An AI agent able to perform actions is only as useful as the data those actions rest on. An agent proposing a margin without knowing the customer's real history, or working out an hourly cost without access to the company's actual fixed costs, produces results that are plausible but not specific.
Read access to the right data
To be useful, an agent must be able to read — not guess — the relevant data: company parameters, product catalogue, commercial history. That access needs designing carefully, giving the agent exactly the data it needs, current at the moment of the request.
The risk of partial data
An agent with only partial access can produce incomplete answers without flagging it: it proposes a price based on a reference margin, unaware that this particular customer has a different history. Good system design makes sure the agent recognises and says when relevant information is missing.
Current data, not a snapshot
Business data moves: new customers, updated prices, revised margins. An agent working from an old snapshot makes silent errors. Data access has to be live, or as close to it as possible, if the agent is to stay reliable over time.