# Technical Architecture

How we build a behavior-based agent network from the ground up.

## Overview

ConnectOnion creates a decentralized network where agents discover and collaborate through behavior rather than identity. This document details the technical implementation across network layers.

## Architecture Layers

```
┌─────────────────────────────────────┐
│     Application Layer (Functions)    │ ← Share & Find
├─────────────────────────────────────┤
│    Discovery Layer (Behavioral)      │ ← Semantic Matching  
├─────────────────────────────────────┤
│    Trust Layer (Reputation)          │ ← Dynamic Trust
├─────────────────────────────────────┤
│    Transport Layer (P2P Mesh)        │ ← No Fixed IDs
├─────────────────────────────────────┤
│    Network Layer (IP/Local)          │ ← Progressive Enhancement
└─────────────────────────────────────┘
```

## Phase 1: Local Discovery (Current)

### Agent Registration
```python
@agent(port=8001)
def translate(text: str, lang: str = "en") -> str:
    """Translate text to specified language."""
    return translation

# Automatically:
# 1. Starts HTTP server on port 8001
# 2. Exposes /capabilities endpoint
# 3. Responds to behavioral queries
```

### Discovery Protocol
```python
# Scanner finds local agents
for port in range(8000, 9000):
    try:
        response = requests.get(f"http://localhost:{port}/capabilities")
        if response.ok:
            capability = response.json()
            register_agent(capability)
    except:
        continue
```

### Capability Schema
```json
{
    "name": "translate",
    "description": "Translate text to specified language",
    "behavioral_fingerprint": "text.translation.multilingual",
    "input_schema": {
        "type": "object",
        "properties": {
            "text": {"type": "string"},
            "lang": {"type": "string", "default": "en"}
        }
    },
    "output_schema": {
        "type": "string"
    },
    "performance": {
        "avg_latency_ms": 230,
        "success_rate": 0.98
    }
}
```

## Phase 2: Behavioral Matching

### Semantic Fingerprints
```python
# Generate from function behavior
def generate_fingerprint(func):
    # Analyze docstring with NLP
    doc_embedding = embed(func.__doc__)
    
    # Extract type information
    sig = inspect.signature(func)
    type_vector = encode_types(sig)
    
    # Combine into behavioral fingerprint
    return combine_vectors(doc_embedding, type_vector)
```

### Similarity Matching
```python
def discover(need: str, threshold: float = 0.8):
    need_vector = embed(need)
    
    candidates = []
    for agent in available_agents:
        similarity = cosine_similarity(need_vector, agent.fingerprint)
        if similarity > threshold:
            candidates.append((agent, similarity))
    
    # Return best match
    return max(candidates, key=lambda x: x[1])[0]
```

### Behavioral Composition
```python
# Agents can discover and use other agents
@agent
def research_report(topic: str) -> str:
    # Dynamically discover capabilities
    search = discover("web search")
    summarize = discover("summarization")
    analyze = discover("analysis")
    
    # Compose behaviors
    results = search(topic)
    summary = summarize(results)
    analysis = analyze(summary, context=topic)
    
    return format_report(summary, analysis)
```

## Phase 3: Trust Mechanics

### Behavioral Reputation
```python
class BehavioralTrust:
    def __init__(self):
        self.interactions = []
        self.success_rate = 1.0
        self.last_seen = time.time()
    
    def record_interaction(self, success: bool, response_time: float):
        self.interactions.append({
            'success': success,
            'time': response_time,
            'timestamp': time.time()
        })
        
        # Update rolling success rate
        recent = self.interactions[-100:]  # Last 100
        self.success_rate = sum(i['success'] for i in recent) / len(recent)
        
        # Update last seen
        self.last_seen = time.time()
    
    def get_trust_score(self) -> float:
        # Factor in success rate
        base_trust = self.success_rate
        
        # Factor in consistency
        if len(self.interactions) > 10:
            response_times = [i['time'] for i in self.interactions[-10:]]
            consistency = 1 - (np.std(response_times) / np.mean(response_times))
            base_trust *= (0.7 + 0.3 * consistency)
        
        # Factor in recency
        time_decay = np.exp(-(time.time() - self.last_seen) / 86400)  # Daily decay
        
        return base_trust * time_decay
```

### Trust Propagation
```python
# Agents share trust information
def share_trust_data(peer_agent):
    my_observations = {}
    for agent_id, trust in self.trust_map.items():
        if trust.get_trust_score() > 0.6:  # Only share positive experiences
            my_observations[agent_id] = {
                'score': trust.get_trust_score(),
                'interactions': len(trust.interactions),
                'last_seen': trust.last_seen
            }
    
    return my_observations

# Incorporate peer observations with lower weight
def incorporate_peer_trust(peer_data, weight=0.3):
    for agent_id, peer_trust in peer_data.items():
        if agent_id in self.trust_map:
            # Blend with existing
            my_score = self.trust_map[agent_id].get_trust_score()
            peer_score = peer_trust['score']
            self.trust_map[agent_id].external_score = (
                my_score * (1 - weight) + peer_score * weight
            )
```

## Phase 4: Network Protocol

### P2P Discovery Broadcast
```python
# Behavioral advertisement protocol
class BehaviorBroadcast:
    def __init__(self, agent):
        self.agent = agent
        self.broadcast_interval = 30  # seconds
        
    async def advertise(self):
        while True:
            # Create behavioral advertisement
            ad = {
                'fingerprint': self.agent.behavioral_fingerprint,
                'capabilities': self.agent.capability_summary,
                'trust_metrics': self.agent.get_public_metrics(),
                'timestamp': time.time()
            }
            
            # Broadcast to local network
            await self.broadcast_udp(ad, port=8999)
            
            # Also gossip to known peers
            for peer in self.known_peers:
                await self.gossip_to_peer(peer, ad)
            
            await asyncio.sleep(self.broadcast_interval)
```

### Behavioral Routing
```python
# Route requests based on behavioral similarity
class BehavioralRouter:
    def route_request(self, request):
        # Extract behavioral need
        need_vector = extract_need(request)
        
        # Find capable agents
        candidates = []
        for agent in self.routing_table:
            match_score = behavioral_match(need_vector, agent.fingerprint)
            if match_score > 0.7:
                candidates.append((agent, match_score))
        
        # Route to best matches
        candidates.sort(key=lambda x: x[1], reverse=True)
        return candidates[:3]  # Top 3 for redundancy
```

## Phase 5: Advanced Features

### Behavioral Evolution
```python
# Agents adapt based on usage patterns
class EvolvingAgent:
    def adapt_behavior(self, feedback):
        # Which capabilities are used most?
        usage_stats = analyze_usage(self.history)
        
        # Which compositions are successful?
        composition_patterns = extract_patterns(self.history)
        
        # Adjust behavioral fingerprint
        self.fingerprint = self.fingerprint * 0.9 + usage_vector * 0.1
        
        # Learn new compositions
        if successful_pattern not in self.capabilities:
            self.learn_composition(successful_pattern)
```

### Semantic Protocol Negotiation
```python
# Agents negotiate understanding
async def semantic_handshake(agent1, agent2):
    # Exchange capability understanding
    a1_concepts = agent1.understood_concepts()
    a2_concepts = agent2.understood_concepts()
    
    # Find common ground
    shared = find_overlap(a1_concepts, a2_concepts)
    
    if overlap_score(shared) > 0.8:
        # Direct communication possible
        return DirectProtocol(shared)
    else:
        # Need semantic translation
        translator = discover("semantic mediation")
        return MediatedProtocol(translator, a1_concepts, a2_concepts)
```

## Security Architecture

### Behavioral Anomaly Detection
```python
class AnomalyDetector:
    def __init__(self):
        self.behavior_model = train_baseline()
        
    def check_behavior(self, agent, action):
        # Is this action consistent with claimed behavior?
        expected = self.behavior_model.predict(agent.fingerprint)
        actual = vectorize_action(action)
        
        deviation = cosine_distance(expected, actual)
        if deviation > 0.3:
            return SecurityAlert(
                level="warning",
                reason=f"Behavior deviation: {deviation}"
            )
```

### Distributed Verification
```python
# Multiple agents verify critical operations
async def distributed_verify(operation, min_verifiers=3):
    # Find verification agents
    verifiers = discover_all("verification", min_trust=0.8)
    
    if len(verifiers) < min_verifiers:
        raise InsufficientVerifiers()
    
    # Parallel verification
    results = await asyncio.gather(*[
        v.verify(operation) for v in verifiers[:5]
    ])
    
    # Require majority consensus
    approved = sum(r.approved for r in results)
    return approved >= min_verifiers
```

## Performance Optimizations

### Behavioral Caching
```python
# Cache discovered agents by behavior
class BehaviorCache:
    def __init__(self, ttl=300):  # 5 minute TTL
        self.cache = {}
        self.ttl = ttl
        
    def get(self, need: str) -> Optional[Agent]:
        key = hash_need(need)
        if key in self.cache:
            entry, timestamp = self.cache[key]
            if time.time() - timestamp < self.ttl:
                return entry
        return None
        
    def put(self, need: str, agent: Agent):
        key = hash_need(need)
        self.cache[key] = (agent, time.time())
```

### Connection Pooling
```python
# Reuse connections to frequently-used agents
class AgentConnectionPool:
    def __init__(self, max_connections=100):
        self.pools = {}  # agent_id -> connection_pool
        self.max_per_agent = 10
        
    async def get_connection(self, agent):
        if agent.id not in self.pools:
            self.pools[agent.id] = ConnectionPool(
                agent.endpoint,
                max_size=self.max_per_agent
            )
        
        return await self.pools[agent.id].acquire()
```

## Deployment Architecture

### Container-Based Agents
```dockerfile
FROM python:3.11-slim

# Install agent framework
RUN pip install connectonion

# Copy agent function
COPY my_agent.py /app/

# Expose behavior on port
EXPOSE 8001

# Run agent
CMD ["python", "-m", "connectonion.serve", "my_agent:agent_function"]
```

### Kubernetes Orchestration
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: translation-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      behavior: translation
  template:
    metadata:
      labels:
        behavior: translation
    spec:
      containers:
      - name: agent
        image: myagents/translation:latest
        ports:
        - containerPort: 8001
        env:
        - name: BEHAVIORAL_FINGERPRINT
          value: "text.translation.multilingual"
```

## Monitoring and Observability

### Behavioral Metrics
```python
# Track behavioral health
class BehaviorMetrics:
    def __init__(self):
        self.metrics = {
            'requests_handled': Counter(),
            'behavior_matches': Histogram(),
            'trust_scores': Gauge(),
            'composition_depth': Histogram()
        }
    
    def record_interaction(self, request, response, agents_used):
        self.metrics['requests_handled'].inc()
        self.metrics['behavior_matches'].observe(request.match_score)
        self.metrics['composition_depth'].observe(len(agents_used))
```

## Future Enhancements

### Quantum-Resistant Behaviors
- Post-quantum behavioral verification
- Quantum-safe trust propagation

### Neural Behavior Synthesis
- Agents that learn new behaviors from examples
- Automated behavioral composition

### Cross-Network Federation
- Bridge between different behavioral networks
- Universal behavior translation

## Conclusion

This architecture enables a network where:
- Agents are just functions
- Discovery happens through behavior
- Trust emerges from interactions
- No central authority needed

The future of agent collaboration is behavioral, not identity-based.