Engineering is shifting from synchronous, line-by-line implementation to an asynchronous model in which agents execute, verify, and retry while humans own intent, architecture, constraints, and release decisions. 

Drawing on my experience building OpenAI Ads with Codex from day one and scaling it to more than $100 million in ARR in under six weeks, I’ll share an end-to-end operating model for building a production product with coding agents while avoiding common failure modes. I’ll share what worked, what did not, and the lessons we learned about using coding agents to build quickly without compromising quality or engineering judgment.

To make the workflow concrete, I’ll use a sanitized case study of reducing latency on a complex production request path without compromising correctness or reliability. Performance work creates a particularly useful agentic workflow because progress is measurable. We can establish a baseline, investigate bottlenecks, implement candidate improvements, run benchmarks and regression tests, and iterate based on evidence until the improvement holds.

Although the examples use Codex, the operating model is not Codex-specific. The same principles apply to other agentic coding tools and harnesses that can work with a codebase, use engineering tools, and iterate against tests, benchmarks, and other forms of feedback. 

I’ll also separate tokenmaxxing from token economics. The goal is not to minimize tokens or maximize agent activity. It is to spend more inference only when verification shows that it improves accepted production throughput, cycle time, or reliability. Attendees will leave with a practical operating model for moving faster with coding agents without surrendering engineering judgment or accountability.

Main Takeaways:

1. Use measurable feedback loops for complex production work. Follow a sanitized latency improvement from baseline and investigation through implementation, benchmarking, regression testing, and rollout.
2. Build a repeatable agentic engineering workflow, not a tool-specific trick. See how the practices demonstrated with Codex can be applied with other agentic coding tools and harnesses.
3. Practice token economics, not indiscriminate tokenmaxxing. Spend inference where evidence shows it improves cycle time, reliability, and accepted production output.