Stop Paying the "Latency Tax": A Developer's Guide to Prompt Caching

Imagine you're a researcher tasked with writing a 50-page report on a 500-page legal document. Now, imagine that every time you want to write a single new sentence, you're forced to re-read the entire 500-page document from scratch. Sounds exhausting, right? It’s a massive waste of time and cognitive energy. Yet, this is exactly what we’ve been asking our AI agents to do. Until now. The "Latency Tax" of the Agentic Loop The shift from simple chatbots to autonomous AI agents is a game-changer. While a chatbot waits for a prompt, an agent proactively reasons, selects tools, and executes multi-step workflows. But this autonomy comes with a hidden cost: the latency tax. In a traditional "stateless" architecture, every time an agent takes a step, searching a database, calling an API, or reflecting on its own output, it sends the entire context back to the model. This includes: Thousands of tokens of system instructions. Complex tool definitions. A growing history of previous actions. The LL