Agentic AIJanuary 26, 202610 min read

Building a Governance Framework for AI Agents

As agent autonomy increases, governance becomes an architecture requirement, not a compliance afterthought.

Define ownership by workflow, not by model

Governance succeeds when each automated workflow has a named business owner, risk owner, and technical owner. Model teams alone cannot own production impact.

Standardize decision boundaries

Every agent should have explicit policies for what it can decide, what requires approval, and what must be blocked. This policy layer must be auditable and versioned.

  • Policy registry mapped to workflow risk level
  • Human approval checkpoints for irreversible actions
  • Exception tracking for policy and quality breaches

Build observability into governance

Logs should capture prompts, tool calls, decisions, and overrides. Governance reviews become productive when teams can trace behavior from request to action.

Enterprises that treat governance as a product capability ship faster because risk teams and delivery teams work from the same control model.

Design governance before scale

Kyper helps organizations implement policy-driven AI operations with measurable controls.