To maximize leverage, reframe every SDLC component—docs, tests, review agents—as a way to 'prompt inject' non-functional requirements into the agent. This approach teases out expert knowledge from engineers' heads and makes it part of the automated system, guided by the agent's mistakes.

To prevent an AI agent from repeating mistakes across coding sessions, create 'agents.md' files in your codebase. These act as a persistent memory, providing context and instructions specific to a folder or the entire repo. The agent reads these files before working, allowing it to learn from past iterations and improve over time.

Creating "skills" (e.g., Markdown files) to teach AI agents how to interact with a codebase forces developers to explicitly document processes and best practices. This AI-centric documentation serves a dual purpose as a clear contribution guide for humans, effectively turning what should be a `contributing.md` file into a machine-readable, actionable standard.

To improve an agent's performance on a specific task like prompting the VO3 video model, create a dedicated 'onboarding document'. Use a tool like Perplexity to gather best practices from experts, compile them into a doc, and instruct the agent to reference it. This shortcuts the learning curve and embeds expertise.

Kun Chen's 'no mistakes' pipeline includes a testing phase where agents run comprehensive end-to-end tests to check for regressions. Crucially, the agent captures and embeds evidence, like screenshots or videos of the working feature, directly into the PR description for easy human verification.

OpenAI structures its repositories to be a complete, self-contained knowledge base for AI agents. All project artifacts—design docs, historical implementation plans, and even text versions of external library documentation—are checked in, allowing the agent to find any needed context via simple search.

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

To get consistent, high-quality results from AI coding assistants, define reusable instructions in dedicated files (e.g., `prd.md`) within your repository. This "agent briefing" file can be referenced in prompts, ensuring all generated assets adhere to a predefined structure and style.

The 'agents.md' file is an open format that functions like a README, but specifically for AI agents. It provides a dedicated, predictable place to store context and instructions, ensuring the AI consistently follows rules for commits, tests, and project setup across all your repositories.