What I learned building a long-lived AI agent (the boring version)
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Practical log of building a long-lived AI agent with caching, routing, and memory is directly applicable to agent orchestration.
Building a long-lived AI agent revealed that the real complexity lies not in the LLM call but in the surrounding machinery: routing, memory, caching, and context construction. A key insight was that prompt caching is highly sensitive to prefix stability; stabilizing the request structure yielded a 66% average cache hit rate, debunking claims of 90% token savings without showing the workload. Routing decisions must account for conversation state and intent, not just the current query, making agent architecture a systems engineering challenge.