Prabira Acharya explains that managing data storage without observability is like driving a car without a dashboard. The talk outlines the seven pillars of storage observability, the critical role of AI in analyzing vast amounts of data for anomaly detection and predictive analytics, and the evolution toward agentic AIOps for creating self-healing and self-managing storage infrastructures.
Liz Fong-Jones and Lesley Cordero explore the evolution of platform engineering from its DevOps and SRE roots, discussing the challenges of building effective developer platforms, the importance of psychological safety, the complexities of open source sustainability, and the delicate balance between centralized platform teams and developer autonomy.
Albert Tanure's "ASP.NET Core 9 Essentials" is structured to guide developers through the entire application lifecycle, starting with foundational .NET concepts and culminating in modern cloud-native principles. The book emphasizes understanding not just the code, but also the operational aspects like DevOps, observability, and dynamic configuration necessary for building and maintaining applications in today's dynamic cloud environments.
Experts from Anthropic discuss the evolution of the Claude Developer Platform, the philosophy of "unhobbling" models with tools rather than restrictive scaffolding, and the future of building sophisticated, autonomous AI agents with features like the Claude Agent SDK, advanced context management, and persistent memory.
An inside look at AlwaysCool.ai's journey from simple GPT wrappers to a production-ready agentic infrastructure. This talk covers the evolution from synchronous tools to asynchronous, multi-step flows orchestrated by LangGraph, the critical role of OpenTelemetry for compliance and observability, and the architectural patterns of using FastAPI to serve centralized AI agents.
Annie Condon and Jeff Groom from Acre Security detail their practical approach to robustly evaluating non-deterministic AI agents. They share their philosophy that evaluations are critical for quality, introduce their "X-ray machine" analogy for observability, and walk through their evaluation stack, including versioning strategies and the use of tools like Logfire for tracing and Confident AI (Deep Evals) for systematic metric tracking.