Solo developer shares hard-won lessons building a production-grade AI agent

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A self-taught developer with a civil engineering background built a personal AI assistant over time, expanding it into a complex system with memory layers, routing logic, caching, and quality monitoring. Through hands-on experimentation, they discovered that most failures stemmed not from the AI model itself but from the surrounding infrastructure handling sessions, context, and tool calls. They found that prompt cache hit rates depend heavily on request structure stability rather than model capability, achieving around 66–80% cache hits only after standardising prefixes. Routing requests to cheaper models also proved unreliable in multi-turn conversations, where full context and memory are needed to interpret follow-up messages correctly. The developer concludes that real-world agent performance is shaped by workload design and system architecture, not by benchmark results.

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