
How to pair program with an AI coding agent
Learn how to keep an AI agent from guessing wrong during pair programming, so a mistake costs seconds instead of hours.
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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 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.

Learn how to move unfinished coding tasks between agents without losing assumptions, rejected approaches, or context. Keep work alive across sessions.

Why undocumented decisions are the main source of technical debt in AI generated code and what to capture at the moment you make them.

Learn how to write prompts that get code changes you can review and trust, with clear constraints, scoped files, and explicit assumptions.

A step-by-step guide to running parallel coding agents on the same repository using git worktrees, with concrete commands and warnings about what the setup does not solve.

Learn why reviewing and integrating generated work safely matters more than typing speed, and how to judge scope, taste in review, and document decisions so the next person is not guessing.

Compare MCP servers by category to see which ones handle repository access, issue trackers, browser tasks, databases, docs search, and shared state between agents.