Aparna Dhinakaran of Arize AI argues that the AI engineer's job is shifting from writing features to building the loop that builds them: evals that review non-deterministic work, telemetry that agents consume, and always-on agents that act on it. She uses Arize's Signal agent as a case study, covering triggers, sandboxes, durable state, model and harness independence, and evaluating the evaluators.
Erik Bernhardsson, CEO of Modal, traces his path from building Spotify's recommendation system to founding a serverless GPU cloud, and explains how Modal aggregates demand, secures GPU capacity, weighs renting against owning, and sees agent sandboxes becoming a major product line.
Philipp Schmid of Google DeepMind introduces the Gemini Interactions API, a unified interface for models and agents with server-side state and long-running jobs. He then shows managed cloud sandboxes for Antigravity agents that persist, can be shared, and automatically pick up skills and AGENTS.md files, closing with a live AI talk radio demo built from a single prompt.
Ryan Cooke of WorkOS argues that software factories should be judged by customer outcomes rather than PR counts or AI-generated code volume. He describes how WorkOS embedded its product engineering process into its factory through TARS, Horizon, a PM agent that drafts Hilltop docs, and an internal MCP gateway connected to Snowflake, along with bug triage, owned sandboxes, a memory layer and outcome-focused metrics.