Treat the Context Window Like a Budget, Not a Junk Drawer

Short summary

The article introduces a context engineering framework that treats the AI context window as a bounded budget, using a dedicated context-engineer agent to produce a manifest before downstream agents begin work. It distinguishes Context, Memory, and Learning as separate concerns, applies context decay so stale artifacts are summarized rather than loaded verbatim, and projects a shared 24-agent, 53-skill layer across six AI coding tools with parity checks to prevent drift. Even without adopting the full framework, the core ideas—pre-flight manifests, structural handoffs, and context/memory/learning separation—are immediately stealable for any AI-assisted development workflow.

• •Context window should be treated as a budget with a pre-flight manifest produced by a dedicated context-engineer agent
• •Framework distinguishes Context (working set), Memory (durable corpus), and Learning (feedback loop) instead of flattening all into RAG
• •Shared canonical layer with 24 agents and 53 skills projected across Claude Code, Cursor, Windsurf, GitHub Copilot, Gemini, and OpenAI Codex with parity checks

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