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.
As AI agents handle the mechanics of code generation, the primary role of a developer is elevated. The new bottlenecks are not typing speed or syntax, but higher-level cognitive tasks: deciding what to build, designing system architecture, and curating the AI's work.
AI coding agents enable "vibe coding," where non-engineers like designers can build functional prototypes without deep technical expertise. This accelerates iteration by allowing designers to translate ideas directly into interactive surfaces for testing.
Product managers can use coding agents like Codex for self-service technical discovery. Instead of interrupting engineers with questions, they can ask the AI about the codebase, feature status, or implementation details, increasing their autonomy and team efficiency.
Newer models like OpenAI's 5.2 can solve bugs that were previously impossible for AI by "thinking" for extended periods—up to 37 minutes in one example. This reframes latency not as a flaw, but as a necessary trade-off for tackling deep, complex problems.
When a coding agent loses context, don't just start over. A power-user technique is to begin a new session and instruct the agent to read the locally stored conversation logs from the previous, failed session to regain context and continue the task.
The "harness" around a model is key to its performance. The Codex CLI is open-source so users can see exactly how OpenAI gets the best results from its own evolving models, serving as a real-time guide to advanced prompting and interaction techniques.
Go beyond static AI code analysis. After an AI like Codex automatically flags a high-confidence issue in a GitHub pull request, developers can reply directly in a comment, "Hey, Codex, can you fix it?" The agent will then attempt to fix the issue it found.
To get a thorough implementation plan from Codex, provide it with a `plans.md` file. This file acts as a template, or "meta-plan," defining what a good plan looks like (e.g., milestones, self-contained steps), which guides the AI to produce a more structured output.
A proactive AI feature at OpenAI that automatically revised PRs based on human feedback was unpopular. Unlike assistive tools, fully automated loops face an extremely high bar for quality, and the feature's "hit rate" wasn't high enough to be worth the cognitive overhead.