Governing LLM and agent decisions
Short answer: Governing an LLM or an agent means treating its output as a decision: explain which context and tools produced it, verify whether it is reliable for that case, and set when it may run on its own and when a person must review it.
Definition
Generative models and agents do not only classify. They draft, retrieve context, call tools and, in some designs, execute actions. The governance problem is not “whether the model is good”. It is what the system is authorised to do with that output when the cost of being wrong falls on a customer, a regulator or a critical operation.
How it differs from a classic classifier
In a fraud or credit model the output is usually a score or a class. The explanation points to input factors. In an LLM the output is text or a tool call, and it depends on retrieved context that changes from one request to the next.
An agent adds a second jump: it does not only produce a result, it proposes or executes a step. Governing that step requires knowing which tool was used, with which arguments, and whether that action is inside policy.
What you need to be able to show
Which context was retrieved and whether it was appropriate for that request.
Why an action or a wording was chosen, not only that the model “answered”.
Whether the case may be automated or must escalate to a person, and who is recorded as accountable.
What this does not solve
It does not replace the model or the agent framework. It does not produce a vendor ranking or a maturity score. Behaviour on a specific model, corpus and tool is evaluated case by case.
In the Azuba model this belongs to Explainable AI and Intelligent Decision, under generative AI assurance, governance and decision intelligence. The organization can keep building LLMs, RAG and agents; in that solution, AyGLOO covers the layer that verifies and governs each output.
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Is a content filter on the LLM enough?
A filter can block unacceptable outputs. It does not explain why an answer was produced, it does not verify whether the retrieved context was appropriate, and it does not decide whether an agent action may run on its own.
Do we have to redesign the AI stack in order to govern agents?
Not necessarily. Governance sits between the model or agent output and the action. The model, retrieval and tools can stay; what is missing is the layer that explains, verifies and authorises.