By Gadiel Guadarrama · September 24, 2026
An agent’s authority should be designed as carefully as its intelligence.
An analytical assistant that describes a performance change and an agent that changes a business system carry different responsibilities. The interface may look similar, but the consequences do not. Enterprise AI strategy needs to make that boundary visible before expanding access or autonomy.
Three questions before deployment
Is the answer supported? A correct calculation does not automatically justify its explanation. A sales increase during a promotion may be observable; attributing the increase to the promotion requires a stronger design. Evaluation should distinguish numerical correctness, factual support and causal claims.
Is the action authorised? A model’s ability to suggest an action is different from a user’s permission to execute it. The application needs an explicit policy for access, approval and the scope of available tools.
Can the organisation detect and recover from failure? Decide who reviews exceptions, which evidence is retained and what happens when a source becomes unavailable or an answer cannot be substantiated.
Where AI security enters the decision system
The OWASP 2025 Top 10 for LLM applications identifies risks including prompt injection, sensitive information disclosure and excessive agency. For a business leader, these are useful prompts for examining how a proposed workflow crosses trust boundaries.
Consider a hypothetical assistant that reads supplier documents and recommends an approval. The document is evidence to inspect, not authority to change the workflow. Its contents should not grant new permissions or bypass a required reviewer. This is a design question for the whole application, not simply a request to improve its prompt.
Governance needs an owner
The NIST AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, development, use and evaluation. It provides a broader risk-management reference; using it does not by itself establish certification or compliance.
My practical starting point is a short decision record for the proposed workflow: the business objective, accountable owner, allowed actions, evidence requirements, evaluation criteria and escalation route. That record makes disagreements visible while changes are still relatively inexpensive.
Start with bounded usefulness
A first release might help a team inspect a governed metric, compare alternatives and prepare a recommendation for review. Evaluate whether it improves the actual decision process: can users understand the evidence, spot limitations and identify when further analysis is needed?
Set a baseline and test against realistic failure cases before expanding responsibility. Include ambiguous questions and incomplete evidence, not only examples with clean answers. The desired behaviour may be to clarify, qualify or stop.
For enterprise adoption, AI safety, analytical reliability and security should meet at the workflow where business consequences occur. That is where an abstract governance principle becomes an operational choice.