Building Reliable Agents with the Transactional Outbox Pattern and Redis Streams

AI agents are pretty good at deciding what should happen next, given a well-defined business workflow. In the case of a customer support agent, for example, they can read a conversation, apply a policy, and return a response like "approve the refund" or "escalate this case." That part is exciting, and it is usually what gets demoed first. But the hard part starts right after the decision is made. In a real system, a decision only matters if the rest of the platform can trust it. If an agent decides a customer should get a refund, that decision still has to turn into real work across the rest of the application. The support case needs to be updated, and billing needs to issue the refund. The customer may need an email, and the CRM probably needs the updated status, too. If the app updates the case and then crashes before billing gets the event, you now have a case that says "refund approved" and a customer who never actually got refunded. That is the kind of bug that makes a system feel