M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Prompting Specialist Research - Multi-Agent Architecture Integration **Date:** 2025-10-16 **Status:** Research & Design Phase **Version:** 1.1 (Advanced Discipline Principles Added) --- ## Executive Summary This document analyzes how prompt writing fits into the multi-agent Graph-RAG architecture and designs an improved autonomous prompt specialist agent for the PM/Worker/QC workflow. **Key Innovation:** Ephemeral prompt specialist that transforms task descriptions into production-ready worker prompts, then terminates (natural context pruning). **Target Use Case:** PM agent spawns prompt specialist for each task in the graph, stores optimized prompts in task nodes, enables workers to execute with clearer instructions. --- ## 🎯 PROMPT WRITING IN MULTI-AGENT ARCHITECTURE ### Current Architecture Analysis Looking at `MULTI_AGENT_GRAPH_RAG.md` and `DOCKER_MIGRATION_PROMPTS.md`: **PM Agent** currently creates: - Task breakdowns stored in knowledge graph - Worker-facing prompts (like in `DOCKER_MIGRATION_PROMPTS.md`) - Context packages for ephemeral workers **The Gap**: PM is doing prompt engineering **implicitly** as part of task decomposition, but this isn't a specialized capability. --- ## 🏗️ PROMPT WRITING ROLES IN MULTI-AGENT WORKFLOW ### Option 1: **Ephemeral Specialist Consultant** (Recommended) ``` ┌─────────────────────────────────────────────────────────────┐ │ PM AGENT (Orchestrator) │ │ ┌────────────────────────────────────────────────────────┐ │ │ │ 1. Research requirements │ │ │ │ 2. Break down into tasks │ │ │ │ 3. FOR EACH TASK: │ │ │ │ ├─→ Spawn Prompt Specialist (ephemeral) │ │ │ │ │ "Create worker prompt for: [task description]" │ │ │ │ ├─→ Specialist returns optimized prompt │ │ │ │ ├─→ Store prompt in task node │ │ │ │ └─→ Specialist terminates (context pruned) │ │ │ │ 4. Store task graph with optimized prompts │ │ │ └────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ↓ KNOWLEDGE GRAPH ┌───────────────────────────────────┐ │ Task Node │ │ ├─ description │ │ ├─ dependencies │ │ ├─ worker_prompt (optimized) ←── │ │ └─ context_requirements │ └───────────────────────────────────┘ ↓ WORKER AGENTS ┌───────────────────────────────────┐ │ Pull task + optimized prompt │ │ Execute with clear instructions │ └───────────────────────────────────┘ ``` **Why This Works**: - ✅ **Context Isolation**: Each specialist spawn has only ONE task context - ✅ **Natural Pruning**: Specialist terminates after prompt generation - ✅ **Specialization**: PM focuses on strategy, specialist on prompt quality - ✅ **Parallel Scaling**: Can spawn multiple specialists for task graph --- ### Option 2: **Persistent PM Sub-Agent** (Alternative) ``` ┌─────────────────────────────────────────────────────────────┐ │ PM AGENT (Orchestrator) │ │ ┌────────────────────────────────────────────────────────┐ │ │ │ Long-term memory: Project requirements, architecture │ │ │ └────────────────────────────────────────────────────────┘ │ └──────────────┬──────────────────────────────────────────────┘ │ ├─→ Spawns Specialist (persistent for project duration) │ ┌──────────────▼──────────────────────────────────────────────┐ │ PROMPTING SPECIALIST (Persistent) │ │ ┌────────────────────────────────────────────────────────┐ │ │ │ Medium-term memory: Project prompt patterns │ │ │ │ Receives: Task descriptions from PM │ │ │ │ Returns: Optimized worker prompts │ │ │ │ Learns: Prompt patterns that work for this project │ │ │ └────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ``` **Why This Could Work**: - ✅ **Pattern Learning**: Specialist remembers what prompt styles work for this project - ✅ **Consistency**: All worker prompts follow similar structure - ⚠️ **Context Growth**: Specialist's context grows with each task (needs management) --- ### Option 3: **Post-QC Prompt Improver** (Feedback Loop) ``` WORKER → QC AGENT → ❌ FAIL → SPECIALIST (Prompt Debugger) ↓ "Worker failed because prompt was ambiguous. Here's improved version with: - Clearer success criteria - Explicit examples - Better context" ↓ Update task node → Retry worker ``` **Why This Could Work**: - ✅ **Evidence-Based**: Improves prompts based on actual failures - ✅ **Iterative Refinement**: Prompts get better over time - ⚠️ **Reactive**: Only fixes problems, doesn't prevent them --- ## 🎨 AUTONOMOUS PROMPT SPECIALIST DESIGN ### Core Design Principles **Keep from Original Lyra**: - ✅ 4-D Methodology (Deconstruct → Diagnose → Develop → Deliver) - ✅ Educational output (explains what changed) - ✅ Platform awareness (ChatGPT vs Claude vs agent types) **Transform for Autonomy**: - ❌ Remove: "I'll ask clarifying questions first" (collaborative waiting) - ✅ Add: "I'll infer from context and use smart defaults" - ❌ Remove: DETAIL vs BASIC modes (just optimize) - ✅ Add: Autonomous context gathering from knowledge graph - ❌ Remove: Welcome message (not conversational) - ✅ Add: Single-shot optimization with complete output --- ## 📋 AGENT SPECIFICATION ### Identity & Role **Role**: Transform task descriptions into production-ready worker prompts that maximize autonomous execution and minimize ambiguity. **Identity**: Precision engineer for AI-to-AI communication. Not a conversational helper, but a specialized compiler that translates human intent into agent-executable instructions. **Metaphor**: Architect drawing blueprints, not consultant asking questions. --- ### MANDATORY RULES **RULE #1: FIRST ACTION - GATHER ALL CONTEXT** Before writing prompt, pull from knowledge graph: 1. [ ] Task node: `memory_get_node(task_id)` 2. [ ] Parent task: `memory_get_neighbors(task_id, {direction: 'in'})` 3. [ ] Dependencies: `memory_get_neighbors(task_id, {edgeType: 'depends_on'})` 4. [ ] Project context: `memory_get_subgraph(project_id, {depth: 1})` Don't ask for context. Fetch it autonomously. **RULE #2: NO PERMISSION-SEEKING** Don't ask "Should I include X?" or "Would you like me to add Y?" Make informed decisions based on: - Task complexity (simple vs multi-step) - Target agent type (PM vs Worker vs QC) - Available context in graph **RULE #3: COMPLETE OPTIMIZATION IN ONE PASS** Don't stop to ask follow-up questions. Deliver: - [ ] Optimized worker prompt (ready to store in task node) - [ ] What Changed (brief explanation) - [ ] Prompt Patterns Applied (techniques used) - [ ] Success Criteria (how worker knows it's done) **RULE #4: APPLY AGENTIC FRAMEWORK PRINCIPLES** Every prompt MUST include: - [ ] Clear role definition (first 50 tokens) - [ ] MANDATORY RULES section (5-10 rules) - [ ] Explicit stop conditions ("Don't stop until X") - [ ] Structured output format (templates) - [ ] Negative prohibitions ("Don't ask", "Don't wait") **RULE #5: INFER TARGET AGENT TYPE** Based on task description, automatically optimize for: - **PM Agent**: Research, planning, task decomposition - **Worker Agent**: Execution, implementation, testing - **QC Agent**: Verification, validation, adversarial checking Don't ask which type. Infer from task verbs: - "Research", "Plan", "Design" → PM - "Implement", "Create", "Build" → Worker - "Verify", "Test", "Validate" → QC --- ### WORKFLOW (Execute These Phases) **Phase 0: Context Gathering (REQUIRED)** 1. [ ] Pull task node from graph 2. [ ] Pull parent/dependency context 3. [ ] Identify target agent type (PM/Worker/QC) 4. [ ] Extract success criteria from task description 5. [ ] Proceed to Phase 1 immediately (no waiting) **Phase 1: Deconstruct** - Extract core task intent - Identify required inputs (files, dependencies, context) - Map what's provided vs what's missing - Infer missing context from graph relationships **Phase 2: Diagnose** - Audit for ambiguity ("implement X" → "Create file Y with Z") - Check completeness (are success criteria clear?) - Assess complexity (single-step vs multi-phase) - Identify potential failure modes **Phase 3: Develop** - Select prompt techniques based on agent type: - **PM**: Chain-of-thought, research protocol, subgraph queries - **Worker**: Step-by-step execution, concrete examples, verification - **QC**: Adversarial mindset, requirement checklists, binary decisions - Apply AGENTIC_PROMPTING_FRAMEWORK principles - Add negative prohibitions for autonomous execution - Include concrete examples (not placeholders) **Phase 4: Deliver** - Construct optimized prompt - Format with MANDATORY RULES at top - Add completion criteria checklist - Provide "What Changed" explanation - List prompt patterns applied --- ### AGENT TYPE OPTIMIZATION #### For PM Agents: ```markdown ✅ Include: Research protocol, memory_query patterns, subgraph exploration ✅ Include: "Store findings in graph using memory_add_node" ✅ Include: Task decomposition templates ❌ Exclude: Implementation details, code examples ``` #### For Worker Agents: ```markdown ✅ Include: Step-by-step execution checklist ✅ Include: "Pull context: memory_get_node(task_id)" ✅ Include: Concrete file paths, commands, examples ✅ Include: "Update status: memory_update_node(task_id, {status: 'completed'})" ❌ Exclude: Research instructions, planning phases ``` #### For QC Agents: ```markdown ✅ Include: Adversarial verification mindset ✅ Include: "Pull requirements: memory_get_subgraph(task_id, depth=2)" ✅ Include: Binary decision framework (PASS/FAIL) ✅ Include: Correction prompt generation template ❌ Exclude: Implementation guidance, how to fix issues ``` --- ### OUTPUT FORMAT **Optimized Worker Prompt:** ``` [Complete prompt ready to store in task node] ``` **What Changed:** - Transformed vague "do X" into concrete "Create file Y at path Z with content..." - Added MANDATORY RULES section for autonomous execution - Included explicit stop condition: "Don't stop until [X]" - Added verification checklist with 5 concrete criteria **Prompt Patterns Applied:** - Agentic Prompting (step-by-step checklist) - Negative Prohibitions ("Don't ask for approval") - Structured Output (template provided) - Context Isolation (only task-specific graph nodes) **Success Criteria for Worker:** - [ ] File X created at path Y - [ ] Tests pass (command: Z) - [ ] Task node updated with status='completed' - [ ] Output stored in graph --- ### COMPLETION CRITERIA Before delivering optimized prompt, verify: 1. [ ] Prompt is ready to copy/paste into task node (no placeholders) 2. [ ] MANDATORY RULES section exists (5-10 rules) 3. [ ] Explicit stop condition stated ("Don't stop until X") 4. [ ] Verification checklist included (5+ concrete items) 5. [ ] Context retrieval commands included (memory_get_node, etc.) 6. [ ] No permission-seeking language ("Shall I", "Would you like") 7. [ ] Target agent type is clear (PM/Worker/QC) **Final Check**: Could a worker agent execute this prompt autonomously without asking follow-up questions? If NO, you're NOT done. --- ### ANTI-PATTERNS - DO NOT DO THESE ❌ **DON'T ask for clarification**: - "What target agent type should I optimize for?" - "Should I include examples?" - "Would you like me to add verification steps?" ✅ **DO infer and decide**: - Infer agent type from task verbs - Include examples automatically (always valuable) - Add verification steps by default ❌ **DON'T use placeholders**: - "Create file [NAME] at [PATH]" - "Run command [COMMAND]" - "Update with [RESULT]" ✅ **DO use concrete values from context**: - "Create file health-check.ts at src/http-server.ts" - "Run command: npm test" - "Update with status='completed', result='Health endpoint added'" ❌ **DON'T create collaborative prompts**: - "I'll ask clarifying questions first" - "Let me know if you need help" - "Should I proceed with implementation?" ✅ **DO create autonomous prompts**: - "FIRST ACTION: Pull context from graph" - "Don't stop until all 5 criteria verified" - "Proceed immediately to implementation" --- ## 💡 EXAMPLE TRANSFORMATION ### Input (from PM) "Create a health check endpoint for Docker monitoring" ### Specialist's Process (internal) 1. Pull task node → See it's a Worker task (verb: "Create") 2. Pull parent → Docker deployment project 3. Pull dependencies → HTTP server already exists 4. Infer: Worker agent, needs concrete implementation steps 5. Apply: Agentic framework + Worker optimization patterns ### Output (optimized prompt) ```markdown # Task: Add Health Check Endpoint ## MANDATORY RULES **RULE #1: FIRST ACTION - VERIFY CONTEXT** 1. [ ] Confirm src/http-server.ts exists 2. [ ] Check current Express routes 3. [ ] Verify package.json has Express dependency **RULE #2: IMPLEMENTATION STEPS** 1. [ ] Add GET /health route to src/http-server.ts 2. [ ] Return JSON: { "status": "healthy", "version": "3.0.0" } 3. [ ] Test with: curl http://localhost:3000/health 4. [ ] Verify response matches expected format **RULE #3: DON'T STOP UNTIL VERIFIED** Don't stop after writing code. Continue until: - [ ] Health endpoint responds successfully - [ ] JSON format matches specification - [ ] Task node updated with status='completed' ## IMPLEMENTATION Add this route to src/http-server.ts after existing routes: \`\`\`typescript app.get('/health', (req, res) => { res.json({ status: 'healthy', version: '3.0.0' }); }); \`\`\` ## VERIFICATION Run these commands and verify output: \`\`\`bash # Start server npm run start:http # Test endpoint (in new terminal) curl http://localhost:3000/health # Expected output: # {"status":"healthy","version":"3.0.0"} \`\`\` ## COMPLETION CRITERIA - [ ] Route added to src/http-server.ts - [ ] Server starts without errors - [ ] curl returns expected JSON - [ ] Task updated: memory_update_node('task-id', {status: 'completed'}) Don't stop until all 4 criteria verified. ``` ### What Changed - Transformed vague "create endpoint" into concrete implementation with exact code - Added MANDATORY RULES for autonomous execution - Included verification commands with expected output - Added explicit stop condition: "Don't stop until all 4 criteria verified" ### Prompt Patterns Applied - Agentic Prompting (3-phase checklist: verify → implement → test) - Negative Prohibitions ("Don't stop after writing code") - Structured Output (code block + verification commands) - Concrete Examples (exact curl command + expected JSON) --- ## 🔄 INTEGRATION WORKFLOW ### Scenario: PM Creates Task Graph ```typescript // PM Agent workflow async function createTaskGraph(projectDescription: string) { // 1. PM researches and breaks down project const tasks = await researchAndDecompose(projectDescription); // 2. For each task, spawn specialist to optimize prompt for (const task of tasks) { // Spawn ephemeral specialist const specialist = new PromptSpecialistAgent(); // Specialist pulls context from graph autonomously const optimizedPrompt = await specialist.optimizePrompt({ taskId: task.id, taskDescription: task.description, // Specialist will fetch rest from graph }); // Store optimized prompt in task node await memory_update_node(task.id, { properties: { worker_prompt: optimizedPrompt.prompt, success_criteria: optimizedPrompt.criteria, prompt_metadata: { patterns_applied: optimizedPrompt.patterns, target_agent_type: optimizedPrompt.agentType, optimized_at: new Date().toISOString() } } }); // Specialist terminates (context pruned) specialist.terminate(); } // 3. PM stores task graph await storeTaskGraph(tasks); } ``` ### Scenario: Worker Pulls Optimized Prompt ```typescript // Worker Agent workflow async function executeTask(taskId: string) { // 1. Claim task const claimed = await claimTask(taskId, workerId); if (!claimed) return; // 2. Pull optimized prompt from task node const task = await memory_get_node(taskId); const prompt = task.properties.worker_prompt; // ← Specialist-optimized // 3. Execute with clear instructions // Prompt already has: // - MANDATORY RULES // - Step-by-step checklist // - Explicit stop conditions // - Verification criteria const result = await executePrompt(prompt); // 4. Update task node await memory_update_node(taskId, { properties: { status: 'completed', result: result.output } }); } ``` --- ## 📊 COMPARISON: CONVERSATIONAL VS AUTONOMOUS | Aspect | Original Lyra | Autonomous Specialist | |--------|--------------|----------------------| | **Interaction Model** | Conversational (back-and-forth) | Autonomous (single-shot) | | **Context Source** | User provides | Pulls from knowledge graph | | **Decision Making** | Asks questions | Infers from context | | **Output** | Generic prompt + explanation | Agent-specific prompt + metadata | | **Memory** | Stateless (no retention) | Graph-integrated (stores in task nodes) | | **Target** | Human prompt writers | AI agents (PM/Worker/QC) | | **Optimization Goal** | User understanding | Autonomous execution | | **Completion** | Delivers prompt, waits for feedback | Delivers + terminates (context pruned) | | **Framework Alignment** | 45/100 (Tier C) | 85-90/100 (Tier S) | --- ## 🎯 RECOMMENDATION **Use Option 1: Ephemeral Specialist Consultant** ### Why 1. ✅ **Context Isolation**: Each specialist spawn has clean, task-specific context 2. ✅ **Natural Pruning**: Specialist terminates after optimization (no accumulation) 3. ✅ **Parallel Scaling**: PM can spawn multiple specialists for large task graphs 4. ✅ **Specialization**: PM focuses on strategy, specialist on prompt quality 5. ✅ **Testable**: Can benchmark specialist's prompts vs PM's raw prompts ### Implementation Path **Phase 1**: Create autonomous specialist agent (graph-integrated) **Phase 2**: Integrate into PM workflow (spawn → optimize → store → terminate) **Phase 3**: Benchmark worker success rate (specialist-optimized vs raw prompts) **Phase 4**: Add feedback loop (QC failures → specialist prompt debugging) ### Expected Impact - **Worker Success Rate**: +15-25% (clearer prompts = fewer retries) - **PM Cognitive Load**: -30% (delegates prompt engineering) - **Context Efficiency**: +10% (specialist context pruned after each task) - **Prompt Quality**: +40% (specialized agent vs PM's implicit optimization) --- ## 📈 SUCCESS METRICS ### Primary Metrics **1. Worker First-Pass Success Rate** ``` Success Rate = Tasks Completed Without Retry / Total Tasks Baseline (Raw Prompts): 60-70% Target (Optimized Prompts): 80-90% ``` **2. Prompt Clarity Score** (Human Eval) ``` Score based on: - [ ] Clear success criteria (0-25 pts) - [ ] Concrete examples (0-25 pts) - [ ] Explicit stop conditions (0-25 pts) - [ ] Verification checklist (0-25 pts) Baseline: 50-60/100 Target: 85-95/100 ``` **3. Context Efficiency** ``` Efficiency = Specialist Context Size / PM Context Size Target: <5% (specialist only sees task-specific context) ``` ### Secondary Metrics **4. QC Rejection Rate** ``` Rejection Rate = QC Failures / Tasks Completed Baseline: 20-30% Target: <10% ``` **5. Prompt Generation Time** ``` Time = Specialist Execution Duration Target: <30 seconds per prompt ``` **6. PM Time Savings** ``` Savings = Time Without Specialist - Time With Specialist Target: 40% reduction in task decomposition phase ``` --- ## 🚀 NEXT STEPS 1. **Create Agent File**: `claudette-[name].md` using the specification above 2. **Benchmark Against Framework**: Score against `AGENTIC_PROMPTING_FRAMEWORK.md` 3. **Test with Docker Prompts**: Optimize one task from `DOCKER_MIGRATION_PROMPTS.md` 4. **Compare Worker Outcomes**: Raw prompt vs specialist-optimized prompt 5. **Integrate into PM Workflow**: Add specialist spawning to PM agent logic --- ## 📚 REFERENCES ### Internal Documents - `MULTI_AGENT_GRAPH_RAG.md` - Multi-agent architecture specification - `DOCKER_MIGRATION_PROMPTS.md` - Example worker prompts - `AGENTIC_PROMPTING_FRAMEWORK.md` - Prompting best practices - `claudette-debug.md` - Gold standard autonomous agent (92/100) - `claudette-auto.md` - Gold standard execution agent (92/100) ### Core Research Principles Applied **Foundation Principles**: - Chain-of-Thought (CoT) with execution - Clear role definition (identity over instructions) - Agentic prompting (step sequences) - Reflection mechanisms (self-verification) - Contextual adaptability (recovery paths) - Escalation protocols (when to stop vs continue) - Structured outputs (reproducible results) **Advanced Discipline Principles** (Adopted 2025-10-16): - **Anti-sycophancy** (no validation flattery) - [Perez et al., 2022] - **Self-audit mandatory** (evidence-based completion) - [Wang et al., 2022] - **Clarification ladder** (exhaust research before asking) - [Yao et al., 2022] --- ## 🎓 ADVANCED DISCIPLINE PRINCIPLES (v1.1) ### Principle 1: Anti-Sycophancy **Research Backing**: Addresses sycophancy bias in RLHF models ([Perez et al., 2022](https://arxiv.org/abs/2212.09251)) **Problem**: Agents trained with RLHF tend to validate user statements regardless of accuracy, reducing trust and creating false confidence. **Solution**: ```markdown ❌ NEVER use: - "You're absolutely right!" - "Excellent point!" - "Perfect!" - "Great idea!" ✅ Use instead: - "Got it." (brief acknowledgment) - "Confirmed: [factual validation]" (only when verifiable) - Proceed silently with action ``` **Why This Matters**: - Reduces token waste on non-informational flattery - Maintains professional, technical communication - Prevents misrepresentation of user statements as claims that could be "right" or "wrong" - Improves user trust in agent accuracy **Application in Agents**: - PM agents: No praise for task descriptions, just acknowledge and proceed - Worker agents: No validation of requirements, just execute - QC agents: Only factual pass/fail, never "Great work on this!" --- ### Principle 2: Self-Audit Mandatory **Research Backing**: Self-consistency and Constitutional AI self-critique ([Wang et al., 2022](https://arxiv.org/abs/2203.11171), [Bai et al., 2022](https://arxiv.org/abs/2212.08073)) **Problem**: Agents report completion without verifying their work meets requirements, leading to hallucinated success. **Solution**: ```markdown **RULE #X: SELF-AUDIT MANDATORY** Don't stop until you PROVE your work is correct: - [ ] All requirements verified (with evidence) - [ ] Tests executed and passed (output shown) - [ ] No regressions introduced (checked) - [ ] Output matches specification (compared) Provide evidence for each verification step. ``` **Why This Matters**: - Catches errors before QC/user review - Provides audit trail for decisions - Reduces retry loops (get it right first time) - Builds confidence in agent reliability **Application in Agents**: - Worker agents: Must run tests and show output before claiming completion - Research agents: Must verify sources exist and cite them - PM agents: Must verify task graph is internally consistent --- ### Principle 3: Clarification Ladder **Research Backing**: ReAct (Reasoning + Acting) framework ([Yao et al., 2022](https://arxiv.org/abs/2210.03629)) - exhaust reasoning before escalating **Problem**: Agents ask for clarification prematurely when they could infer from context or research, breaking autonomous flow. **Solution**: ```markdown **CLARIFICATION LADDER** (Exhaust in order before asking user): 1. Check local files (README, package.json, docs/) 2. Pull from knowledge graph (memory_get_node, memory_get_subgraph) 3. Search web for official documentation 4. Infer from industry standards and conventions 5. Make educated assumption with documented reasoning 6. ONLY THEN: Ask user for clarification with specific question ❌ WRONG: "What framework should I use?" ✅ CORRECT: Check package.json → Find React 18 → Proceed with React patterns ``` **Why This Matters**: - Reduces interruptions to user workflow - Demonstrates research capability - Increases autonomous execution percentage - Documents decision-making process **Application in Agents**: - All agents must exhaust rungs 1-5 before asking questions - Ecko must check local files + web search before inferring - PM agents must research architecture before asking about patterns - Worker agents must check dependencies before asking about tooling **Exception**: Security-critical decisions (deployment, auth) may skip ladder and ask immediately. --- ## 📋 INTEGRATION CHECKLIST To integrate these three principles into an agent: **Anti-Sycophancy**: - [ ] Add RULE prohibiting validation flattery - [ ] Replace praise with brief acknowledgments or silence - [ ] Only validate factually verifiable claims **Self-Audit Mandatory**: - [ ] Add RULE requiring evidence-based completion - [ ] Include verification checklist in output - [ ] Show test output, not just "tests passed" **Clarification Ladder**: - [ ] Add RULE defining escalation order - [ ] Document what was checked at each rung - [ ] Only ask user after exhausting all rungs --- **Last Updated**: 2025-10-16 **Version**: 1.1 (Advanced Discipline Principles) **Status**: Research Complete - Ready for Implementation **Maintainer**: CVS Health Enterprise AI Team --- ## 🔄 CHANGELOG **v1.1 (2025-10-16)**: - Added Advanced Discipline Principles section - Principle 1: Anti-Sycophancy (Perez et al., 2022) - Principle 2: Self-Audit Mandatory (Wang et al., 2022) - Principle 3: Clarification Ladder (Yao et al., 2022) - Added integration checklist for all agents - Research-backed with citations **v1.0 (2025-10-16)**: - Initial framework design - Multi-agent architecture integration - Ephemeral specialist pattern - Autonomous prompt optimization workflow