GoKawiilMost teams start building agents the same way they build any other web feature: wrap the model in a route handler, parse the request, and wait for the response.
This is fine for a demo. It's also the first thing to break in production.
Agents are long-running, stateful, and non-deterministic. They call tools, wait for external APIs, branch into subtasks, hit rate limits, and crash halfway through multi-step sequences. If your agent is tied to a single HTTP request, you've coupled your application's reliability to the wall-clock time of a non-deterministic loop.
Moving past the demo means breaking that coupling. This post walks through the three core patterns teams use to turn fragile agent scripts into scalable, resilient production systems.
The Nature of Agents
At the core of an agent is a loop: receive a goal, decide what to do next, call a model or tool, observe the result, update state, repeat until done. That loop has a few properties that make it hostile to naive web architectures.
Agents are long-running. A normal API request should finish quickly. Agents often don't, somtimes taking hours or even days to complete.
Agents are stateful. A run isn't one function call. It's a goal, a plan, tool calls and outputs, retries, errors, decisions, and a final output. If the process crashes, you need to know what already happened. If not, you either lose progress or rerun work blindly. Neither is great, especially for long-running agents.
Agents are non-deterministic. Traditional workflow code says:
Agent code says:
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