"Can I return the blue jacket and use the store credit to order the same one in a large?"
The tester from Parts 2, 3, and 4 again. That one sentence touches four systems: the order lookup from Part 1, the return policy, the store credit balance, and the product catalog for the size swap. My agent had nine tools for exactly these jobs, and it still fumbled.
It picked the wrong tool twice. It searched products when it should have read the return policy. Then it quoted a policy line that belonged to a different category. And nothing in the logs said why. The system prompt had grown into a wall of rules: "use this tool when the shopper gives concrete criteria", "use that tool only for returns", "never confuse store credit with refunds". Every rule I added fixed one incident and made the next question slightly harder.
That is the wall every single-agent system hits. This part of the series is what I did about it. I stopped asking one agent to know everything, and turned it into a supervisor that hands work to specialist agents.
I am a Senior Software Engineer II at BS23 in Dhaka, and I have been building production AI agents with Spring Boot and Spring AI for over a year. Everything below comes from the same e-commerce agent as Parts 1 through 4: same project, same tools, same memory, same observability. The only change is the shape of the system.
