Explore the shift from traditional, human-driven software development to an agent-native lifecycle. Learn how AI agents, powered by centralized context, can orchestrate the entire SDLC, from planning and coding to incident response, transforming developers into orchestrators of AI systems.
Scott Wu, CEO of Cognition AI, discusses the exponential growth of AI capabilities in software engineering, likening it to a "Moore's Law for AI agents" with a doubling time of every 70 days. He chronicles the evolution of their AI agent, Devin, from handling repetitive code migrations to autonomously managing entire backlogs, highlighting the key technical challenges and paradigm shifts at each stage.
Rakesh Kumar from Lyft details the evolution of their real-time feature generation platform, from cron jobs to a sophisticated streaming architecture using Apache Beam and Flink. Key discussions include solving the 'hot shard' problem with geohashes, building a custom geospatial feature store, and optimizing pipelines with YAML-based configurations.
Eric Peter from Databricks outlines the evolution from the traditional MLOps lifecycle to the more complex Agent Ops lifecycle. He details the five essential components of a successful agent development platform and introduces MLflow 3.0, a new release designed to provide a comprehensive, open-standard solution for building, evaluating, and deploying AI agents.
Sherwood Callaway of 11X discusses the architecture of "Alice," an AI Sales Development Representative. He covers the practical decision to use LangGraph for its reliability in production, the challenges of infrastructure and observability when using hosted agent platforms, and their methodology for running Evals to mitigate hallucinations by comparing generated content against source data.
Zohar Bronfman discusses why current LLMs are not on a path to AGI, contrasting their combinatorial creativity with the transformational, domain-general intelligence of humans. He argues that predictive models, not generative ones, deliver the most business value and explains how his platform, Pecan AI, automates the critical data preparation bottleneck to democratize predictive analytics for all businesses.