Direct answer
A governance framework that works is made of enforced mechanisms rather than policy documents. It defines risk tiers that decide who may approve what, permissions enforced by the systems being called, an append-only record of every decision, and a named owner able to pause the system. Each part can be demonstrated by attempting a violation.
Enterprise AI governance
The set of enforced controls and named accountabilities that make an AI system's consequential actions attributable and reviewable. It covers who may approve which actions, how access is restricted, what is recorded, and who operates the system after release.
The four parts that carry the weight
A framework is judged by what it refuses, not by what it declares.
Risk tiers
A classification of actions by consequence, deciding which need approval and from whom.
Enforced access
Permissions applied by the systems being called, so an out-of-scope request is refused underneath the agent.
An append-only record
Actor, reason, evidence, artifact and version per run, written where it cannot be quietly revised.
Named ownership
A business approver, an engineering owner, and someone with standing to pause the system.
Why documents alone fail an audit
A policy stating that agents must not access restricted data is a claim. A permission check that refuses the request is evidence, and it can be demonstrated in a meeting. Where the two disagree, the mechanism is what holds, so writing the policy after the mechanism tends to produce a document you can actually stand behind.
Non-determinism is not the obstacle
Most frameworks require that risk is assessed, access controlled, decisions owned and events reconstructable — not that a system produces identical output twice. Human review processes are non-deterministic and remain auditable for exactly that reason. Put the determinism in the controls and the question resolves.
The Propose–Decide–Execute pattern
CodeDTX builds agentic systems on the Propose–Decide–Execute pattern: agents may only write proposals with evidence attached, a named human records an approval or rejection with a reason, and a separate execution layer carries out approved work and logs the artifact. No agent holds a write tool to the outside world.
- 01
Propose
The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.
- 02
Decide
A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.
- 03
Execute
A separate execution layer performs approved work — merge, publish, call, write — and records the resulting artifact.
- 04
Audit
Actor, reason, evidence, artifact, tokens, and cost are retained for every run, so any decision can be reconstructed later.
The six layers we build and review against
- 1
Agent layer
Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.
- 2
Integration layer
MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.
- 3
Knowledge layer
Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.
- 4
Reliability layer
Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.
- 5
Safety layer
Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.
- 6
Product layer
The application people actually use: interfaces, approval queues, and operational runbooks.
Frequently asked
Where should a governance framework start?
With risk tiers, because everything else follows from them. Once actions are classified by consequence, it becomes obvious which need approval, which need tighter access, and which can proceed unattended.
Who owns AI governance internally?
In practice it is shared: the business owns approval of outcomes, engineering owns the enforced controls, and risk or compliance owns the standard being met. The failure mode is ambiguity, where each assumes another is holding it.
Does this slow delivery down?
Early on it adds design work, mostly in defining tiers and the record. After that it tends to speed delivery, because the question of whether a given capability may ship has a documented answer instead of becoming a debate each time.
Have a workflow that should become AI-enabled?
Tell us about the system it lives in. We reply from an engineering seat, not a sales deck.