Govern autonomous AI agents.
Keep agents under specific governance with their permissions, tools, oversight model and decision log captured, separate from the AI systems they sit on top of.
Why agents need their own governance
An agent is not the same kind of object as a passive AI system. A chatbot suggests a response. An agent reads customer messages, queries order systems, issues refunds within configured limits, and escalates to humans when uncertain. It takes actions on your organisation's behalf, which is a materially different thing from generating outputs for a human to act on.
The governance questions are also different. What is the agent's autonomy level. Which tools and systems can it interact with. What can it not do. How is it overseen. What gets escalated. What is the decision log of what it actually does. Aicura's AI Register holds agents as their own kind of entry, and the surfaces around it answer these questions in a way a plain list of systems was not designed to.
Register the agent with its specifics
The agent entry captures fields that do not apply to passive AI systems. Autonomy level on your organisation's scale, action scope (the tools and systems the agent can interact with), permission boundaries (what it cannot do), oversight model (which actions go through human review and which do not), escalation paths, decision logging requirements. The entry links to the underlying AI system it runs on, the business unit responsible, the vendor specification, the data flow.
Assess agent-specific risks
The risk items for an agent are different from a passive system's. Cascading action errors (one wrong action triggers another), tool misuse, prompt injection from input expanding the agent's scope, behavioural drift over time, escalation handling failures, customer impact of incorrect actions. You work through each, capture controls, and sign off the assessment.
Log every decision with reasoning and outcome
Every action the agent takes is logged with the prompt context, the agent's reasoning chain, the action taken, and the outcome. You upload a daily summary to Aicura, which anchors it externally so the record can be verified without taking your word for it.
Review the decision log on cadence
The customer service operations team (or whichever team owns the agent's domain) reviews decision logs on the cadence the agent's risk profile requires. Anomalies surface for closer look, including actions near the upper bounds of agent authority, escalations that were not handled cleanly, and any customer dissatisfaction following an agent action.
Evolve the agent through versioned changes
When the agent's scope is expanded, its prompt is updated, or a new tool is added, the change is versioned in the agent register entry. Material expansions go through the procurement workflow as a new adoption rather than an in-place edit. The agent's history of changes is captured so any question about behaviour at a point in time can be answered.
After the initial governance setup, the agent operates within defined and documented boundaries. Its actions are logged, reviewable, and anchored. Incidents involving the agent get captured and learned from. Expansions are governed the same way as any new AI adoption. If the agent's behaviour is ever questioned, the record is there, including what it did, why it did it, and what was changed afterwards.
Frequently asked questions
How is governing an AI agent different from governing other AI systems?
An agent takes actions on your organisation's behalf, which is materially different from generating outputs for a person to act on. The governance questions change with it: the agent's autonomy level, which tools and systems it can interact with, what it cannot do, how it is overseen and what its decision log shows. Aicura's AI Register captures those specifics in the agent's own entry, alongside the AI systems the agent sits on top of.
What should an agent register record?
The agent's autonomy level on your organisation's scale, its action scope, its permission boundaries, its oversight model, its escalation paths and its decision logging requirements. The entry links to the underlying AI system it runs on, the business unit responsible, the vendor specification and the data flow.
What happens when an agent's behaviour is questioned?
The record is there: what it did, why it did it and what was changed afterwards. Actions are logged, reviewable and anchored, incidents involving the agent are captured and learned from, and expansions of the agent's scope are governed the same way as any new AI adoption.
Start the work in Aicura
Aicura supports this work from the register that anchors it through to the documents and records it produces, with your people reviewing and approving everything along the way. It is guidance, not certification, audit or legal advice.