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Most AI projects don’t fail because the model is weak.

They fail because nobody can get the right context into the workflow.

The data is in three systems. The permissions are unclear. The customer record exists in six slightly different versions.

And then everyone is surprised when the agent gives an answer that sounds confident but is completely wrong.

I’ve been testing what changes when an AI agent can work with the systems a team already uses.

Not a demo database.

Not a clean spreadsheet someone prepared for the presentation.

The actual CRM. The support tickets. The product docs. The approval history.

That feels less like asking AI a question, and more like giving a new team member the information they need to do the job.

But access is only half the problem.

The agent also needs to know what it is allowed to do.

A customer service agent might be able to read an account history.

That doesn’t mean it should be able to change a refund status, export a customer list, and send an email without anyone checking the decision.

This is where the workflow starts to matter.

The best systems I’ve seen don’t treat the agent as a magic answer box. They give it a defined role, the relevant context, and a clear boundary around what happens next.