Intent System

The Intent System is a sophisticated capability discovery and message routing mechanism in PAF-Core that enables agents to automatically discover, understand, and route messages to the most appropriate agent based on intent recognition and capability matching.

Overview

The Intent System provides intelligent message routing by:

• 🧠 Intent Recognition - Understanding what agents want to accomplish
• 🔍 Capability Discovery - Finding agents with the right capabilities
• 🎯 Smart Routing - Directing messages to the most suitable agent
• 🔄 Fallback Handling - Graceful degradation when no perfect match exists

Core Concepts

Intent Recognition

Intent recognition analyzes incoming messages to understand the user's or agent's intent:

# Intent recognition example
intent = intent_system.recognize_intent(
    message="I need help with data analysis",
    context={"user_id": "user123", "session_id": "session456"}
)

# Result
{
    "intent": "data_analysis",
    "confidence": 0.95,
    "entities": {
        "task": "data analysis",
        "urgency": "normal"
    },
    "capabilities_required": ["data_processing", "statistical_analysis"]
}

Capability Discovery

Agents register their capabilities with the intent system:

# Agent capability registration
agent_capabilities = {
    "agent_id": "data-analyst-001",
    "capabilities": [
        {
            "name": "data_processing",
            "description": "Process and clean datasets",
            "confidence": 0.9,
            "supported_formats": ["csv", "json", "parquet"]
        },
        {
            "name": "statistical_analysis",
            "description": "Perform statistical analysis and modeling",
            "confidence": 0.95,
            "supported_methods": ["regression", "classification", "clustering"]
        }
    ],
    "availability": "online",
    "load_factor": 0.3
}

Message Routing

The intent system routes messages to the most appropriate agent:

# Message routing
routing_result = intent_system.route_message(
    intent="data_analysis",
    required_capabilities=["data_processing", "statistical_analysis"],
    context={"user_id": "user123"}
)

# Result
{
    "target_agent": "data-analyst-001",
    "confidence": 0.92,
    "routing_reason": "Best capability match",
    "fallback_agents": ["data-analyst-002", "general-ai-001"]
}

Intent Recognition Engine

Natural Language Processing

The intent recognition engine uses advanced NLP techniques:

class IntentRecognitionEngine:
    def __init__(self):
        self.nlp_model = load_pretrained_model("intent-classifier-v2")
        self.entity_extractor = EntityExtractor()
        self.context_analyzer = ContextAnalyzer()
    
    def recognize_intent(self, message, context=None):
        # Preprocess message
        processed_message = self.preprocess(message)
        
        # Extract entities
        entities = self.entity_extractor.extract(processed_message)
        
        # Analyze context
        context_features = self.context_analyzer.analyze(context or {})
        
        # Classify intent
        intent_prediction = self.nlp_model.predict(
            processed_message, entities, context_features
        )
        
        return {
            "intent": intent_prediction.intent,
            "confidence": intent_prediction.confidence,
            "entities": entities,
            "context_features": context_features
        }

Intent Categories

The system recognizes various intent categories:

Category	Description	Examples
data_analysis	Data processing and analysis	"Analyze this dataset", "Create a report"
communication	Agent-to-agent communication	"Send message to agent X", "Broadcast update"
task_delegation	Assigning tasks to other agents	"Delegate this task", "Find someone to help"
information_request	Requesting information	"What's the status?", "Get latest data"
collaboration	Multi-agent collaboration	"Work together on this", "Coordinate response"
monitoring	System monitoring and health	"Check system status", "Monitor performance"

Entity Extraction

The system extracts relevant entities from messages:

# Entity extraction example
entities = intent_system.extract_entities(
    "Analyze the sales data from last quarter and create a visualization"
)

# Result
{
    "task": "analyze",
    "data_type": "sales data",
    "timeframe": "last quarter",
    "output": "visualization",
    "urgency": "normal"
}

Capability Registry

Agent Registration

Agents register their capabilities with the system:

# Agent capability registration
class CapabilityRegistry:
    def __init__(self):
        self.agents = {}
        self.capability_index = {}
    
    def register_agent(self, agent_id, capabilities):
        """Register agent capabilities"""
        self.agents[agent_id] = {
            "capabilities": capabilities,
            "status": "online",
            "last_seen": datetime.now(),
            "load_factor": 0.0
        }
        
        # Update capability index
        for capability in capabilities:
            if capability["name"] not in self.capability_index:
                self.capability_index[capability["name"]] = []
            self.capability_index[capability["name"]].append(agent_id)
    
    def find_agents_by_capability(self, capability_name):
        """Find agents with specific capability"""
        return self.capability_index.get(capability_name, [])
    
    def get_best_agent(self, required_capabilities, context=None):
        """Find the best agent for given capabilities"""
        candidates = []
        
        for capability in required_capabilities:
            agents = self.find_agents_by_capability(capability)
            candidates.extend(agents)
        
        # Score and rank candidates
        scored_candidates = []
        for agent_id in set(candidates):
            score = self._calculate_agent_score(agent_id, required_capabilities, context)
            scored_candidates.append((agent_id, score))
        
        # Return best match
        return max(scored_candidates, key=lambda x: x[1])

Capability Matching

The system matches required capabilities with available agents:

def calculate_capability_match(agent_capabilities, required_capabilities):
    """Calculate how well an agent matches required capabilities"""
    matches = 0
    total_required = len(required_capabilities)
    
    for required in required_capabilities:
        for agent_cap in agent_capabilities:
            if (agent_cap["name"] == required["name"] and 
                agent_cap["confidence"] >= required["min_confidence"]):
                matches += 1
                break
    
    return matches / total_required if total_required > 0 else 0

Message Routing

Routing Strategies

The system supports multiple routing strategies:

1. Capability-Based Routing

def route_by_capability(intent, required_capabilities):
    """Route based on capability matching"""
    candidates = capability_registry.find_agents_by_capability(
        required_capabilities[0]
    )
    
    best_match = None
    best_score = 0
    
    for agent_id in candidates:
        score = calculate_capability_match(
            capability_registry.get_agent_capabilities(agent_id),
            required_capabilities
        )
        if score > best_score:
            best_score = score
            best_match = agent_id
    
    return best_match, best_score

2. Load-Based Routing

def route_by_load(intent, required_capabilities):
    """Route based on agent load and availability"""
    candidates = capability_registry.find_agents_by_capability(
        required_capabilities[0]
    )
    
    # Filter by availability and load
    available_agents = [
        agent_id for agent_id in candidates
        if capability_registry.is_agent_available(agent_id)
    ]
    
    if not available_agents:
        return None, 0
    
    # Choose agent with lowest load
    best_agent = min(available_agents, key=lambda x: 
        capability_registry.get_agent_load(x)
    )
    
    return best_agent, 1.0

3. Context-Aware Routing

def route_by_context(intent, required_capabilities, context):
    """Route based on context and history"""
    candidates = capability_registry.find_agents_by_capability(
        required_capabilities[0]
    )
    
    # Score based on context
    scored_candidates = []
    for agent_id in candidates:
        score = calculate_context_score(agent_id, context)
        scored_candidates.append((agent_id, score))
    
    # Return best match
    return max(scored_candidates, key=lambda x: x[1])

Fallback Mechanisms

When no perfect match is found, the system provides fallback options:

def route_with_fallback(intent, required_capabilities, context):
    """Route with fallback mechanisms"""
    # Try primary routing
    primary_agent, confidence = route_by_capability(intent, required_capabilities)
    
    if confidence >= 0.8:
        return primary_agent, confidence
    
    # Try fallback routing
    fallback_agents = []
    
    # 1. Partial capability match
    partial_matches = find_partial_capability_matches(required_capabilities)
    fallback_agents.extend(partial_matches)
    
    # 2. General purpose agents
    general_agents = find_general_purpose_agents()
    fallback_agents.extend(general_agents)
    
    # 3. Human escalation
    if not fallback_agents:
        return "human-escalation", 0.1
    
    # Choose best fallback
    best_fallback = max(fallback_agents, key=lambda x: x[1])
    return best_fallback[0], best_fallback[1]

Intent Learning

Continuous Learning

The intent system continuously learns from interactions:

class IntentLearner:
    def __init__(self):
        self.feedback_buffer = []
        self.model_updater = ModelUpdater()
    
    def record_interaction(self, message, intent, outcome, feedback):
        """Record interaction for learning"""
        interaction = {
            "message": message,
            "predicted_intent": intent,
            "outcome": outcome,
            "feedback": feedback,
            "timestamp": datetime.now()
        }
        self.feedback_buffer.append(interaction)
    
    def update_model(self):
        """Update intent recognition model"""
        if len(self.feedback_buffer) >= 100:
            training_data = self.prepare_training_data()
            self.model_updater.update(training_data)
            self.feedback_buffer.clear()

Feedback Integration

The system integrates feedback to improve routing:

def integrate_feedback(agent_id, message, outcome, feedback):
    """Integrate feedback to improve routing"""
    # Update agent performance metrics
    agent_metrics = get_agent_metrics(agent_id)
    agent_metrics.update_performance(outcome, feedback)
    
    # Update capability confidence
    if feedback["satisfaction"] > 0.8:
        increase_capability_confidence(agent_id, feedback["capabilities"])
    else:
        decrease_capability_confidence(agent_id, feedback["capabilities"])
    
    # Update routing preferences
    update_routing_preferences(agent_id, feedback)

Advanced Features

Multi-Agent Collaboration

The intent system can coordinate multiple agents:

def coordinate_multi_agent_task(intent, required_capabilities):
    """Coordinate multiple agents for complex tasks"""
    # Find agents for each capability
    agent_assignments = {}
    for capability in required_capabilities:
        agents = find_agents_by_capability(capability["name"])
        if agents:
            agent_assignments[capability["name"]] = agents[0]
    
    # Create collaboration plan
    collaboration_plan = {
        "task_id": generate_task_id(),
        "agents": agent_assignments,
        "coordination_strategy": "sequential",  # or "parallel", "pipeline"
        "communication_protocol": "grpc"
    }
    
    return collaboration_plan

Intent Chaining

The system can chain intents for complex workflows:

def chain_intents(initial_intent, context):
    """Chain multiple intents for complex workflows"""
    intent_chain = [initial_intent]
    current_context = context
    
    while True:
        next_intent = predict_next_intent(intent_chain, current_context)
        if not next_intent or next_intent["confidence"] < 0.7:
            break
        
        intent_chain.append(next_intent)
        current_context = update_context(current_context, next_intent)
    
    return intent_chain

Dynamic Capability Discovery

The system can discover new capabilities dynamically:

def discover_new_capabilities(agent_id, interactions):
    """Discover new capabilities from agent interactions"""
    capability_patterns = analyze_interaction_patterns(interactions)
    new_capabilities = []
    
    for pattern in capability_patterns:
        if pattern["confidence"] > 0.8:
            new_capability = {
                "name": pattern["capability_name"],
                "description": pattern["description"],
                "confidence": pattern["confidence"],
                "examples": pattern["examples"]
            }
            new_capabilities.append(new_capability)
    
    return new_capabilities

Configuration

Intent System Configuration

# intent-system-config.yaml
intent_system:
  recognition:
    model: "intent-classifier-v2"
    confidence_threshold: 0.7
    max_intents: 10
  
  routing:
    strategy: "capability_based"  # or "load_based", "context_aware"
    fallback_enabled: true
    max_fallback_depth: 3
  
  learning:
    enabled: true
    feedback_buffer_size: 100
    update_frequency: "daily"
  
  capabilities:
    auto_discovery: true
    confidence_decay: 0.95
    min_confidence: 0.5

Agent Configuration

# agent-intent-config.yaml
agent:
  intent_system:
    enabled: true
    capabilities:
      - name: "data_analysis"
        confidence: 0.9
        description: "Analyze datasets and generate insights"
      - name: "report_generation"
        confidence: 0.85
        description: "Generate reports and visualizations"
    
    routing_preferences:
      preferred_agents: ["data-analyst-001", "report-generator-002"]
      fallback_agents: ["general-ai-001"]
    
    learning:
      feedback_enabled: true
      capability_auto_update: true

Monitoring and Analytics

Intent Analytics

class IntentAnalytics:
    def __init__(self):
        self.metrics = {}
    
    def track_intent_recognition(self, intent, confidence, outcome):
        """Track intent recognition performance"""
        self.metrics["intent_recognition"] = {
            "total_requests": self.metrics.get("total_requests", 0) + 1,
            "success_rate": self.calculate_success_rate(),
            "average_confidence": self.calculate_average_confidence(),
            "top_intents": self.get_top_intents()
        }
    
    def track_routing_performance(self, agent_id, success, response_time):
        """Track routing performance"""
        self.metrics["routing_performance"] = {
            "agent_success_rates": self.calculate_agent_success_rates(),
            "average_response_time": self.calculate_average_response_time(),
            "routing_accuracy": self.calculate_routing_accuracy()
        }

Performance Metrics

def get_intent_system_metrics():
    """Get comprehensive intent system metrics"""
    return {
        "recognition_accuracy": 0.92,
        "routing_success_rate": 0.89,
        "average_response_time": 0.15,
        "capability_coverage": 0.85,
        "learning_rate": 0.03,
        "fallback_usage": 0.12
    }

Troubleshooting

Common Issues

1. Low Intent Recognition Accuracy

Symptoms: Intent recognition confidence below threshold

Solutions:

# Increase training data
intent_system.add_training_data(new_examples)

# Adjust confidence threshold
intent_system.set_confidence_threshold(0.6)

# Update model
intent_system.retrain_model()

2. Poor Routing Performance

Symptoms: Messages routed to wrong agents

Solutions:

# Update capability registry
capability_registry.refresh_agent_capabilities()

# Adjust routing strategy
intent_system.set_routing_strategy("context_aware")

# Add fallback mechanisms
intent_system.enable_fallback_routing()

3. Slow Response Times

Symptoms: Intent recognition taking too long

Solutions:

# Optimize model
intent_system.optimize_model()

# Cache frequent intents
intent_system.enable_intent_caching()

# Use faster routing strategy
intent_system.set_routing_strategy("capability_based")

Next Steps

After understanding the Intent System:

1. A2A Protocol - Learn about the communication protocol
2. Message Routing - Understand message routing mechanisms
3. Security - Explore security considerations
4. LangGraph Integration - Advanced workflow integration

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Ready to learn about the protocol? Check out A2A Protocol to understand the communication mechanisms!