QCon AI Boston's Early Program Focuses on the Engineering Work Behind Production AI

"Getting a demo to work is one thing; building something that remains reliable, observable, explainable, and secure in production is another. As more teams move from AI pilots to production systems, the technical discussion is shifting with them, focusing on the engineering work needed to make these systems usable under real operating conditions."

"Context Engineering Over Prompting: prompts that ace demos often fail under real-world constraints such as latency and limited context windows, reframing AI as a systems design problem rather than a prompt-writing exercise. This represents a fundamental shift in how teams approach AI implementation."

"Agent Explainability addresses the need to inspect why an agent selected a specific tool. When tool calls are wrong and failures propagate downstream, teams need visibility into the decision path itself, not just output logs, ensuring trustworthy AI systems in production."

QCon AI Boston 2026 reflects a maturation in AI adoption, moving beyond proof-of-concept demonstrations to address production-grade engineering challenges. The conference program, curated by leaders from Red Hat AI, Doubleword, and Zoox, emphasizes practical implementation over novelty. Key themes include context engineering as a systems design problem rather than prompt optimization, agent explainability for understanding tool selection decisions, advancing beyond basic retrieval-augmented generation through knowledge graphs, and bridging performance gaps between offline testing and live deployment. These topics collectively address the central challenge: deploying AI systems that teams can reliably trust and maintain in production environments.

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