Jason Lopatecki
Co-founder and CEO of Arize AI, an AI and agent observability and evaluation company; previously co-founded TubeMogul, which went public and was acquired by Adobe.
Telemetry Files Turn Coding Agents Into First-Response Investigators
Jason Lopatecki of Arize argues that the constraint on agent-driven software repair is no longer generating code but assembling enough production evidence to trust a fix. His proposed loop has agents retrieve traces, logs and evaluations into the repository as files, investigate the execution path that actually failed, and produce a pull request for human review. The model shifts engineers from initial incident response toward review, though Lopatecki says larger changes still require human direction.
Agentic AI Is Turning Model Quality Into a Systems Problem
At AI Engineer Singapore’s second day, speakers from Google DeepMind, Cloudflare, Arize, OpenClaw, Adaption and other teams made a shared engineering case: as AI systems become more agentic, model quality is no longer separable from the systems around the model. Richard Ngo framed the risk as long-horizon, situationally aware agents whose goals cannot be inspected, while practitioners argued that production AI now depends on continuous evaluation, traces, deterministic execution boundaries, routing, memory, fine-tuning and test-time search. The source’s central claim is that useful and safe agentic AI is becoming a systems problem, not just a model-selection problem.