# AI Prompt Engineer Agent Specification for Agora v5

## Executive Summary

The AI Prompt Engineer Agent (AIPEA) operates as an **autonomous orchestration agent** within Agora's Orchestration & Supervision layer, positioned to intercept and enhance all user queries before they reach downstream agents. It implements a **dual-mode architecture** (online/offline) with military-grade resilience, ensuring consistent operation from enterprise data centers to disconnected field deployments.

### Key Capabilities
- **100% Availability**: Functions with zero connectivity in hostile or remote environments
- **Autonomous Operation**: Self-directed enhancement with minimal human oversight
- **Military-Grade Security**: FedRAMP-compliant with support for classified operations
- **Adaptive Learning**: Continuously improves from user interactions
- **Multi-Tier Intelligence**: Scales from edge devices to enterprise clusters

## Core Architecture Philosophy

Following the principles of logical, graceful, elegant, robust, secure, and innovative design, AIPEA implements a **"Cascade of Intelligence"** architecture:

```
┌─────────────────────────────────────────────────────────────┐
│                   AI Prompt Engineer Agent                   │
│                  "Cascade of Intelligence"                   │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌─────────────┐  ┌──────────────┐  ┌─────────────────┐  │
│  │   Tier 0    │  │    Tier 1    │  │     Tier 2      │  │
│  │   OFFLINE   │  │   TACTICAL   │  │   STRATEGIC     │  │
│  │             │  │              │  │                 │  │
│  │ • Llama 3.3 │  │ • Claude 4   │  │ • Multi-Agent   │  │
│  │   70B Local │  │   Sonnet     │  │   Orchestration │  │
│  │ • Gemma 3n  │  │ • GPT-4.1    │  │ • STMCP Loop    │  │
│  │   Edge      │  │ • Context7   │  │ • Synthesis     │  │
│  └─────────────┘  └──────────────┘  └─────────────────┘  │
│         ↓                ↓                    ↓           │
│  ┌─────────────────────────────────────────────────────┐  │
│  │          Unified Enhancement Pipeline                │  │
│  └─────────────────────────────────────────────────────┘  │
│                           ↓                               │
│                   Enhanced Query Output                   │
└─────────────────────────────────────────────────────────┘
```

### Design Principles

1. **Resilience First**: Every component assumes hostile conditions
2. **Graceful Degradation**: Maintains functionality as capabilities diminish
3. **Security by Default**: Zero-trust architecture throughout
4. **Continuous Learning**: Improves with every interaction
5. **Resource Awareness**: Optimizes for available compute/storage

## Integration with Agora Architecture

AIPEA seamlessly integrates within the Agora v5 ecosystem:

### Positioning in the Stack

1. **User Interface Layer** → AIPEA → **Orchestration Layer**
   - Receives raw queries from Web Interface, API Gateway, Collaboration Hub
   - Enhances and enriches queries with context and intent
   - Routes to appropriate supervisors

2. **Orchestration Coordination**
   - **AI Mediator Supervisor**: Complex reasoning tasks
   - **Synthesis Orchestrator**: Multi-source aggregation
   - **Research Coordinator**: Deep research queries

3. **Memory & Context Integration**
   - Leverages Mem0 Architecture for context retention
   - Interfaces with Knowledge Graph for relationship mapping
   - Utilizes Hybrid RAG for efficient retrieval

4. **MCP Tool Access**
   - Web Search for real-time information
   - Academic Research for scholarly content
   - Internal Data for organizational knowledge
   - Computation for analytical tasks

### Communication Protocol

```python
# Standard Agora message format
message = AgoraMessage(
    source="prompt_engineer_agent_001",
    destination="ai_mediator_supervisor",
    message_type="ENHANCED_QUERY",
    payload={
        "original_query": user_input,
        "enhanced_query": enhanced_output,
        "context": context_7_lenses,
        "routing_hints": routing_metadata
    },
    priority=MessagePriority.HIGH,
    security_classification="SECRET"
)
```

## Three-Tier Operational Model

### Tier 0: Offline Foundation (100% Availability)

**Purpose**: Guarantee functionality in zero-connectivity environments

**Components**:
- **Primary Model**: Llama 3.3 70B (4-bit quantized)
  - ~35GB memory footprint
  - GPT-4 comparable performance
  - Full instruction following
- **Secondary Model**: Gemma 3n with MatFormer
  - <3GB for extreme edge cases
  - Dynamic E2B/E4B execution modes
  - Mobile-first optimization

**Capabilities**:
- Query clarification and expansion
- Context injection from offline knowledge base
- Intent detection and routing
- Security pre-screening
- Basic enhancement strategies

**Performance**:
- Latency: <2 seconds
- Quality: >85% of online performance
- Availability: 100%

### Tier 1: Tactical Enhancement (Online/Hybrid)

**Purpose**: Rapid, high-quality enhancement with external resources

**Components**:
- **Claude 4 Sonnet**: Primary enhancement (72.7% SWE-bench)
- **Context7 MCP**: Real-time documentation access
- **Sequential Thinking MCP**: Structured reasoning
- **Hybrid RAG**: Mem0 architecture integration

**Processing Pipeline**:
1. Query complexity analysis
2. Context aggregation (7 lenses)
3. Strategy selection (6 enhancement types)
4. Tool-augmented processing
5. Quality validation
6. Security verification

**Performance**:
- Latency: <5 seconds
- Quality: 95% benchmark scores
- Tool integration: Full MCP suite

### Tier 2: Strategic Orchestration (Full Online)

**Purpose**: Complex, multi-agent prompt engineering for critical queries

**Components**:
- Multi-agent spawning and coordination
- Synthesis orchestration integration
- Critique loops with quality assessment
- Full STMCP reasoning chains

**Advanced Features**:
- Parallel agent analysis
- Multi-round refinement
- Consensus building
- Deep reasoning chains

**Performance**:
- Latency: <15 seconds
- Quality: >98% accuracy
- Complexity handling: Unlimited

## Key Innovations

### 1. Autonomous Context Management

The Context7 framework provides comprehensive situational awareness:

```python
class Context7:
    user: UserProfile          # Expertise, role, preferences
    task: TaskAnalysis        # Intent, complexity, domain
    data: List[Artifact]      # Relevant documents, history
    environment: EnvStatus    # Connectivity, resources
    history: List[Query]      # Previous interactions
    output: Requirements      # Format, style, constraints
    feedback: List[Rating]    # Quality signals
```

### 2. Offline Knowledge Base Architecture

Military-grade knowledge management system:
- **Compression**: 10:1 ratios for edge deployment
- **Domains**: Military, Medical, Technical, Cybersecurity, Logistics
- **Storage Tiers**: Ultra-Compact (<1GB) to Extended (100GB)
- **Learning**: Continuous improvement without connectivity
- **Sync**: Intelligent merge when connection restored

### 3. Enhancement Strategy Engine

Six specialized enhancement strategies:

1. **Technical**: Specification extraction, constraint identification
2. **Research**: Hypothesis clarification, methodology enhancement
3. **Creative**: Perspective multiplication, narrative structuring
4. **Analytical**: Metric definition, visualization recommendations
5. **Operational**: Task decomposition, resource mapping
6. **Strategic**: Objective hierarchy, scenario planning

### 4. Resilience & Security Pipeline

Multi-layered security approach:
- **Input Sanitization**: Injection detection and neutralization
- **Classification Enforcement**: Respect for security levels
- **Audit Trail**: Cryptographic signatures on all operations
- **Emergency Protocols**: Instant lockdown capabilities
- **Compliance**: FedRAMP, ITAR, GDPR ready

### 5. Adaptive Learning System

Continuous improvement without internet:
- **Pattern Recognition**: Learns successful enhancement patterns
- **User Adaptation**: Personalizes to individual preferences
- **Domain Expertise**: Builds specialized knowledge over time
- **Strategy Optimization**: Selects best approach based on history

## Performance Characteristics

### Latency Targets
- **Tier 0 (Offline)**: <2 seconds
- **Tier 1 (Tactical)**: <5 seconds
- **Tier 2 (Strategic)**: <15 seconds
- **Failover Time**: <500ms between tiers

### Quality Metrics
- **Intent Preservation**: >99.5%
- **Context Utilization**: >95%
- **Security Compliance**: 100%
- **Offline Parity**: >85% of online quality

### Resource Requirements
- **Minimum (Tier 0)**: 8GB RAM, 4 CPU cores
- **Recommended (Tier 1)**: 32GB RAM, 8 CPU cores
- **Optimal (Tier 2)**: 64GB RAM, 16 CPU cores, GPU

### Scalability
- **Concurrent Requests**: 100 (Tier 0), 500 (Tier 1), 1000 (Tier 2)
- **Knowledge Base**: 1GB to 100GB depending on deployment
- **User Capacity**: Unlimited with proper infrastructure

## Deployment Configurations

### 1. Enterprise Data Center
- Full Tier 0-2 availability
- GPU clusters for local model hosting
- High-bandwidth MCP connections
- Real-time synchronization
- Redundant failover systems

### 2. Field Deployment (Disconnected)
- Tier 0 only mode
- Edge device optimization
- Ruggedized hardware support
- Solar/battery power awareness
- Periodic sync when possible

### 3. Hybrid Cloud
- Dynamic tier selection
- Classification-based routing
- On-premise sensitive processing
- Cloud burst for peak loads
- Encrypted state synchronization

### 4. Mobile/Tactical
- Smartphone deployment ready
- Tablet-optimized interfaces
- Vehicle-mounted systems
- Wearable integration
- Mesh network support

## Autonomous Operation Modes

### 1. Continuous Enhancement Mode
- Monitors query streams in real-time
- Pre-emptively enhances based on patterns
- Builds context proactively
- Optimizes for common requests

### 2. Collaborative Mode
- Human-in-the-loop operations
- Suggestion and approval workflows
- Learning from corrections
- Preference adaptation

### 3. Guardian Mode
- Security-first processing
- Threat detection and blocking
- Anomaly alerting
- Compliance enforcement

### 4. Stealth Mode
- Minimal footprint operation
- No external communications
- Local-only processing
- Evidence-free operation

## Integration Protocols

### Message Bus Architecture
```python
# Registration with Agora
registration = AgentRegistration(
    agent_id="prompt_engineer_agent_001",
    capabilities=[
        AgentCapability.QUERY_ENHANCEMENT,
        AgentCapability.CONTEXT_BUILDING,
        AgentCapability.SECURITY_SCREENING,
        AgentCapability.OFFLINE_PROCESSING
    ],
    performance_metrics={
        "enhancement_accuracy": 0.95,
        "average_latency_ms": 250,
        "offline_capability": 1.0
    }
)
```

### Coordination Patterns
1. **Direct Enhancement**: Standalone query processing
2. **Mediated Enhancement**: Complex reasoning via AI Mediator
3. **Synthesized Enhancement**: Multi-source via Synthesis Orchestrator
4. **Swarm Enhancement**: Parallel processing with agent teams

### Emergency Protocols
- **SECURITY_BREACH**: Immediate lockdown and alert
- **NETWORK_ISOLATION**: Automatic Tier 0 failover
- **RESOURCE_EXHAUSTION**: Graceful degradation
- **SYSTEM_FAILURE**: State recovery and continuation

## Testing & Validation

### Resilience Test Categories
1. **Connectivity**: Desert scenario, satellite failover
2. **Adversarial**: Injection attacks, resource exhaustion
3. **Load**: 1000 QPS sustained, burst handling
4. **Security**: FedRAMP validation, cross-domain isolation
5. **Degradation**: Progressive service failure
6. **Recovery**: 15-minute disaster recovery
7. **Compliance**: GDPR, ITAR verification

### Quality Assurance
- Automated test suite with 100+ scenarios
- Red team exercises quarterly
- Continuous monitoring in production
- User satisfaction tracking
- Performance regression detection

## Future Enhancements

### Planned Capabilities
1. **Multimodal Support**: Image, audio, video queries
2. **Predictive Enhancement**: Anticipate user needs
3. **Federated Learning**: Cross-deployment improvements
4. **Quantum-Resistant Security**: Post-quantum cryptography
5. **Neural Architecture Search**: Self-optimizing models

### Research Directions
- Neuromorphic computing integration
- Brain-computer interface support
- Swarm intelligence optimization
- Causal reasoning enhancement
- Explainable AI improvements

## Conclusion

The AI Prompt Engineer Agent represents a paradigm shift in human-AI interaction, providing intelligent query enhancement that works anywhere, learns continuously, and maintains military-grade security. By implementing a resilient, multi-tier architecture with offline-first design, AIPEA ensures that advanced AI capabilities are available when and where they're needed most - from the boardroom to the battlefield.

This specification serves as the living document for AIPEA development, deployment, and evolution within the Agora v5 ecosystem.