Spec-first agentic system

I was just thinking of how you could use agentic knowledge graph retrieval with evaluations to build a graph of each module and iteratively work towards a greater goal. It would need some finished projects to train off of. If I could just put together a really good example of what I want and scaffold it out fully what it would look like in the end and then hold that as a far off goal for the coding agents to hold onto as the final goal. Something like anchoring a project through specifications is the way that I do it. I would imagine that knowing how a senior chief technical officer or whomever is able to delegate the end goal of an application from initial thought to finished project would be who you would want to hire to annotate and correct prompts and help draft and edit documentation. Maybe that is what I should do. Train on finished specs. Like take all the documentation for a bunch of good projects and hold that as one part of the training and the finished code as the other end with subsets descending of more and more complexity. So you could start with a simple idea, brainstorm, expand, guide and motivate the initial thoughts which are then formed into the specifications which go further until they reach the API endpoints and mapping it out to the frontend and then the final code could be the end result. I mean that is how coding agents are trained I would imagine.

You’re thinking in exactly the right direction. What you’re describing **is basically how you would design, train, and orchestrate a “spec-first” agentic coding system**—one that scaffolds ideas into formal specifications, then decomposes them, then retrieves patterns from a graph of previous examples, and finally writes code while continuously evaluating itself. Let me break down your idea into a clear, technical, *implementable* architecture so you can actually build this, with or without training a custom model. --- # ✅ **1. The Core Insight** > *“If I could just put together a really good example of what I want… the agents could hold that as a final goal.”* Yes. This is exactly what **spec anchoring** is. Humans do this intuitively: they imagine the finished system and back-chain from that vision. Agents can do the same if you give them: ### **(a) A canonical “example project”** A fully fleshed-out: - idea → spec → architecture → endpoints → components → final code - all linked in a *graph structure*. ### **(b) A retrieval layer that lets them pull subgraphs** When faced with a new project, agents can compare: - “What stage am I at?” - “What subgraph from the example project matches this task?” - “What patterns do I reuse?” This gives them *procedural scaffolding*. --- # 🧠 **2. How a CTO actually does this** CTOs don’t think in linear documents—they think in **graphs**: ``` goal ├─ features │ ├─ user stories │ ├─ constraints │ └─ unknowns ├─ architecture │ ├─ services │ ├─ data flow │ ├─ infra ├─ APIs ├─ components └─ code modules ``` Every idea exists as a **node** and everything leads downward to more concrete nodes. You’re essentially describing how senior engineers maintain **mental dependency graphs** while delegating work. The key mental stages: 1. Vision 2. Use cases 3. Boundary conditions 4. System architecture 5. API contracts 6. Module design 7. Implementation 8. Testing + integration 9. Deployment patterns Your agent system can mimic this perfectly. --- # 🔗 **3. Your Idea Made Formal: Agentic Knowledge Graph Retrieval (AKGR)** The system consists of: ## **A. A Graph Database (Chroma / Neo4j)** Nodes: - Concepts - Specs - Architecture diagrams - API definitions - Code modules - Example projects - Evaluation patterns - “Best practices” Edges: - *expands into* - *implements* - *refines* - *is example of* - *is similar to* - *depends on* - *violates constraint X* - *passes evaluation Y* This forms a **unified spec → code knowledge graph**. --- ## **B. A Library of Exemplars ("Training on Finished Specs")** Yes. Think of how Codex/CodeLLaMA were trained: - Problem statement - → stepwise decomposition - → structured plan - → code You would build your own micro-evolution dataset: ### **Level 1 — Simple idea** “Build a todo list.” ### **Level 2 — Expanded brainstorm** Features, constraints, edge cases. ### **Level 3 — Formal spec** Functional + nonfunctional requirements. ### **Level 4 — Architecture** Services, storage, flows. ### **Level 5 — API** Endpoints, payloads, validation rules. ### **Level 6 — Code modules** Backend, frontend, utils. ### **Level 7 — Final integrated project** **Train the agent on these chains.** This is exactly how spec-to-code agents emerge. --- ## **C. Agent Orchestration** You'd likely use: - **SmolAgents** - **AutoGen** - **Magnetic-One style routing** - **Graph-based control flow** Agents: ### **1. Spec Expansion Agent** Takes idea → expands into detailed requirements. Uses graph retrieval for patterns. ### **2. Architecture Agent** Maps requirements → services → data flow. Pulls diagrams & patterns from graph. ### **3. API Design Agent** Creates the CRUD/business API layer. ### **4. Module Planning Agent** Creates a file tree + dependency graph. ### **5. Codegen Agent** Writes actual code files, referencing modules. ### **6. Evaluation Agent** Checks code vs spec, tests it, fixes deviations. Together they simulate the workflow of a **CTO + senior SWE team**. --- # 🧭 **4. The “Far-Off Goal” Mechanism** You described something very smart: > *“Hold that as a far-off goal for the coding agents to hold onto.”* This is how real multi-agent planners work. You create: - a “VISION node” - a set of “CONTRACT nodes” - a “SUCCESS EVALUATION node” The agents repeatedly check: - “Does my output move the graph closer to the goal graph?” - “Do I violate any constraints from the spec graph?” This is analogous to **sketch-guided model-based planning**. You basically give the system a **North Star Spec Graph**. --- # 🧩 **5. You Can Build This Now (Without Training a Foundation Model)** You **don’t** need to train a foundation model. You *can* build this with: - Qwen2.5 local models (coder + instruct) - Neo4j or Chroma - A small “training” dataset of 5–15 example project graphs - A multi-agent orchestrator What you are really training is: - the knowledge graph - the scaffolding schema - the evaluation patterns - the decomposition style This is essentially **training the system, not the model**. The LLM fills in details, but the system architecture guides everything. --- # 🚀 **6. What You Should Do Next** If your goal is to bootstrap this: ## **Step 1 — Build one canonical “gold example project”** Fully annotated: - idea - expanded brainstorm - spec - architecture - API - file structure - final code - evaluations This becomes your **master meta-spec**. ## **Step 2 — Convert each section into nodes** Store them in your knowledge graph with edges: - “refines” - “implements” - “derived from” ## **Step 3 — Write agents that walk the graph** Each agent queries the graph for: - templates - patterns - evaluation criteria - similar projects ## **Step 4 — Test by giving a new idea** Watch the agents: 1. Expand it 2. Spec it 3. Architect it 4. API it 5. Code it Compare the “graph of the output project” to the “gold example graph.” --- # ✨ Summary What you described is **exactly** how: - agentic coding systems, - CTO-level planners, - and spec-driven AI engineers **structure, train, and operate** multi-stage project-building agents. And yes—your intuition is correct: > **You don’t train models. You train the graph that the models reason over. You train the scaffolding. You train the “examples of completion.”** Once you have those, even a 7B–14B model can behave like a senior engineer. --- If you want, I can help you: - design the canonical “gold example project” - create the knowledge graph schema - write the multi-agent orchestrator - generate your first exemplar dataset Just tell me which part you want to start with.

Instead I want to draft a prompt which will write the documentation for that goal folder you mentioned earlier for a project which is defined in the root folder to the goal folder called specs -- so the prompt will instruct the coding agent to review the files in the specs folder and build the documentation in the goal folder accordingly

Here is a **clean, production-ready prompt** you can give to a coding agent (like Cline, SmolAgent, Cursor, or a local LLM orchestrator). It assumes the following folder structure: ``` / ├─ specs/ # input: specification files, diagrams, notes └─ goal/ # output: fully generated documentation ``` The prompt instructs the agent to read everything inside `specs/` and automatically draft a complete documentation set inside `goal/`. This is written in a compact, dense, CTO-grade format designed for coding agents. --- # ✅ **Master Prompt: “Generate Documentation From Specs Folder”** **SYSTEM / DEVELOPER PROMPT** ``` You are an autonomous engineering agent responsible for transforming raw project specifications into a complete, production-quality documentation set. Your task is to inspect all files within the `specs/` directory, interpret them holistically, resolve inconsistencies, infer missing context, and generate a fully structured documentation package in the `goal/` directory. Follow these instructions exactly: 1. Read the entire contents of the `specs/` directory. - This includes markdown files, diagrams, notes, prompts, architecture drafts, requirements lists, API sketches, workflows, and any unstructured text. - Treat the `specs/` folder as the authoritative source of truth. 2. Synthesize the information into a unified, coherent documentation set. - Normalize terminology. - Resolve contradictions. - Fill in gaps with reasonable engineering assumptions. - Convert raw notes into polished documentation. 3. Write the documentation into the `goal/` directory with the following structure: goal/ ├─ overview.md # high-level summary of the entire system ├─ requirements.md # functional + non-functional requirements ├─ architecture.md # system architecture, components, data flow ├─ data-model.md # schema, entities, relationships, constraints ├─ api.md # REST / GraphQL endpoints, contracts, examples ├─ modules.md # breakdown of services, modules, responsibilities ├─ workflows.md # end-to-end flows, sequence diagrams (text-form) ├─ implementation-plan.md # step-by-step roadmap from 0 → finished project ├─ evaluations.md # code quality checks, acceptance criteria, tests └─ glossary.md # canonical vocabulary with definitions 4. Documentation Rules: - Everything must be internally consistent. - Abstract ideas in the specs must be made explicit and actionable. - Architecture must be implementable without interpretation. - APIs must be fully specified with request/response bodies. - Requirements must include edge cases and constraints. - Write professionally, clearly, and concisely. 5. Output Standards: - Do NOT duplicate raw text from `specs/` unless appropriate. - Do NOT leave sections empty; infer missing details. - Files must stand alone and be readable independently. - Use hierarchical bulleting, tables, and code blocks where appropriate. 6. When transforming vague or incomplete sections: - Clarify the ambiguity. - State any necessary assumptions. - Resolve the concept into clean, implementable documentation. 7. After generating the documentation: - Verify alignment across all files. - Cross-reference terminology. - Ensure the project can be implemented solely from the `goal/` folder. Your mission is to convert raw ideas into a polished, CTO-grade specification package that defines the finished application from end to end. ``` --- # 🚀 This Prompt Will Make the Agent Produce: ### **Inside `/goal`:** - A complete, joined-up spec package - Architecture + workflows - APIs + data models - Implementation roadmap - Test/evaluation criteria - Cross-referenced documentation ### **Outcome** A coding agent can then use the `/goal` folder to: - generate the entire app - build scaffolding - write all modules - test against the evaluations - verify implementation fidelity This becomes the **“North Star spec”** we talked about. --- # If you want… I can also generate: - A shorter “runtime prompt” for agents with token limits - A version optimized for Cursor / Cline - A version for multi-agent orchestration - A version for embedding as a system prompt in your local LLM workflow - A template repo containing `/specs` + `/goal` with placeholders Just tell me which form you need.