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An AI agent can complete a transaction correctly in technical terms and still take an action the business never authorized. A successful API call, a normal dashboard, or access inherited from an employee does not establish permission to issue a credit, change a contract, or alter another consequential record. The organization must define the agent’s business authority, set controls around it, and be able to show why an action was allowed.
Technical success is not business authorization
In a September 21, 2026 opinion article, CIO contributor Richard Ewing described an architecture review in which a customer-support agent issued an unapproved account credit while technical dashboards showed normal operation. That is Ewing’s reported example, not an independently investigated case study. Its point is that monitoring whether software ran as expected is different from deciding whether its action was permitted.
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An agent may have the technical ability to write a record because it uses a connected application, an employee’s permissions, or a service account. That access answers what the agent can do. A business policy must answer what it may do, under which conditions, and who is accountable for setting those conditions. As Ewing puts it, “The vendor can provide the software, but the enterprise still owns the business rules.”
Four questions to ask about an agent action
Did the system operate?
Operational health asks whether the application or agent completed its task, whether it encountered errors, and whether the connected service behaved normally. A green status indicator can answer this question without showing whether the action was appropriate.
Can the action be reconstructed?
Logs may show that a change occurred, but useful reconstruction also requires context: what request or record the agent saw, what decision basis it used, what rule applied, and what permissions it had at the time. Evidence that a transaction happened is not necessarily evidence that it was authorized.
Was the action permitted?
That requires an explicit policy boundary. For example, a support agent might be allowed to recommend a refund or issue one only below a defined limit and for specified reasons. The application’s technical permissions should not silently become the organization’s business policy.
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Who owns the result?
A named business owner should define and review the rules governing the agent’s work. Vendors can provide and secure software, but the organization still needs to decide which outcomes it accepts and how exceptions are handled. The sources support this governance distinction; they do not establish a universal rule about legal liability. Legal responsibility can depend on jurisdiction, contracts, sector rules, and the facts of a specific incident.
Match controls to the consequence
A tool that drafts a meeting summary does not create the same exposure as one that issues refunds, changes customer accounts, commits the company to contract terms, or updates sensitive records. Controls should reflect the possible harm, financial or contractual impact, and reversibility of each action—not apply an identical approval step to every agent.
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Gartner’s May 26, 2026 release argues that uniform governance across AI agents can lead to failure. Ewing likewise argues that requiring manual approval for everything can become unworkable at scale. These are governance arguments, not controlled evidence that a particular review design will succeed. A practical approach is risk-tiered review: let low-consequence tasks proceed within defined limits, while requiring stronger checks for high-impact or hard-to-reverse actions.
- Informational work: Reading, summarizing, or drafting without changing a live record generally calls for different boundaries from transactions or commitments.
- Limited write actions: Allow specific changes only within documented conditions, such as an approved reason code or financial threshold.
- High-consequence actions: Put a policy check before the record changes. Depending on the risk, that check may enforce a hard limit, route an exception for review, or require a separate authorized decision.
Human review is useful only when reviewers have enough context and capacity to make a real decision. A queue of approvals without the request, applicable policy, relevant record, and proposed action can become a rubber stamp rather than a control.
Build an authorization trail before deployment
The following sequence is a governance recommendation, not a universal legal mandate. It helps connect each agent’s technical reach to an accountable business rule.
- Inventory write-capable agents. Include vendor-supplied and internally built agents that can modify live records, contracts, customer accounts, or financial transactions—not only systems marketed as autonomous.
- Document actual access. Record what permissions the agent inherits from a user and any additional service-account or system access. Distinguish read access from the ability to create, approve, or change a record.
- Name the business owner. Assign a person or role responsible for the rules, exceptions, and periodic review of allowed actions. A technical administrator alone may not be positioned to decide the business policy.
- Classify actions by impact. Consider consequence, reversibility, financial or contractual effect, sensitive-data exposure, and whether a mistaken action can be corrected.
- Enforce policy before consequential changes. For high-impact actions, use checks that evaluate the relevant policy and limits before the system writes the record or makes a commitment.
- Keep evidence of context and authorization. Preserve what the agent acted on, the policy or threshold that applied, the permissions in effect, and whether an exception or separate approval was required. Where feasible, retain evidence independently of behavior the vendor can change.
- Review behavior and respond to incidents. Monitor deployed agents, investigate unexpected actions, and revisit permissions and rules when behavior, workflows, or connected systems change.
Monitoring helps, but does not settle the authorization question
Monitoring after deployment can reveal real-world behavior that testing missed. NIST’s March 6, 2026 publication, “Challenges to the monitoring of deployed AI systems”, says post-deployment monitoring is crucial to validating real-world reliability, identifying unforeseen outputs, and providing visibility into unexpected consequences. The same report says practices, validated methods, and shared terminology remain nascent and scattered. Monitoring matters, but NIST does not describe a settled method that by itself proves an action was authorized.
Keep operational alerts and authorization evidence distinct. A dashboard can tell a team that an agent made a change; a policy-linked record should help answer why the change was allowed. The organization also needs an incident path for actions that violate a rule, cause harm, or cannot be explained from available records.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST’s framework as structure, not a compliance guarantee
NIST’s Artificial Intelligence Risk Management Framework (AI RMF 1.0), published in January 2023, is voluntary, rights-preserving, non-sector-specific, and use-case agnostic. Its four functions—Govern, Map, Measure, and Manage—offer a way to organize risk work: establish responsibility, understand context, assess risks, and respond to them. It is a framework, not a certification, legal opinion, or guarantee that an agent is safe or compliant.
That distinction matters operationally. A framework can help leaders assign ownership and structure review, while monitoring methods and terminology continue to develop. Organizations still need controls suited to the particular agent, its access, and the consequence of its actions.
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What adoption forecasts do—and do not—show
Gartner’s August 26, 2025 forecast said 40% of enterprise applications would feature task-specific AI agents by the end of 2026, up from less than 5% at the time of the forecast. This was a forecast about the share of enterprise applications, not a measured result or a claim that 40% of companies would use agents. Adoption projections do not establish that organizations have resolved authorization, monitoring, or accountability.
Questions leaders should be able to answer
- Which agents can change business records, and which can only read, summarize, or draft?
- What user permissions, service accounts, or system credentials does each agent use?
- Which business leader owns the rules for each consequential action, and how often are those rules reviewed?
- How do controls change with financial impact, contractual commitment, data sensitivity, and reversibility?
- What happens when a vendor update changes agent behavior or the permissions it uses?
- Can the organization later reconstruct the request, context, applicable rule, permissions, and authorization behind a particular action?
If these questions have no clear answers, technical access has outpaced business governance. The priority is to define the agent’s authority and build evidence around it before relying on successful execution as proof of a sound decision.
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