AI Con USA 2026 - Agentic
Wednesday, June 10
AI-Native Delivery: Why the Future Belongs to Those Who Specify and Validate
AI in software delivery is no longer just about experimenting with copilots or generating code faster. It is about changing how software is imagined, specified, generated, validated, governed, and improved. As AI continues to be used for producing prototypes, requirements, code, tests, documentation, deployment artifacts, and operational insights, the software lifecycle shifts from a sequence of handoffs into a continuous learning loop. The real opportunity is therefore not only about leveraging AI tools, but adopting a new working model for transforming intent into trusted outcomes. Join...
Tracing the Mind of the Machine: Observability for AI Agents
AI agents have evolved beyond LLM chatbots; they possess the ability to plan, reason, and act autonomously. However, as their autonomy increases, understanding how they make decisions becomes more challenging. Traditional methods of observability—such as metrics, logs, and traces—capture outcomes but do not reveal the underlying reasoning. This session will explore how AI Agent Observability can shed light on the decision-making process by collecting and analyzing agent traces. We will discuss emerging standards like the Model Context Protocol (MCP), which provides structured and shareable...
The Context Economy: Getting More Bang for Your Token
Building effective agents requires fundamental changes to how engineers think about their work. LLMs that sit at the heart of agentic systems are stochastic by nature and operate in a world that requires optimal context to produce optimal results. While models offer bigger and bigger context windows, the cost and performance during inference is tied to the consistency of context delivery and the stability of that context over time. As tool calling and the Model Context Protocol (MCP) ecosystem play a larger role in agentic development, engineers need to invest in not only delivering the...