Your AI coding agent doesn't need a smarter model. It needs your backlog.

The article argues that coding agents usually fail on complex tasks because their context is stale or incomplete, not because the models are fundamentally weak.

It proposes giving agents direct access to live project context—like a structured backlog, acceptance criteria, tests, and architecture decisions—through MCP.

With tools like Claude Code and Codex, agents could fetch the current truth on demand instead of relying on pasted prompts or guesswork.

The core idea is that better context retrieval, not bigger models, may deliver a cheaper and more practical boost to real-world coding performance.