A deep dive into the evolution from static chatbots to dynamic, agentic AI systems. Philipp Schmid of Google DeepMind explores how to design, build, and evaluate AI agents that leverage structured outputs, function calling, and workflow orchestration with Google Gemini, covering key agentic patterns and the future of AI development.
A talk on designing a dependable AI execution layer that handles thousands of operations without constant supervision. The system is built to be precise, responsible, and action-oriented, avoiding common LLM pitfalls like burnout, memory bloat, and overthinking.
Maria Zhang, CEO of Palona AI, and Somya Rai, Principal AI Engineer at EXL, discuss the architecture, scaling, memory management, and cost optimization of multi-agent systems in their respective domains of restaurants and insurance. They explore practical challenges, such as real-world bottlenecks and regulatory compliance, and share their technical stacks, including LangGraph, Ray, and NVIDIA platforms, for building robust and efficient agentic solutions.
Anthropic’s Meaghan Choi and Alex Albert explore the design philosophy behind Claude Code, discussing its terminal-first approach, the evolution of developer workflows in the age of LLMs, and how agentic coding empowers both engineers and designers.
Large Language Models (LLMs) are unsuitable for building decision agents in complex AI frameworks due to their inconsistency and lack of transparency. This summary explores an alternative approach using dedicated decision platforms and machine learning models to create consistent, explainable, and agile decision-making systems for enterprise automation.
Joe Reis and Matt Housley, authors of "Fundamentals of Data Engineering," reflect on the book's principles three years after its publication. They discuss how the rise of AI has created both powerful tools and dangerous "bear traps" for engineers, the critical role of expertise in a world of AI-generated content, and why foundational knowledge is more important than ever.