This week, a long technical article dissecting MyCodeAgent source code went viral on Juejin, landing a counterintuitive claim: what determines how far an AI coding assistant (Agent — an AI program that can autonomously complete multi-step tasks) can run isn't the model — it's how its "prompt" is architected across four layers.
What This Is
In the old way of writing Agent prompts (the hidden instructions sent to the large model that govern how the AI speaks and uses tools), the habit was to pack persona, tool descriptions, and project conventions into one big string. It runs, but it's a maintenance nightmare — change one thing and you have to re-read the whole file; swap out the tool list and the entire string's cache goes invalid.
MyCodeAgent's approach: the system message sent to the model is split into four layers — Identity Constitution (the persona layer), Tool Contracts (natural-language usage conventions for each tool), Project Rules (loaded from a code_law.md file in the repo), and Runtime Signals — plus an optional cross-turn summary memory module.
Each layer can evolve and be cached independently. Change the persona without touching tools; swap tools without polluting project rules. Prompt engineering is shifting from "writing a paragraph" to "building a system."
How the Industry Sees It
The supportive camp: we notice that Claude Code, Cursor, ByteDance's Trae, and other leading AI coding products are all moving toward layering. The payoff is clear — maintenance costs drop, cache hit rates climb, product iteration accelerates.
But there are sober objections. A senior engineer on Hacker News put it bluntly: for 90% of internal Agents, layering is over-engineering — a 500-word prompt doesn't need to be split into modules; splitting it actually makes debugging harder. Another overlooked risk is vendor lock-in — build your internal pipeline around one open-source framework's layering convention, and switching vendors means rewriting everything.
There's another hard reality: this architectural capability is currently concentrated in companies with proprietary models. Application-layer players copying the homework will discover the underlying constraints aren't in their hands.
Impact on Regular People
For enterprise IT: when evaluating AI coding tools, "can the prompts be audited, can they be managed in layers" will graduate from a technical footnote to a procurement requirement — especially in compliance and code-security scenarios.
For working professionals: knowing how to write prompts is now entry-level; understanding "how to split prompts into layers, how to keep an Agent from losing memory across long tasks" is the new differentiator — and it isn't just for engineers, either. Product managers and ops teams will find it just as useful.
For the consumer market: as AI assistants entrench themselves in the enterprise, tools that can reliably run long tasks without mid-stream "personality splits" will command a premium. Today's breakout products may quietly get swapped out six months from now — casualties of maintenance overhead.