# Agent Orchestration Platform - Problem Statement & Benchmarks ## Problem Statement ✅ ### Core Challenge Develop an orchestration platform that demonstrates measurable improvements in **accuracy**, **wall-clock time**, and most importantly **human time required** for complex tasks by intelligently coordinating multiple specialized agents, with results compelling enough for a technical raise. ### Success Criteria - Beat single-agent baselines on accuracy AND speed - **Dramatically reduce human time investment (>80% reduction)** - Show clear architectural advantages - Demonstrate learning/improvement over time - Have compelling visualizations of agent collaboration - Show human-in-the-loop advantages with minimal human effort ## What is Agent Orchestration? ✅ **The Core Concept**: Instead of one LLM trying to do everything in a single context, we coordinate multiple specialized agents that each handle part of the problem. Think of it like the difference between: - **Single Agent**: One person trying to build a house alone - **Orchestrated Agents**: A construction crew with specialists (electrician, plumber, carpenter) coordinated by a foreman **Why Orchestration?** 1. **Context Window Limits**: Can't fit entire codebases in one prompt 2. **Specialization**: Different agents can be optimized for different tasks 3. **Parallelization**: Multiple agents can work simultaneously 4. **Error Isolation**: One agent's mistake doesn't contaminate everything 5. **Human Time**: Humans can supervise at high level vs doing everything **What is an "agent"?**: For now, different prompts to LLMs. Future: could be different models, fine-tuned variants, or even humans. ## The DAG-Based Orchestration Process 🤔 ### Core Process Flow ```mermaid graph TD A[Input: Fix bug #123] --> B[Model 1: DAG Generator] B --> C{DAG with Optional Nodes} C --> D[Core Task: Understand Bug] C --> E[Core Task: Write Fix] C --> F[Optional: Review Fix] C --> G[Core Task: Write Tests] C --> H[Optional: Verify Tests] C --> I[Core Task: Deploy] D --> J[Model 2: Performance Predictor] E --> J F --> J G --> J H --> J I --> J J --> K[Performance Matrix:<br/>Each task × agent combination] K --> L[Model 3: Assignment Optimizer] M[Constraints:<br/>- 95% quality required<br/>- $50 budget<br/>- 30 min deadline] --> L L --> N[Optimal Path:<br/>Include Review, Skip Verify<br/>Assign GPT-4 to code<br/>Human reviews critical parts] N --> O[Model 4: Prompt Optimizer] O --> P[Customized prompts<br/>for each agent-task pair] style A fill:#f9f,stroke:#333,stroke-width:2px style N fill:#ff9,stroke:#333,stroke-width:2px style P fill:#9f9,stroke:#333,stroke-width:2px ``` ### Key Innovation: Review as Optional Path Traditional: Every task goes through review (slow, expensive) Our approach: Review only when it improves expected value ```mermaid graph LR subgraph "Path A: No Review" A1[Write Code] --> A2[Test] A2 --> A3[85% success<br/>20 min total] end subgraph "Path B: With Review" B1[Write Code] --> B2[Human Review] B2 --> B3[Test] B3 --> B4[98% success<br/>25 min total<br/>5 min human] end ``` The Assignment Optimizer chooses paths based on your constraints! ### Parallel Execution Timeline ```mermaid gantt title Single Agent vs Orchestrated Execution dateFormat HH:mm axisFormat %M min section Single Agent Understand Bug :done, single1, 00:00, 10m Write Fix :done, single2, after single1, 20m Write Tests :done, single3, after single2, 15m Update Docs :done, single4, after single3, 10m Integration Test :done, single5, after single4, 5m section Orchestrated Understand Bug :done, orch1, 00:00, 5m Write Fix :active, orch2, after orch1, 15m Human Review :crit, orch3, after orch2, 3m Write Tests :active, orch4, after orch1, 12m Update Docs :active, orch5, after orch1, 8m Integration Test :orch6, 00:23, 2m ``` ## The Four Core Models 🤔 ### Model 1: DAG Generator **What it is**: Decomposes tasks into subtasks with dependencies **Input**: Task description **Output**: DAG with task nodes (including optional review/verify nodes) **Key Innovation**: Review nodes are optional - included based on task complexity ### Model 2: Performance Predictor **What it is**: Predicts (success probability, time, cost) for any (task, agent) pair **Input**: Task description + Agent description **Output**: P(success), expected_time, cost **Works for any agent**: Human reviewers, AI models, specialized tools ### Model 3: Assignment Optimizer **What it is**: Finds optimal agent-to-task mapping given constraints **Input**: - DAG with all possible paths (including optional reviews) - Available agents with their predicted performance - Constraints (deadlines, budgets, quality requirements) **Output**: Optimal assignment + which optional nodes to include **Key Innovation**: Treats review/verification as optional nodes in path optimization ```python # Example optimization Given DAG: Code → [Review?] → Test → [Verify?] → Deploy If Review adds 5 min human time but increases success from 85% to 98%: - Include if: Quality critical OR Human time available - Skip if: Speed critical AND Risk acceptable ``` ### Model 4: Prompt Optimizer **What it is**: Generates optimal prompts for each (task, agent) pairing **Input**: Task details + Assigned agent characteristics **Output**: Customized prompt maximizing success probability **Example**: - Task: "Fix authentication bug" - Agent: "GPT-4" - Optimized prompt: Includes codebase context, specific error logs, testing requirements ## How They Work Together ```mermaid graph TD A[Task: Fix bug #123] --> B[Model 1: DAG Generator] B --> C[DAG with optional review nodes] C --> D[Model 2: Performance Predictor] E[Available Agents] --> D D --> F[Performance Matrix] F --> G[Model 3: Assignment Optimizer] H[Constraints] --> G G --> I[Optimal Assignment + Path] I --> J[Model 4: Prompt Optimizer] J --> K[Execute with optimized prompts] style A fill:#f9f,stroke:#333,stroke-width:2px style K fill:#9f9,stroke:#333,stroke-width:2px ``` ## Future Enhancement: End-to-End Differentiable System 🤔 **Current Approach**: 4 independent models, optimized separately **Future Vision**: Single differentiable system optimizing globally **Why not start with E2E?** - Requires massive training data - Hard to debug failures - Complex implementation (Gumbel-softmax, differentiable graphs) - Difficult to explain to stakeholders **Path Forward**: 1. Launch with modular 4-model system 2. Collect execution data 3. Pre-train E2E system using successful traces 4. Gradually transition once proven **The E2E system would learn**: - Implicit review strategies (when to review without being told) - Cross-task dependencies we didn't anticipate - Agent collaboration patterns - Prompt strategies that emerge from data But for MVP: **Ship the 4-model system that works!** ## The Improvement Paradigm 🤔 ### How We Show Continuous Improvement 1. **Online Learning Loop**: ```mermaid graph TD A[Task Execution] --> B[Log Performance] B --> C{Compare Predicted vs Actual} C --> D[Update Agent Model] C --> E[Identify Good DAG Patterns] D --> F[Better Agent Assignment] E --> G[Better Task Decomposition] F --> A G --> A ``` 2. **Metrics That Improve Over Time**: - **Week 1**: 65% SWE-Bench solve rate, 45 min avg - **Week 2**: 70% solve rate, 40 min avg (model learns agent strengths) - **Week 3**: 73% solve rate, 35 min avg (better DAGs) - **Week 4**: 75% solve rate, 30 min avg (optimal human placement) 3. **A/B Testing Built In**: - Try different DAGs for same task type - Try different agent assignments - Measure and adopt winning strategies ### Learning New Agents Automatically When a new agent joins the system: ```mermaid sequenceDiagram participant S as System participant N as New Agent (Gemini) participant M as Performance Model Note over S,N: Day 1: Agent Added S->>N: Try simple task N->>S: Complete (8 min) S->>M: Update: Gemini + simple task = success, 8 min Note over S,N: Day 2-3: Exploration S->>N: Try complex debugging N->>S: Partial success (45 min) S->>M: Update performance data S->>N: Try documentation task N->>S: Complete (5 min) S->>M: Update: Gemini great at docs! Note over S,N: Day 5: Specialization Emerges S->>S: Gemini now auto-assigned to doc tasks S->>S: Overall system 8% faster ``` **Key Innovation**: No manual configuration needed - system discovers agent capabilities through experience! ## MVP Definition 🤔 ### Phase 1: Beat SWE-Bench (Week 1-2) **Goal**: Show superior performance on standard benchmark **Deliverables**: - DAG generator that decomposes SWE-Bench tasks - Agent performance model trained on initial data - Basic parallel executor - **Target**: 70% solve rate (vs. 65% baseline), 2x faster **Demo**: - Live solving of SWE-Bench problem - Show parallel execution visualization - Show time savings ### Phase 2: Human-in-the-Loop Excellence (Week 3) **Goal**: Show optimal human utilization **Deliverables**: - Human performance predictions in agent model - ROI-based human injection points - **Target**: 85% solve rate with avg 5 min human time **Demo**: - "Watch a developer solve 10 tickets in 1 hour" - Show human only reviews critical decisions - Compare to 3+ hours doing it manually ### Phase 3: Self-Improving System (Week 4) **Goal**: Show the system getting better **Deliverables**: - Online learning pipeline - Performance tracking dashboard - **Target**: 5% improvement week-over-week **Demo**: - Show performance graphs over time - Show agent specialization emerging - Show DAGs getting more efficient ### Phase 4: Dynamic Agent Addition (Week 5) **Goal**: Show system adapts to new team members/agents **Scenario**: "We just got access to Gemini-Ultra with code execution" ```mermaid graph LR subgraph "Before New Agent" T1[Complex Debug Task] --> A1[Claude: 70% success] T1 --> A2[GPT-4: 75% success] T1 --> A3[Human: 95% success] end subgraph "After Adding Gemini" T2[Complex Debug Task] --> B1[Claude: 70% success] T2 --> B2[GPT-4: 75% success] T2 --> B3[Human: 95% success] T2 --> B4[Gemini: ???] end subgraph "After 50 Tasks" T3[Complex Debug Task] --> C1[Claude: 70% success] T3 --> C2[GPT-4: 75% success] T3 --> C3[Human: 95% success] T3 --> C4[Gemini: 82% success] end style B4 fill:#ff9,stroke:#333,stroke-width:2px style C4 fill:#9f9,stroke:#333,stroke-width:2px ``` **Demo Flow**: 1. Add new agent with just a description: "Gemini-Ultra with code execution capabilities" 2. System automatically tries it on various subtasks 3. Within 10-20 tasks, learns its strengths/weaknesses 4. Show Gemini being auto-assigned to tasks it's good at 5. Show overall system performance improving **Live Examples**: *Adding a New AI Agent*: ```json { "agent_description": "Anthropic Claude 3 with vision capabilities", "initial_capabilities": "unknown" } ``` → System discovers: Great at UI/UX tasks, visual debugging, screenshot analysis *Adding a New Human Team Member*: ```json { "agent_description": "Junior developer, 2 years Python experience, Emily", "initial_capabilities": "unknown" } ``` → System discovers: Fast at Python scripts, needs review on architecture decisions **Results After 1 Week**: - System knows each agent's strengths/weaknesses - Automatically assigns appropriate tasks - Creates optimal pairings (e.g., Emily + Senior for architecture) - Overall team velocity increased 15% **Key Message**: "Plug in any new AI model or hire any new developer - the system adapts automatically" ## Why This Wins 🤔 ### For Investors 1. **Clear Metrics**: "We beat SWE-Bench by 10% while being 2x faster" 2. **Defensible**: "Our models improve with every task executed" 3. **Scalable**: "Works with any agents - GPT-4, Claude, Gemini, humans" 4. **Business Model**: "Every execution makes the system better" 5. **Future-Proof**: "When GPT-5 launches, just plug it in - no re-engineering" ### For Developers 1. **10x Productivity**: "Review 10 PRs in the time it takes to write 1" 2. **Focus on Interesting**: "AI handles boilerplate, you handle architecture" 3. **Gets Better**: "System learns your preferences and strengths" 4. **Team Integration**: "New team member? System learns their strengths in days" ### Technical Differentiation 1. **Novel Architecture**: DAG-based orchestration with learned optimization 2. **Human-AI Synthesis**: Humans and AI agents in same framework 3. **Online Learning**: Continuously improving, not static 4. **Parallel-First**: Built for speed, not just accuracy 5. **Agent-Agnostic**: Any LLM, any human, same system ### The "New Agent" Superpower ```mermaid graph TD A[New AI Model Released] --> B{Traditional Approach} A --> C{Our Approach} B --> D[Rewrite Prompts] D --> E[Retune System] E --> F[Test Everything] F --> G[Deploy - 2-4 weeks] C --> H[Add Agent Description] H --> I[System Auto-Explores] I --> J[Learns Strengths] J --> K[Auto-Optimizes - 2-3 days] style G fill:#fbb,stroke:#333,stroke-width:2px style K fill:#9f9,stroke:#333,stroke-width:2px ``` **Pitch**: "Every new AI model makes our system stronger, automatically" ## Bootstrap Strategy 🤔 ### Week 0: Data Collection - Run 100 SWE-Bench problems manually - Log everything: times, success rates, DAG structures - Get baseline metrics ### Week 1: Model Training - Train agent performance model on collected data - Train DAG generator on successful patterns - Build basic parallel executor ### Week 2: MVP Demo - Beat SWE-Bench baseline - Show parallel execution - Basic visualizations ### Week 3: Human Integration - Add human performance predictions - Show optimal human utilization - "10x developer" demo ### Week 4: Learning Loop - Implement online learning - Show improvement over time - Prepare investor demo ### Week 5: Dynamic Agent Demo - Live demo: Add new agent (e.g., Gemini) during presentation - Show system automatically learning its capabilities - Demonstrate performance improvement in real-time - "Any agent, anytime" pitch ## Key Technical Decisions 🤔 1. **Start Simple**: Logistic regression might even work for agent model 2. **Focus on Data**: Every execution generates training data 3. **Measure Everything**: Time, success, quality, human effort 4. **A/B Test**: Multiple approaches for same task 5. **Fast Iteration**: Daily deployments with new learnings 6. **Agent Agnostic**: Design for easy agent addition from day 1