Architecting for AI: Building Production-Ready Systems with Context, Control, and Automated Testing

The explosion of AI capabilities, particularly with Large Language Models (LLMs), has created both incredible opportunities and significant architectural challenges for experienced software professionals. While conversational interfaces capture the headlines, realizing the true potential of AI requires building programmatic systems that can automate complex tasks and drive business value. This talk dives deep into the architectural patterns and techniques needed to move beyond the chatbot demo and build reliable, production-ready AI applications, specifically addressing the pitfalls of general knowledge LLMs. The key to unlocking LLMs' true potential lies in supplementing their core knowledge with specialized, context-specific information. We’ll explore strategies for enriching the LLM’s understanding before it engages with a prompt – a crucial step in ensuring accurate and reliable responses. This leads us to examine a range of knowledge integration architectures, including RAG, CAG, and KGR. We'll also look at the architecture of Chain-of-Thought (CoT) and how that can improve deep thought problems. We will also look at architecting different domains of knowledge and function through agentic AI. Critically, we’ll examine architectures designed to limit both input and output, enabling early detection and mitigation of hallucinations – a key concern for production AI systems. You'll learn how to enforce boundaries and control LLM behavior to ensure predictable and trustworthy results. Finally, we'll introduce a powerful technique: using AI to generate and validate test use cases, demonstrating a Monte Carlo sampling process for robust testing. This allows for efficient monitoring of progress and rapid identification of regressions, ensuring high-quality AI-powered solutions. This talk is designed for software architects, developers, and technical leaders seeking to effectively integrate LLMs into their applications. Attendees will gain a practical understanding of how to build scalable, testable, and reliable AI-powered solutions, ready to tackle real-world challenges. You'll leave with actionable strategies focused on delivering AI solutions with verifiable accuracy and controllability.