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Notes on shared work-state, overlap between agents, handoffs, and reviewing code that a person did not write.
Learn how to keep two coding agents aligned on the same task without losing work or duplicating effort. A practical guide to sharing state between agents.
A practical workflow to run Claude Code and Codex side by side, transfer context between them, and keep changes reviewed and aligned.
What a Claude Code agent team looks like in practice, the three patterns people confuse, and what shared state each pattern needs to avoid breaking work.
What coordination means for AI agents in a codebase and how it differs from orchestration, with concrete examples.
A practical guide to coordinating multiple parallel Claude Code agents on one repository without duplicating work or losing context.
How an AI native engineering team scopes work, staffs reviews, and keeps coordination costs low when most code is generated by agents.
Learn how to keep an AI agent from guessing wrong during pair programming, so a mistake costs seconds instead of hours.
How to write short, specific Cursor rules that stop agents from repeating the same mistakes and make reviews faster.
Practical ways to scope tasks, keep runs short, and decide what an agent can do unsupervised when you use the Codex CLI.
Learn the three situations where AI coding agents cost more than they save. Includes concrete examples and a strong objection answered.
Learn how to keep coding agents from inventing facts and assumptions by making them surface and confirm their context as they work.
A concise explanation of why lines of code and PR counts no longer signal productivity when AI agents write code, and which metrics actually matter.