This is a step-by-step walkthrough for onboarding to OpenAI Codex. It covers installing the CLI and VS Code extension, configuring your workflow with agents.md and config.toml, and applying effective prompting patterns. The session also dives into advanced use cases, including integrating external tools via MCPs, running Codex programmatically in headless mode, and building multi-agent systems with the Agents SDK.
A deep dive into the security vulnerabilities of Multi-Context Protocol (MCP) for AI agents. The talk explores how identity loss, "all-or-nothing" permissions, and disappearing audit trails create significant attack surfaces, and presents solutions like identity chain tracking, context-aware permissions, and intelligent auditing to secure agent-to-tool communication.
Sam Partee, CTO of Arcade AI, explains that building production-grade agentic systems requires moving beyond simple chatbots. He details the critical components for creating reliable, secure, and scalable tools, including rigorous schema management, the principle of least privilege, continuous evaluation, and a crucial distinction between 'exploratory' and 'operational' tools.
John Roese, Dell's Global CTO, discusses the "agent-washing" phenomenon and how Dell achieved a $10 billion revenue boost while cutting costs through disciplined AI adoption. He introduces the concept of the "knowledge layer" as a crucial new component in AI architecture and details his 2026 predictions. These include a focus on governance, a four-part technical definition for agentic AI systems, the need for modern resiliency in AI factories, and the maturation of sovereign AI strategies.
Bhuvaneswari Subramani details the "Agentic Shift" in AI by presenting an evolutionary journey through seven foundational system design patterns. The talk progresses from simple conversational clients to sophisticated, multi-agent systems, covering key patterns like Retrieval-Augmented Generation (RAG), Self-Correcting RAG, and the Model Context Protocol (MCP), explaining how each pattern adds new layers of context, action, and autonomy.
Simba Khadder explains the evolution of feature stores and MLOps, detailing why they remain crucial in the age of LLMs for high-scale use cases. He discusses the acquisition of his company, Featureform, by Redis and outlines their new vision: building a "Context Engine" for AI. This engine aims to unify structured data, unstructured data, and memory into a single pane of glass, moving beyond simple RAG to a more sophisticated "Context Engineering 2.0" that empowers agents with rich, queryable context.