Panos Stravopodis, CTO of Elyos AI, shares the infrastructure and orchestration challenges of building production-ready voice AI agents. He details the four pillars for success—latency, consistency, context, and recovery—and provides engineering patterns for error handling, context management, and achieving conversational coherence in real-time systems.
Robert from OpenAI discusses the critical role of structured evaluations (evals) and graders for developing advanced collaborative agents. He explores the limitations of 'vibe-based' assessments, introduces a maturity model for evals, and presents a comprehensive rubric for measuring agent performance beyond simple accuracy, connecting these concepts to the power of Reinforcement Fine-Tuning (RFT).
Rani Radhakrishnan, Principal at PwC, discusses the convergence of MLOps and IT operations, the practicalities of deploying AI agents, and the strategic considerations for scaling and sustaining AI initiatives in the enterprise. The conversation covers the shift from experimentation to ROI, the importance of human-in-the-loop processes, and the evolving skillsets required for future-ready IT.
A deep dive into orchestrating swarms of AI agents for large-scale code refactoring. Learn practical workflows, task decomposition strategies, and context-sharing patterns, demonstrated with a real-world case study on migrating a large codebase.
Matthew Glickman, CEO of Genesis Computing, discusses the rise of AI data agents designed to automate complex data engineering workflows. He covers the "last 10%" problem in enterprise AI, the unique value of targeting the data engineer persona, and how these agents can tackle challenges like legacy system migration and knowledge capture, ultimately giving valuable time back to data teams.
Learn how Vercel's open-source Workflows platform simplifies deploying durable, observable, and long-running AI agents by abstracting away the infrastructure complexities of queues, databases, and error handling.