What the Microservices Era Can Teach Us About AI

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The most expensive mistake teams are making with AI agents right now is treating them as microservices with a language model bolted on. The approach is understandable; microservices are the architecture most platform engineers know best, agents feel like services that happen to think, and the migration path looks familiar. But the analogy only holds long enough to lead you somewhere painful. Microservices changed how software gets built. The idea was to help teams work independently, ship faster and scale efficiently. In those days, shipping software meant one giant codebase compiled together, tested together and released on 12- or 18-month cycles. Then the cloud came along, and teams needed to ship in weeks or days. The only way to do it was to split systems apart and let each piece move on its own schedule. It worked, but the systems underneath stayed simple in one crucial way: they were deterministic. Distributed systems are hard, but the behavior was predictable. A service received a call and returned a result, and when it failed, it failed in milliseconds and threw an error you could see. That is the assumption agents break. An agent workflow can run for hours or days, touch a dozen systems, and make a non-deterministic decision at step three that you don’t discover was wrong until step forty. Nothing threw an error, nothing lit up red on a dashboard; the process just quietly went the wrong way and kept going. Where a broken microservice pages you, a misbehaving agent sends a confident, wrong result and moves on. This is why the microservices playbook both applies and …

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