The data behind autonomous agents

An AI workflow asked to investigate an order may need to find the customer, check stock, read a policy, and update a record. Each step depends on information from a different place. If the customer has three identifiers or the policy is out of date, the workflow can carry the mistake into the next action.

Before adding more autonomy, examine those dependencies. A model that can interpret a request still needs reliable business context and limits on what it can change.

Give business terms a consistent meaning

Define the entities the workflow uses: customer, order, product, invoice, location. Map those definitions to the records in each system and decide which source takes precedence when they disagree.

A shared semantic layer can make these definitions available to applications. It should help the workflow retrieve the right record and interpret its fields. Permissions and action limits still need to be enforced by the surrounding software.

For example, a request to change an order should resolve to a specific customer and order ID. The application should check the user’s access and the order’s current state before allowing a change.

Fix the data path the use case needs

Start with the sources and updates required for the selected workflow. Establish who maintains them and how failures will be detected.

Documents need attention too. A search result should identify its source, version, and relevant access restrictions. Check whether the guidance is still in force before acting on it.

Test the workflow with missing fields, duplicate records, stale information, and conflicting instructions. Decide which cases should stop for review. Keep enough information about each run to investigate what happened later, within the applicable data-handling requirements.

Keep business controls in the application

Use the model for interpretation and selecting among permitted steps. Give it a small set of tools with structured inputs and defined actions.

The application should enforce permissions, approval requirements, calculations, and transaction checks. It should also handle retries without repeating a completed action. A second model may help review an output, but it is not a substitute for those controls.

Begin with limited actions and human review where the consequences of error warrant it. Expand only when testing and experience support the change.

Spend custom engineering where it matters

Assess existing infrastructure, search, model, and monitoring tools before building equivalents. Reserve custom engineering for the definitions, integrations, and process logic specific to the business.

The first use case does not need a platform for every possible future agent. It needs reliable access to the right information, a controlled set of actions, and a support team that can explain and correct failures. Reuse those parts when the next workflow needs them.