Have you ever struggled to make an LLM perform the task that you intend? Providing more raw context is still only a stop gap. Even though new releases of models such as Gemini support context lengths of up to 1M tokens, Retrieval Augmented Generation (RAG) is brittle to the retrieval mechanism and often fails to reconcile between multiple sources of context. The same failure points are amplified when introducing Autonomous Agents due to compounding effects of chaining multiple tasks together. Learn how to build a reliable production AI Agent System and apply it to high stake industries such as financial services and healthcare. We will deep dive into sentiment analysis as an unexpected and useful showcase of how you can go from a naive system to production level performance through a composable AI system to manage an agent’s memory, connect it to the higher-level concept of sentiment, and utilize reasoning traces for continual performance enhancement.