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The author introduces 'Anchor Engineering,' a software development method that encodes human design judgement into 'anchors'—guiding constraints stored in an 'Anchor Book' alongside the code—so AI coding agents can build systems faster without losing shared understanding, quality, or team coordination.
The author describes challenges in team collaboration with AI coding agents, such as conflicting decisions and context drift, and seeks advice from the community.
The author, running a small dev team, shares mixed real-world results from using AI coding tools: they speed up boilerplate and onboarding, but produce confident wrong answers on complex problems and increase code review workload, yielding modest net gains far below the often-cited 10x improvement.
The post compares AI agents to 'rockstar developers' who create clever but unmaintainable code, pointing out that agents lack memory of their own actions. It recommends using visible conventions like AGENTS.md, ADRs, and tests to keep agent-generated code understandable by the team.
The article describes lessons learned from building a 'harness' system to wrap coding agents with context, tools, provenance, and verification, detailing the first two of eight pillars: Context and Provenance.
OpenAI demonstrates how ChatGPT Work streamlines marketing workflows, from customer insights to campaign execution, for team collaboration.