Framework
A formal operating model for governing AI systems and autonomous agents in environments where speed, assurance, and accountability must coexist.
Five-layer architecture
Each layer produces a concrete governance artifact. The stack is designed in sequence because evidence, controls, and velocity are ineffective when authority is undefined.
Define who may decide, recommend, approve, delegate, or act—human or agent—and under what consequence threshold.
Capture the record of consequential AI-supported action: data used, model or agent involved, control outcome, human checkpoint, and decision basis.
Scale controls to autonomy and consequence so low-risk assistants are not governed like high-consequence actors, and high-risk workflows receive sufficient oversight.
Move people to the decisions where ambiguity, consequence, ethics, legality, or public trust require accountable human judgment.
Measure whether governance reduces time-to-trusted-decision while lowering ungoverned action and evidence gaps.
Autonomy-tiered governance
Proportional governance distinguishes between support tools, recommendation systems, bounded action agents, and high-consequence autonomous workflows.
| Tier | Agent authority | Representative use | Governance requirement |
|---|---|---|---|
| T0 · Observe | No action or recommendation authority | Summarization, classification, discovery | Inventory, data controls, user notice, retention rules |
| T1 · Assist | Drafts or retrieves information for human use | Draft memos, knowledge search, policy lookup | Human review, source traceability, output labeling |
| T2 · Recommend | Recommends a decision or ranked option | Prioritization, triage, case routing | Decision owner, bias/error monitoring, evidence log |
| T3 · Bounded action | Acts within approved parameters | Workflow updates, ticket closure, notifications | Permitted-action register, identity boundary, rollback path |
| T4 · Supervised autonomous | Chains steps across systems under supervision | Multi-system process automation | Continuous monitoring, escalation triggers, exception board |
| T5 · High consequence | Material rights, safety, finance, legal, personnel, or public-trust impact | Credit, hiring, access, benefits, enforcement, security-sensitive action | Human accountable authority, documented basis, pre-defined appeal or review path |
Implementation roadmap
The first 90 days should produce a defensible governed pilot, not an indefinite committee process.
Inventory AI and agent use. Classify autonomy and consequence. Baseline data governance, ownership, control evidence, and stakeholder risk.
Stand up decision rights, evidence schema, proportional controls, accountable ownership, and the governance operating rhythm.
Pilot governed agents on one high-value workflow. Prove controls, measure decision velocity, and expand by adoption rather than decree.
Measurement discipline
A formal program measures coverage, auditability, risk reduction, adoption, and speed. Governance without measures becomes policy theater; measured governance becomes operating capability.
Percentage of AI systems and agents inventoried, classified, and assigned to control owners.
Percentage of consequential decisions with complete evidence trails and named accountable authority.
Reduction in ungoverned action, over-permissioned agents, missing human checkpoints, and exception recurrence.
Decision cycle time compared against the pre-governance baseline for governed workflows.
