This presentation by Maciej Jedrzejewski explores the concept of evolutionary software architecture, arguing that architecture is not a static, upfront design but a continuous process. It delves into the cognitive biases that lead to over-engineering, emphasizes the critical role of context, and provides a practical four-step model for evolving systems from simplicity to complexity.
Grant Miller challenges the "Hollywood view" of AI super agents, proposing a shift towards collaborative, specialized agentic systems. He introduces a framework for categorizing agents based on their risk and capability, detailing how to design safer, more effective AI applications by minimizing access, implementing dynamic controls, and incorporating a human-in-the-loop for high-risk tasks.
A practitioner's guide to deploying a text-to-SQL agent in a real-world business environment. The talk covers the critical lessons learned in moving from concept to production, focusing on the importance of the communication channel (Slack), the necessity of a semantic layer over benchmark scores, and a pragmatic approach to system architecture, testing, and evaluation.
A deep dive into the challenges of deploying AI agents in production, arguing that reliability stems not from model intelligence but from a "system-first" approach. The talk introduces a new architecture that separates the LLM's reasoning from a versioned, auditable "Context Layer" containing business logic and expert knowledge, which is continuously updated through a "Living Ground Truth" loop driven by expert feedback.
David Anderson explains his "Value Flywheel Effect" framework, a model for continuous cloud modernization that joins business and technology strategy. He details how creating psychological safety, a robust serverless-first technology strategy, and a focus on system design over code builds the necessary momentum and shared context to harness future technologies like AI effectively.
Poetic, a startup by ex-DeepMind researchers, has significantly advanced performance on the ARC-AGI benchmark by applying a recursive self-improvement system to Gemini 3. Co-founder Ian Fisher discusses how their approach of automating prompt and system engineering provides a substantial performance boost without needing access to model weights, and explores its potential as a path toward AGI.