AI Agents Can Act, but Without Business Context They Can Still Be Wrong

The article says the enterprise AI debate has focused mainly on the models, asking which is smartest and which is most efficient. That focus made sense when AI was mostly an assistant, such as a copilot that finds and analyzes information or summarizes a dataset before handing the result to a person. But human review is not a perfect safeguard, the article says. People can defer to confident-sounding outputs or rubber-stamp recommendations, allowing a bad answer from an assistant to feed directly into a consequential decision.

Agentic AI raises the stakes further, according to the article. An agent can update customer records, approve requests, trigger workflows or make significant business decisions without a person standing between the model and every action. As that happens, the article says, enterprises have to ask questions beyond which model is best and start asking about the environments around those models. What data can the agent access? What rules govern its behavior? What is it allowed to change? And does it have enough context to understand what a sensible decision actually looks like? Those questions become more urgent when a system can turn a flawed answer into a real-life action, changing records, approving transactions or sending a process in the wrong direction before anyone notices.

The article argues that part of the solution is to give AI greater access to the business, even if that may sound counterintuitive. If an agent is going to act on an organization's behalf, it needs visibility into the organization's goals and rules, as well as the current state for the task at hand. That does not have to mean unrestricted access to everything, but it should mean the agent can see the right information in real time rather than waiting for its next training update. The article draws an important distinction: access should mean greater visibility and context, not necessarily greater authority. An agent may need to understand the customer, the transaction, the workflow, the rules around it and what has already happened, but that does not mean it should be free to change all of those things. The balance enterprises need to get right is broad context with narrow authority.

If agents need broad context to make good decisions, the article asks where that context actually comes from. It says operational context is becoming the differentiator. Many enterprises are still training AI agents in a piecemeal way, hoping they can feed them more and more company documents and that the agents will absorb the context required to run and manage parts of the business. The article says this approach can only get you so far. A document might tell an agent what a rule says, but in most businesses rules have exceptions. They are also regularly updated, meaning old documents can quickly become misleading. If AI is learning from outdated material, or from examples where those rules were applied differently, it can build the wrong understanding of how the business actually works.

The article proposes what it calls putting context at the core. That means placing AI within the core operational platform and grounding it in the systems where the enterprise already records commitments, applies rules and carries out transactions. Instead of working from fragments of the organization, the agent gets a fuller picture of what is happening and what should happen next. Rather than simply receiving a list of rules to follow, AI agents can see how those rules are actually being applied across the business. That gives them much greater context about which rules apply in particular situations, whether those rules are still current and whether the agent is actually allowed to act. The article adds that this does not mean every AI application has to live in the same place. Large organizations will always have a wider technology estate, with different applications and services working together. What matters more, it says, is having a trusted core that brings together the data, rules, permissions and history AI needs to understand how the organization actually works. That is a stronger starting point than having many isolated AI tools across the enterprise, each working from its own partial snapshot of the business.