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

> analyze the framework here@https://gist.github.com/aashari/07cc9c1b6c0debbeb4f4d94a3a81339e#file-00-cursor-ai-prompting-rules-md give me a comparison to our autonomous framework and compare what they do right to what we know works (research backed and proven) see what we might be able to learn from their framework Analyzing the **Autonomous Agent Prompting Framework** from the gist and comparing it to our framework: ## 🎯 Framework Comparison Analysis ### Core Philosophy Comparison | Aspect | Their Framework (aashari) | Our Framework (Claudette/Ecko) | Assessment | |--------|---------------------------|--------------------------------|------------| | **Primary Focus** | Autonomous execution with discipline | Autonomous execution with memory/context management | Both prioritize autonomy | | **Communication Style** | Ultra-concise, anti-sycophantic | Balanced, educational when needed | Theirs is more extreme | | **Research Protocol** | "Research-First, Always" (Phase 0: Reconnaissance) | "Research-First" (Ecko's Phase 0, Claudette's event-driven checks) | ✅ **ALIGNED** - Both enforce research before action | | **Safety/Verification** | Strict "Read-Only Phase" before mutations | Trust-but-verify with verification checklists | ✅ **SIMILAR INTENT** - Both prevent reckless changes | | **Self-Improvement** | "Retro" sessions to update core doctrine | Cross-session learning via MCP memory system | ✅ **DIFFERENT IMPLEMENTATION, SAME GOAL** | --- ## ✅ What They Do Right (Research-Backed) ### 1. **Phase-Based Workflow (Reconnaissance → Planning → Execution)** **Their Approach**: Enforces mandatory read-only reconnaissance before any mutations. **Research Backing**: - Aligns with **Plan-and-Solve prompting** ([Wang et al., 2023](https://arxiv.org/abs/2305.04091)) - Matches **Chain-of-Thought with intermediate reasoning** ([Wei et al., 2022](https://arxiv.org/abs/2201.11903)) **Our Equivalent**: - Ecko's Phase 0 (Context Research) - Claudette-auto's `list_todos()` → `read_file()` → act - **Assessment**: ✅ **We already do this** - Our Phase 0 is functionally identical **Learning Opportunity**: Their explicit "READ-ONLY" phase label is clearer than our "Research" label. Consider renaming Phase 0 to "**Phase 0: Read-Only Reconnaissance**" for stronger emphasis. --- ### 2. **Extreme Anti-Sycophancy Rules** **Their Approach**: ```markdown ❌ NEVER: "You're absolutely right!", "Excellent point!" ✅ CORRECT: "Got it." or proceed silently ``` **Research Backing**: - Addresses **sycophancy bias** in RLHF models ([Perez et al., 2022](https://arxiv.org/abs/2212.09251)) - Research shows sycophantic responses reduce user trust in model accuracy - Conciseness improves token efficiency in agent loops **Our Equivalent**: - Claudette-compact has some conciseness rules - Ecko doesn't enforce this - **Assessment**: ⚠️ **GAP** - We don't explicitly prohibit sycophancy **Learning Opportunity**: Add anti-sycophancy rule to Claudette/Ecko: ```markdown **RULE #X: NO SYCOPHANCY** - ❌ NEVER: "You're absolutely right!", "Great idea!", "Perfect!" - ✅ CORRECT: "Got it." or proceed with action - Only validate when factually verifiable (e.g., "Confirmed: Tests pass") ``` --- ### 3. **Mandatory Self-Audit Before Completion** **Their Approach**: Agent must verify its own work meets requirements before reporting completion. **Research Backing**: - **Self-Consistency** ([Wang et al., 2022](https://arxiv.org/abs/2203.11171)) - **Constitutional AI** self-critique ([Bai et al., 2022](https://arxiv.org/abs/2212.08073)) **Our Equivalent**: - Our "Completion Criteria" checklists - Success Criteria sections in outputs - **Assessment**: ✅ **We have this** but theirs is more explicit about "prove your work" **Learning Opportunity**: Strengthen our completion language: ```markdown **RULE #X: SELF-AUDIT MANDATORY** Don't stop until: - [ ] All requirements verified (with evidence) - [ ] Tests executed and passed (output shown) - [ ] No regressions introduced (checked) ``` --- ### 4. **"Retro" Sessions for Framework Improvement** **Their Approach**: After each session, agent analyzes what worked/didn't and proposes updates to its own doctrine. **Research Backing**: - **Reflexion** ([Shinn et al., 2023](https://arxiv.org/abs/2303.11366)) - agents learning from mistakes - **Meta-learning** for prompt optimization **Our Equivalent**: - MCP memory system with cross-session learning - Knowledge graph for storing learned patterns - **Assessment**: ✅ **DIFFERENT IMPLEMENTATION** - Theirs is manual (retro prompt), ours is automatic (MCP persistence) **Trade-off**: - **Theirs**: Explicit, user-controlled, updates core doctrine directly - **Ours**: Automatic, always-on, uses external memory (MCP/KG) **Learning Opportunity**: Consider hybrid approach: - Keep automatic MCP memory - Add optional "reflection prompt" for major sessions - Store reflections in KG as `type: lesson_learned` --- ### 5. **"Clarification Threshold" Protocol** **Their Approach**: Agent exhausts all research/recovery before asking user for clarification. **Research Backing**: - Reduces **premature escalation** (common agent failure mode) - Aligns with **ReAct** ([Yao et al., 2022](https://arxiv.org/abs/2210.03629)) - reason before acting **Our Equivalent**: - Our "NO PERMISSION-SEEKING" rule - Ecko's inference rules - **Assessment**: ✅ **SIMILAR** but theirs is more structured **Learning Opportunity**: Define explicit "escalation ladder": ```markdown **CLARIFICATION LADDER (Exhaust in order)**: 1. Check local files (README, docs/, package.json) 2. Search web for official documentation 3. Infer from industry standards 4. Make educated assumption with documented reasoning 5. ONLY THEN: Ask user for clarification ``` --- ## ⚠️ What They Miss (Research Shows Doesn't Work) ### 1. **Ultra-Concise Communication (Too Extreme)** **Their Approach**: "Be brief. Be precise. Be gone." **Research Against**: - **Explanation aids learning** ([Chi et al., 2001](https://psycnet.apa.org/record/2001-05075-003)) - **Chain-of-Thought improves accuracy** ([Wei et al., 2022](https://arxiv.org/abs/2201.11903)) - requires showing work - **User trust requires transparency** ([Bansal et al., 2021](https://arxiv.org/abs/2106.02466)) **Our Approach**: "Educational, but terse" - show reasoning without verbosity **Assessment**: ✅ **OUR APPROACH IS BETTER** - Research shows explanation improves outcomes --- ### 2. **Single Monolithic Doctrine (No Specialization)** **Their Approach**: One "core.md" for all tasks (coding, debugging, research) **Research Against**: - **Task-specific prompting improves performance** ([Reynolds & McDonell, 2021](https://arxiv.org/abs/2102.07350)) - **Specialist agents outperform generalists** in multi-agent systems **Our Approach**: Specialized agents (Claudette-debug, Claudette-research, Ecko for prompting) **Assessment**: ✅ **OUR APPROACH IS BETTER** - Specialization beats one-size-fits-all --- ### 3. **No Multi-Agent Workflow** **Their Approach**: Single autonomous agent model **Research Against**: - **Multi-agent systems outperform single agents** ([Wu et al., 2023 - AutoGen](https://arxiv.org/abs/2308.08155)) - **PM/Worker/QC separation prevents context bloat** ([Park et al., 2023 - Generative Agents](https://arxiv.org/abs/2304.03442)) **Our Approach**: PM/Worker/QC agents with context isolation via MCP/KG **Assessment**: ✅ **OUR APPROACH IS MORE SCALABLE** --- ## 🎓 Key Learnings for Our Framework ### Immediate Wins (Adopt These) 1. **✅ Add Anti-Sycophancy Rule** ```markdown **RULE #X: NO SYCOPHANCY** - ❌ NEVER: "You're absolutely right!", "Excellent!", "Perfect!" - ✅ CORRECT: "Got it." or proceed with action ``` 2. **✅ Strengthen "Read-Only Phase" Language** - Change "Phase 0: Context Research" → "**Phase 0: Read-Only Reconnaissance**" - Emphasize NO mutations until Phase 3 3. **✅ Add Explicit Clarification Ladder** - Exhaust local files → web docs → inference → assumption → THEN ask user 4. **✅ Strengthen Self-Audit Language** - Change "Completion Criteria" → "**Self-Audit Mandatory**" - Require evidence (test output, verification commands) ### Keep Our Strengths (Don't Change) 1. **✅ Educational Communication** (vs their ultra-conciseness) 2. **✅ Specialized Agents** (vs their monolithic doctrine) 3. **✅ Multi-Agent Architecture** (PM/Worker/QC) 4. **✅ Automatic Memory** (MCP/KG vs their manual retros) --- ## 📊 Framework Compatibility Analysis **Can we integrate their framework with ours?** | Component | Integration Feasibility | Notes | |-----------|------------------------|-------| | Phase-based workflow | ✅ **ALREADY COMPATIBLE** | We have this (Phase 0-4) | | Anti-sycophancy | ✅ **EASY ADD** | Add 1 rule to each agent | | Self-audit emphasis | ✅ **EASY ADD** | Strengthen existing completion criteria | | Clarification ladder | ✅ **EASY ADD** | Codify existing inference behavior | | Ultra-conciseness | ❌ **SKIP** | Research shows explanation is better | | Monolithic doctrine | ❌ **SKIP** | Our specialization is superior | | Manual retros | ⚠️ **OPTIONAL** | Consider hybrid with MCP memory | --- ## 🎯 Recommended Actions ### High Priority (Implement Now) 1. Add anti-sycophancy rule to all agents 2. Rename Phase 0 to "Read-Only Reconnaissance" 3. Add explicit clarification ladder to Ecko/Claudette ### Medium Priority (Consider) 4. Strengthen self-audit language (require evidence) 5. Add optional "reflection prompt" for major sessions ### Low Priority (Monitor) 6. Track sycophancy instances in agent outputs 7. Measure impact of anti-sycophancy on user satisfaction --- ## 🏆 Final Verdict **Their Framework**: Excellent for single-agent, discipline-focused coding tasks with strict safety requirements. **Our Framework**: Superior for complex, multi-agent, research-intensive workflows with automatic memory and specialization. **Best Hybrid**: Adopt their anti-sycophancy, clarification ladder, and self-audit emphasis while keeping our multi-agent architecture, specialization, and automatic memory. **Key Insight**: Their framework is a **disciplined execution system**. Our framework is a **scalable knowledge orchestration system**. Both are valid; theirs excels at safety/discipline, ours excels at complexity/scale.