptions to set AI review models.
A Loop Placement Model using risk, reversibility, confidence, and impact to choose human-in-the-loop, on-the-loop, or out-of-the-loop AI controls.
An Autonomy Fit Score using frequency, judgment, exceptions, reversibility, confidence, and consequences to choose AI assistance or full automation.
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