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ADG vs “AI Governance”

ADG is related to AI governance, but it is not the same thing.

What most “AI governance” focuses on​

Traditional AI governance typically focuses on:

  • ethical use
  • bias/fairness
  • model risk management
  • regulatory alignment
  • privacy and data handling
  • transparency and accountability at the model/decision layer

This is important—especially for AI systems making decisions about people.

What ADG focuses on​

ADG focuses on the engineering and operational reality of building software with agents:

  • codebase coherence under autonomous change
  • architectural intent preservation
  • tenancy and access boundary enforcement (e.g., RLS, permissions)
  • documentation synchronization as a governance primitive
  • environment isolation and deployment safety
  • auditability and replayability of agent actions
  • policy enforcement at merge/deploy gates
  • drift detection across code/schema/infra/docs

Why this distinction matters​

A company can have excellent AI ethics policies and still ship agentic software that:

  • silently leaks tenant data
  • creates brittle migrations
  • introduces environment coupling
  • bypasses security invariants
  • accumulates contradictory architecture over time

ADG addresses these failures directly.

The ADG difference (positioning statement)​

Most AI governance is about how AI systems should behave.

ADG is about how software must be built when AI systems are doing the building.