# 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