For complex, parallel tasks that might conflict, use `git worktrees`. This creates separate, tracked copies of the codebase, allowing multiple AI agents to work on different features simultaneously without creating merge conflicts in the main branch.

Contrary to demos showing AI building a full feature, experienced developers break work into the smallest logical commits first (e.g., refactor, new endpoint, tests). They then prompt the AI for each individual commit, ensuring changes are focused, reviewable, and maintainable.

Instead of starting new chats for every task, use single, long-running 'monothreads' for each major workstream. Advanced context compaction in tools like Codex allows these threads to persist memory over time, turning the AI from a simple Q&A bot into an ongoing project collaborator with deep context.

When an AI coding assistant goes off track, it can be hard to undo the damage. Developer Terry Lynn mitigates this risk by programming his AI workflow to make a Git commit before and after each small phase of a task. This creates a trail of "breadcrumbs," allowing him to easily revert to a stable state if the AI makes a mistake.

Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.

To make his personal AI development manageable, Steve Newman structures his work as a suite of microservices. Each of his 15+ apps is its own project with a separate GitHub repo and database. This modular approach keeps the context window for the AI coding agent small and focused, which he believes is crucial for its effectiveness.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

To avoid context drift in long AI sessions, create temporary, task-based agents with specialized roles. Use these agents as checkpoints to review outputs from previous steps and make key decisions, ensuring higher-quality results and preventing error propagation.

In an autonomous development environment, it's easy for an AI to deploy prematurely. Explicitly instructing Codex to "save for review" forces a pause, acting as a manual checkpoint. This allows the human developer to verify the build status, storage choices, and access settings before pushing changes live.