Imagine an AI agent reviews a request, reads company information, chooses an action and updates a system. The result looks right. The next question is more important: can your organization reconstruct the decision?
Start with six pieces of evidence
1. Input
What request, document, message or event started the process?
2. Context
Which documents, data sources, instructions and prior information did the system receive?
3. Model
Which model or service was used, and which version or configuration was active?
4. Rules
What policy, workflow logic, thresholds, permissions or business rules constrained the decision?
5. Human role
Was approval required? Did a person review, modify, reject or escalate the recommendation?
6. Action
What did the system actually change, send, approve, create or trigger — and when?
Explainability is bigger than the model.
A model may provide a rationale and still leave the organization unable to explain the complete process. A useful governance trail connects the model response to the information it received, the permissions it had, the workflow that surrounded it and the action that followed.
This becomes more important as systems move from answering questions to taking actions. A chatbot may produce a poor answer. An agent with credentials can create a record, send a message, update an ERP or initiate a transaction.
The human-in-the-loop question is not “human or AI?”
The better question is where human judgment is required. A person may need to review only high-value transactions, unusual cases, low-confidence outputs or actions with legal, financial, safety or reputational consequences.
That placement should be designed before deployment — not improvised after the first incident.
Evidence should survive model changes.
Providers update models. Organizations change prompts, policies, workflows and data sources. Good governance records enough information to understand which configuration produced an outcome at a particular point in time.
This is one reason an AI inventory, change management and monitoring become part of governance rather than separate administrative tasks.
A practical governance test
- Can we identify the AI systems and agents currently in use?
- Can we identify what information each one is allowed to access?
- Can we explain what it is allowed to recommend versus execute?
- Can we show where human approval or escalation occurs?
- Can we reconstruct a material decision after it happens?
- Can we detect when a model, prompt, workflow or provider changes?