Beyond AGENTS.md: Turning AI Pair Programming into Workflows

In practice, the starting pattern for using AI to write code is usually the same: open the IDE, highlight some code, and ask an AI agent (like Copilot or a chat‑based assistant) to "write this feature" or "fix this bug." It can prove to be very powerful and time-efficient, but on the flip side, it can quickly run into predictable failure modes: Context window overflow and degraded responses over time Inconsistent architectural decisions across features Superficial or self‑congratulatory test coverage Features drifting away from original requirements Hidden technical debt that's hard to detect in review The issue isn't that AI is incapable or that the agent is the wrong tool. Instead, the problem lies in teams applying it without structure. This is where the practice of Context Engineering becomes essential. It is the foundational layer that makes AI workflows actually function in a complex repository. Jumping straight into generating code workflows often fails because, in a real-world