Jason Liu
Jason Liu is a Developer Experience Engineer on OpenAI’s Codex team and creator of Instructor, a Python library for structured outputs from language models. His work focuses on AI systems, developer tools, and practical AI software development.
Persistent Agents Turn AI Work Into Shared Team Investigations
At OpenAI DevDay 2026, Jason Liu argues that the next step for workplace agents is not simply helping one person do more, but letting teams contribute to work that continues across conversations, meetings and devices. He says persistent agents, shared workspaces and reusable tools can keep an investigation moving without requiring one person to gather every clue, relay updates and carry the task to completion.
AI Agents Need Bounded Loops, Observable Goals, and Approval Boundaries
OpenAI developer-experience lead Jason Liu argues that Codex is most useful when treated as a set of durable, bounded workstreams rather than a succession of disposable chats. His model starts with a pinned thread that retains context and reports on a defined condition, then adds persistent goals only where completion can be verified, and durable memory, skills and broader computer control only when repeated work warrants them. The governing constraint is explicit: give the system enough context to prepare useful work, but keep action and approval boundaries clear.
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.