Semantic Caching in Agentic AI: Determining Cache Eligibility and Invalidation

<p>You've built the demo. It works. The LLM responds, the tools fire, the output looks great.</p> <p>Then you push it to production — and everything breaks.</p> <p>API calls fail

<p>Enterprise Retrieval-Augmented Generation (RAG) systems are under increasing pressure to deliver accurate answers with lower latency and sustainable operating costs. As

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<p>Claude's tool use (function calling) API is what separates toy chatbots from actual agents. I've built production agents with it — here's what reliable tool use looks like when

<p>If you're calling an LLM API for every single user request, you're almost certainly paying for the same question more than once. Not because your users are dumb because human

<p>Last month I ran a side-by-side test on an AI agent that processes about 4,000 requests a day. The agent has a long system prompt (roughly 2,800 tokens of rules, tool

<!-- SC_OFF --><div class="md"><p>I’m working on an LLM agent setup (using Qwen-style chat templates with tool calling), and I ran into a design trade-off that I’d like to get

<p>RAG is not dead. It just got promoted. For years, retrieval-augmented generation helped apps pull the right documents before an AI answered. Now, AI agents need something