Technical
Intelligent Systems
AI Agents
Creating Custom AI Agents

Creating Custom AI Agents

Overview

While AIMatrix provides 50+ pre-built agents, the real power comes from creating custom agents tailored to your specific business needs. This guide will walk you through creating your first custom AI agent.

What is an AI Agent?

An AI agent in AIMatrix is an autonomous software entity that:

  • Observes - Monitors data and events
  • Decides - Makes intelligent decisions based on rules and ML models
  • Acts - Executes actions in BigLedger or external systems
  • Learns - Improves performance over time

Agent Architecture 

  graph LR
    A[Data Sources] --> B[Agent Core]
    B --> C{Decision Engine}
    C --> D[Actions]
    C --> E[Learning Loop]
    E --> B
    D --> F[BigLedger]
    D --> G[External APIs]
    D --> H[Notifications]

Creating Your First Agent

Step 1: Choose Agent Type

AIMatrix supports several agent types:

Type Use Case Complexity
Rule-Based Simple if-then logic Low
ML-Powered Predictions and patterns Medium
Conversational Natural language interaction Medium
Hybrid Combines multiple approaches High
Multi-Agent Coordinates other agents High

Step 2: Define Agent Blueprint

Create a new agent using the AIMatrix Console or API:

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from aimatrix import AgentBuilder

# Create agent blueprint
agent = AgentBuilder.create(
    name="Customer Churn Predictor",
    type="ml_powered",
    description="Predicts and prevents customer churn"
)

# Define inputs
agent.add_input(
    source="bigledger.customers",
    fields=["purchase_history", "support_tickets", "last_login"]
)

# Define model
agent.set_model(
    type="classification",
    algorithm="gradient_boosting",
    target="churn_probability"
)

# Define actions
agent.add_action(
    trigger="churn_probability > 0.7",
    action="send_retention_offer"
)

Step 3: Configure Data Sources

Connect your agent to BigLedger data:

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# agent-config.yaml
data_sources:
  - name: customer_data
    type: bigledger
    module: crm
    entities:
      - customers
      - transactions
      - interactions
    refresh: realtime
    
  - name: external_data
    type: api
    endpoint: https://api.social.com/sentiment
    auth: bearer_token
    refresh: hourly

Step 4: Design Decision Logic

Rule-Based Logic 

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# Simple rule engine
def decision_logic(context):
    customer = context.customer
    
    if customer.lifetime_value > 10000:
        priority = "high"
    elif customer.recent_tickets > 3:
        priority = "urgent"
    else:
        priority = "normal"
    
    return {
        "priority": priority,
        "action": determine_action(priority)
    }

ML-Based Logic 

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# Machine learning model
def ml_decision_logic(context):
    # Feature extraction
    features = extract_features(context)
    
    # Get prediction
    prediction = model.predict(features)
    
    # Interpret results
    if prediction.confidence > 0.8:
        return {
            "action": prediction.recommended_action,
            "confidence": prediction.confidence
        }
    else:
        return {"action": "human_review"}

Step 5: Implement Actions

Define what your agent can do:

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# Action definitions
class CustomerRetentionActions:
    
    @agent_action
    def send_retention_offer(self, customer_id, offer_type):
        """Send personalized retention offer"""
        offer = generate_offer(customer_id, offer_type)
        
        # Send via email
        email_service.send(
            to=customer.email,
            template="retention_offer",
            data=offer
        )
        
        # Log in BigLedger
        bigledger.log_activity(
            customer_id=customer_id,
            activity="retention_offer_sent",
            details=offer
        )
        
        return {"status": "sent", "offer_id": offer.id}
    
    @agent_action
    def escalate_to_human(self, customer_id, reason):
        """Escalate to human agent"""
        ticket = create_support_ticket(
            customer_id=customer_id,
            priority="high",
            reason=reason
        )
        
        notify_team(ticket)
        return {"ticket_id": ticket.id}

Advanced Agent Features

Multi-Agent Orchestration

Create agents that coordinate with others:

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# Orchestrator agent
orchestrator = AgentBuilder.create(
    name="Sales Orchestrator",
    type="multi_agent"
)

# Add sub-agents
orchestrator.add_agent("lead_scorer")
orchestrator.add_agent("opportunity_predictor")
orchestrator.add_agent("quote_generator")

# Define coordination logic
orchestrator.set_workflow("""
    1. lead_scorer evaluates new leads
    2. IF score > 80:
        - opportunity_predictor analyzes potential
        - quote_generator creates personalized quote
    3. ELSE:
        - Add to nurture campaign
""")

Learning and Adaptation

Enable continuous learning:

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# Enable learning
agent.enable_learning(
    mode="reinforcement",
    feedback_source="user_actions",
    update_frequency="daily"
)

# Define reward function
def reward_function(action, outcome):
    if outcome == "customer_retained":
        return 1.0
    elif outcome == "customer_churned":
        return -1.0
    else:
        return 0.0

agent.set_reward_function(reward_function)

Edge Deployment

Deploy agents at the edge for real-time processing:

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# Export for edge deployment
agent.export(
    format="onnx",
    optimization="quantized",
    target="edge_device"
)

# Edge configuration
edge_config = {
    "device_type": "raspberry_pi",
    "memory_limit": "512MB",
    "inference_engine": "tflite",
    "update_strategy": "incremental"
}

agent.deploy_to_edge(edge_config)

Agent Development Workflow

1. Local Development 

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# Initialize agent project
aimatrix init my-agent

# Develop locally
cd my-agent
python agent.py --mode=development

# Test with sample data
aimatrix test --data=sample.json

2. Simulation Testing 

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# Run simulation
simulator = AgentSimulator(agent)

# Test with historical data
results = simulator.run(
    data="historical_2024.csv",
    metrics=["accuracy", "response_time", "cost"]
)

print(f"Accuracy: {results.accuracy:.2%}")
print(f"Avg Response: {results.response_time}ms")

3. Staging Deployment 

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# Deploy to staging
aimatrix deploy --env=staging

# Monitor performance
aimatrix monitor my-agent --duration=7d

# A/B test against existing solution
aimatrix ab-test my-agent --baseline=current --split=0.1

4. Production Deployment 

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# Deploy to production
aimatrix deploy --env=production --rollout=gradual

# Set up monitoring
aimatrix monitor my-agent --alerts=enabled

# Enable auto-scaling
aimatrix scale my-agent --min=1 --max=10 --metric=cpu

Best Practices

1. Start Simple

  • Begin with rule-based logic
  • Add ML capabilities gradually
  • Test thoroughly before production

2. Design for Failure 

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@agent_action
def safe_action(self, data):
    try:
        result = perform_action(data)
    except Exception as e:
        # Fallback logic
        result = fallback_action(data)
        log_error(e)
    
    return result

3. Monitor Everything 

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# Add comprehensive logging
agent.add_metrics([
    "prediction_accuracy",
    "action_success_rate",
    "response_time_p95",
    "cost_per_decision"
])

# Set up alerts
agent.add_alert(
    condition="accuracy < 0.8",
    action="notify_team",
    severity="high"
)

4. Version Control 

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# agent-version.yaml
version: 1.2.0
changes:
  - improved: Churn prediction accuracy
  - added: SMS notification channel
  - fixed: Timezone handling
compatibility:
  min_aimatrix: 3.0.0
  min_bigledger: 4.1.0

Code Examples

Complete Customer Service Agent 

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from aimatrix import Agent, BigLedgerConnector
from aimatrix.nlp import IntentClassifier
from aimatrix.ml import PredictiveModel

class CustomerServiceAgent(Agent):
    """Intelligent customer service agent"""
    
    def __init__(self):
        super().__init__(name="CustomerServiceBot")
        
        # Initialize components
        self.bigledger = BigLedgerConnector()
        self.intent_classifier = IntentClassifier()
        self.sentiment_analyzer = SentimentAnalyzer()
        self.knowledge_base = KnowledgeBase()
        
    def process_message(self, message, customer_id):
        """Process customer message"""
        
        # Analyze message
        intent = self.intent_classifier.classify(message)
        sentiment = self.sentiment_analyzer.analyze(message)
        
        # Get customer context
        customer = self.bigledger.get_customer(customer_id)
        history = self.get_interaction_history(customer_id)
        
        # Determine response
        if sentiment.score < -0.5:
            # Escalate negative sentiment
            return self.escalate_to_human(customer_id, message)
        
        # Generate response
        response = self.generate_response(
            intent=intent,
            context={"customer": customer, "history": history}
        )
        
        # Log interaction
        self.log_interaction(customer_id, message, response)
        
        return response
    
    def generate_response(self, intent, context):
        """Generate contextual response"""
        
        if intent.type == "order_status":
            order = self.bigledger.get_latest_order(
                context["customer"].id
            )
            return f"Your order #{order.id} is {order.status}"
        
        elif intent.type == "return_request":
            return self.process_return(context["customer"])
        
        else:
            # Use knowledge base for general queries
            return self.knowledge_base.search(intent.query)

# Deploy the agent
agent = CustomerServiceAgent()
agent.deploy(
    channels=["web_chat", "mobile_app", "email"],
    availability="24/7",
    languages=["en", "zh", "ms"]
)

Inventory Optimization Agent 

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class InventoryOptimizer(Agent):
    """AI agent for inventory optimization"""
    
    def __init__(self):
        super().__init__(name="InventoryOptimizer")
        self.predictor = DemandPredictor()
        self.optimizer = StockOptimizer()
        
    @scheduled(cron="0 6 * * *")  # Daily at 6 AM
    def optimize_inventory(self):
        """Daily inventory optimization"""
        
        # Get current inventory
        inventory = self.bigledger.get_inventory()
        
        # Predict demand
        predictions = self.predictor.predict_demand(
            horizon_days=30,
            confidence_level=0.95
        )
        
        # Optimize stock levels
        recommendations = self.optimizer.optimize(
            current=inventory,
            predicted_demand=predictions,
            constraints={
                "budget": 100000,
                "storage_capacity": 5000,
                "lead_times": self.get_supplier_lead_times()
            }
        )
        
        # Execute recommendations
        for rec in recommendations:
            if rec.confidence > 0.8:
                self.execute_recommendation(rec)
            else:
                self.flag_for_review(rec)
        
        return {
            "optimized_skus": len(recommendations),
            "projected_savings": self.calculate_savings(recommendations)
        }

Troubleshooting

Common Issues

Agent not starting

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# Check logs
aimatrix logs my-agent --tail=100

# Verify configuration
aimatrix validate my-agent

# Test connectivity
aimatrix test-connection --source=bigledger

Poor performance

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# Profile agent
profiler = AgentProfiler(agent)
report = profiler.analyze()

print(f"Bottleneck: {report.bottleneck}")
print(f"Optimization suggestions: {report.suggestions}")

Incorrect predictions

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# Retrain model
agent.retrain(
    data="updated_training_data.csv",
    validation_split=0.2,
    epochs=100
)

# Evaluate performance
metrics = agent.evaluate(test_data)
print(f"New accuracy: {metrics.accuracy:.2%}")

Next Steps

Resources