Moving an AI agent from proof of concept into production is not a deployment task. It is a governance task. The agent must be inventoried, risk-assessed, owned, bounded and authorised before it is allowed to take actions in a live environment. Skip these steps and you have an autonomous system making consequential decisions with no accountability.
The first step is to identify the use case and intended actions. Write down what the agent is for, who it serves, and the concrete actions it may attempt. If you cannot list the actions, you cannot bound them. This list becomes the foundation for authority boundaries later.
Next, map the systems and data the agent will access. Which enterprise systems will it reach? Which tools will it invoke? What data will it read or process? This mapping is not optional. An agent that can access customer PII and raise purchase orders has a very different risk profile to one that summarises internal documents.
Assign a named owner. An AI agent should never create its own authority. Authority must originate from the institution, and a named human must be accountable for the agent and its decisions. Without ownership, no one is responsible when something goes wrong.
Assess the risk. Classify the use case against your risk tiers and applicable regulatory frameworks (EU AI Act, ISO/IEC 42001). High-risk use cases trigger specific obligations: risk management systems, data governance, technical documentation, human oversight and logging.
Define authority boundaries. What is the agent permitted to do? What is it prohibited from doing? What financial thresholds apply? Which actions need human approval? What geographic or contextual restrictions apply? These boundaries are the difference between an agent that is governed and one that is not.
Configure governance in ServiceNow. Register the agent in AI Control Tower, record its risk classification, assign ownership, and configure approval workflows. The governance record is the system of record for the agent's lifecycle.
Configure runtime authority in VORTIQ-X. The authority boundaries you defined become enforceable rules. Each protected action is checked against the boundaries before it executes. If an action exceeds the agent's authority, it is held for human approval, transformed to a permitted alternative, or denied.
Test both allowed and denied paths. Verify that permitted actions execute correctly and that prohibited actions are blocked. The most common failure in AI agent productionisation is testing only the happy path and assuming the controls work.
Release, continuously observe, and preserve evidence. Every authority decision and resulting action is recorded back into the ServiceNow governance record. This evidence is what auditors and regulators will ask to see.
Karvin follows this 13-step lifecycle for every AI agent we move into production. If you are productionising an AI use case, our AI Authority Readiness Assessment evaluates whether it is ready to be given operational authority.
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