Agent Development Guide

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# AutoGen - Static, manual configuration
from autogen import AssistantAgent, UserProxyAgent

# Problem 1: Fixed model selection
assistant = AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4"}  # Always uses GPT-4, even for simple tasks
)

# Problem 2: No learning from interactions
user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding"}
)

# Problem 3: Manual workflow definition
user_proxy.initiate_chat(
    assistant,
    message="Generate a report"  # No context awareness
)

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# LangChain - Rigid chains
from langchain import LLMChain, PromptTemplate

# Problem 1: Static chain construction
template = "Process this: {input}"
prompt = PromptTemplate(template=template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)

# Problem 2: No adaptation to input complexity
result = chain.run(input="simple task")  # Uses same chain
result = chain.run(input="complex task")  # Uses same chain

# Problem 3: Breaks on unexpected inputs
result = chain.run(input="completely new format")  # Fails

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# CrewAI - Predefined roles
from crewai import Agent, Task, Crew

# Problem 1: Static role definition
researcher = Agent(
    role='researcher',
    goal='Research information',
    llm=llm
)

# Problem 2: Fixed team composition
crew = Crew(
    agents=[researcher, writer],  # Always same team
    tasks=[task1, task2]
)

# Problem 3: No dynamic adaptation
result = crew.kickoff()  # Can't adjust mid-execution

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from aimatrix import SuperAgent, AgentConfig

# Self-configuring, learning agent
agent = SuperAgent(
    name="business_assistant",
    capabilities=["analyze", "process", "decide"],
    learning_enabled=True
)

# Automatically handles any request
@agent.handle("process_document")
async def process_any_document(doc):
    """
    Super Agent automatically:
    1. Analyzes document type and complexity
    2. Selects optimal LLM (GPT-4, Claude, Gemini, local)
    3. Builds dynamic processing pipeline
    4. Learns from outcome for improvement
    """
    return await agent.process(doc)

# No workflows to define - it figures it out
result = await agent.execute("Process this invoice")
# Automatically creates optimal execution path

# Handles new scenarios without configuration
result = await agent.execute("Handle this edge case")
# Adapts and learns for next time

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from aimatrix import Agent, LearningConfig, ReinforcementStrategy

class IntelligentAgent(Agent):
    def __init__(self):
        super().__init__(
            learning_config=LearningConfig(
                strategy=ReinforcementStrategy.PPO,
                reward_function=self.calculate_reward,
                update_frequency=100,  # Update policy every 100 interactions
                exploration_rate=0.1
            )
        )
        
        # Agent automatically tracks and learns from:
        # - Task completion rates
        # - Execution time
        # - Resource usage
        # - User satisfaction
        # - Error patterns
    
    def calculate_reward(self, state, action, outcome):
        """Define what success looks like"""
        reward = 0
        
        # Reward successful completion
        if outcome.success:
            reward += 1.0
        
        # Reward efficiency
        if outcome.execution_time < outcome.expected_time:
            reward += 0.5
        
        # Reward cost optimization
        if outcome.llm_cost < outcome.budget:
            reward += 0.3
        
        # Penalize errors
        if outcome.errors:
            reward -= 0.5
        
        return reward
    
    async def execute_task(self, task):
        # Agent automatically:
        # 1. Selects optimal approach based on learned policy
        # 2. Chooses best LLM for this specific task
        # 3. Monitors execution
        # 4. Learns from outcome
        
        result = await self.super_agent.execute(task)
        
        # Learning happens automatically
        # No manual training loops needed
        
        return result

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from aimatrix import ModelSelector, TaskProfile

class AdaptiveModelAgent(Agent):
    def __init__(self):
        super().__init__()
        
        # Define available models and their characteristics
        self.model_selector = ModelSelector(
            models={
                "gpt-4o": {
                    "strengths": ["reasoning", "creativity"],
                    "cost": 0.03,
                    "latency": 2.0
                },
                "claude-3-opus": {
                    "strengths": ["long_context", "analysis"],
                    "cost": 0.015,
                    "latency": 1.5
                },
                "gemini-1.5-pro": {
                    "strengths": ["multimodal", "speed"],
                    "cost": 0.007,
                    "latency": 0.8
                },
                "llama-3-70b": {
                    "strengths": ["privacy", "customizable"],
                    "cost": 0.001,  # Local deployment
                    "latency": 1.0
                }
            }
        )
    
    async def process(self, input_data):
        # Automatic model selection based on task
        task_profile = TaskProfile.analyze(input_data)
        
        # ML model predicts best LLM for this task
        optimal_model = self.model_selector.select(
            task_type=task_profile.type,
            complexity=task_profile.complexity,
            context_length=len(input_data),
            urgency=task_profile.urgency,
            budget=task_profile.budget
        )
        
        # Execute with selected model
        result = await self.execute_with_model(optimal_model, input_data)
        
        # System learns from outcome
        self.model_selector.record_performance(
            model=optimal_model,
            task=task_profile,
            performance=result.metrics
        )
        
        return result

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# ❌ Workflow Agent (n8n style) - Static, Brittle
class WorkflowAgent:
    def process_invoice(self, invoice):
        # Rigid, predefined steps
        if invoice.type == "TypeA":
            return self.workflow_a(invoice)
        elif invoice.type == "TypeB":
            return self.workflow_b(invoice)
        else:
            raise Exception("Unknown invoice type")  # Fails
    
    def workflow_a(self, invoice):
        # Hard-coded steps
        step1 = self.extract_data(invoice)
        step2 = self.validate(step1)
        step3 = self.enter_to_system(step2)
        return step3

# ✅ Agentic Workflow (AIMatrix) - Dynamic, Adaptive
class AgenticWorkflow:
    async def process_invoice(self, invoice):
        # Understands intent and context
        understanding = await self.understand_context(invoice)
        
        # Dynamically determines steps needed
        plan = await self.create_execution_plan(understanding)
        
        # Executes with appropriate agents
        result = await self.execute_adaptive_plan(plan)
        
        # Learns for next time
        await self.learn_from_execution(invoice, plan, result)
        
        return result
    
    async def understand_context(self, invoice):
        # Uses AI to understand:
        # - Document structure
        # - Business rules
        # - Historical patterns
        # - Current requirements
        return await self.llm.analyze(invoice)
    
    async def create_execution_plan(self, context):
        # Dynamically creates plan based on:
        # - Current situation
        # - Available resources
        # - Past successes
        # - Business constraints
        return await self.planner.generate(context)

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# Traditional Workflow Approach (n8n/Zapier style)
def traditional_invoice_workflow():
    """
    Problems:
    - Need separate workflow for each vendor
    - Breaks when format changes
    - Can't handle exceptions
    - No learning or improvement
    """
    workflows = {
        "vendor_a": workflow_vendor_a,
        "vendor_b": workflow_vendor_b,
        "vendor_c": workflow_vendor_c,
        # ... hundreds more
    }
    
    # Manual mapping required
    return workflows[vendor_type](invoice)  # Fails if unknown vendor

# AIMatrix Agentic Approach
async def aimatrix_invoice_processing(invoice):
    """
    Benefits:
    - Handles any vendor automatically
    - Adapts to format changes
    - Manages exceptions intelligently
    - Improves with each processing
    """
    # Super Agent figures it out
    agent = SuperAgent()
    
    # Understands invoice regardless of format
    understanding = await agent.understand(invoice)
    
    # Determines optimal processing path
    # No predefined workflows needed!
    result = await agent.process(
        invoice,
        context=understanding,
        learn=True  # Improves for next time
    )
    
    return result

# Example execution
invoice_new_vendor = load_invoice("never_seen_before.pdf")
result = await aimatrix_invoice_processing(invoice_new_vendor)
# ✅ Successfully processed without any configuration!

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from aimatrix import ProductionAgent, Monitoring, Security

class EnterpriseAgent(ProductionAgent):
    def __init__(self):
        super().__init__(
            # Automatic monitoring
            monitoring=Monitoring(
                metrics=["latency", "accuracy", "cost"],
                alerting=True,
                dashboard=True
            ),
            
            # Built-in security
            security=Security(
                authentication="oauth2",
                encryption="aes-256",
                audit_logging=True,
                pii_detection=True
            ),
            
            # Reinforcement learning
            learning=LearningConfig(
                enabled=True,
                strategy="ppo",
                update_frequency=1000
            ),
            
            # Model optimization
            model_selection=ModelSelectionConfig(
                auto_select=True,
                cost_optimize=True,
                latency_target=2000  # ms
            )
        )
    
    @agent.tool("process_business_document")
    async def process_document(self, doc, context):
        """
        Automatically:
        - Selects optimal LLM
        - Creates processing pipeline
        - Monitors performance
        - Learns from outcome
        """
        # Agent handles everything
        return await self.execute(doc, context)
    
    @agent.error_handler
    async def handle_failure(self, error, context):
        """
        Intelligent error recovery:
        - Analyzes failure
        - Attempts recovery strategies
        - Learns from failures
        - Escalates if needed
        """
        recovery_result = await self.recover(error, context)
        
        # Learn from failure
        await self.learn_from_failure(error, recovery_result)
        
        return recovery_result

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from aimatrix import BusinessIntegration, MCP

class BusinessSystemAgent(Agent):
    def __init__(self):
        super().__init__()
        
        # Automatic MCP integration
        self.integrations = BusinessIntegration(
            systems=["sap", "salesforce", "quickbooks", "shopify"],
            auto_discover=True,
            credential_manager=True
        )
    
    async def execute_business_logic(self, request):
        """
        Example: "Create order for customer John for 10 units of SKU-123"
        
        Agent automatically:
        1. Understands intent
        2. Identifies systems involved
        3. Orchestrates across systems
        4. Handles errors and edge cases
        5. Learns optimal paths
        """
        
        # Parse intent (any natural language)
        intent = await self.understand_intent(request)
        
        # Identify required systems
        systems = await self.identify_systems(intent)
        
        # Create execution plan
        plan = await self.create_plan(intent, systems)
        
        # Execute across systems
        async with self.transaction() as tx:
            for step in plan.steps:
                result = await self.execute_step(step, tx)
                
                # Adaptive execution
                if result.requires_adjustment:
                    plan = await self.adjust_plan(plan, result)
            
            # Commit transaction
            await tx.commit()
        
        # Learn from execution
        await self.record_learning(request, plan, result)
        
        return result

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from aimatrix import TeamBuilder, AgentPool

class OrchestratedTeam:
    def __init__(self):
        self.team_builder = TeamBuilder()
        self.agent_pool = AgentPool(max_agents=100)
    
    async def solve_complex_problem(self, problem):
        """
        Dynamically assembles and coordinates agent teams
        """
        # Analyze problem complexity
        analysis = await self.analyze_problem(problem)
        
        # Dynamically build team
        team = await self.team_builder.assemble(
            required_capabilities=analysis.capabilities,
            optimization_goal="accuracy",  # or "speed", "cost"
            max_agents=10
        )
        
        # Coordinate execution
        coordinator = await self.create_coordinator(team)
        
        # Execute with parallel processing
        results = await coordinator.execute_parallel(
            problem,
            merge_strategy="consensus"  # or "best", "weighted"
        )
        
        # Learn optimal team compositions
        await self.team_builder.learn(
            problem_type=analysis.type,
            team_composition=team,
            performance=results.metrics
        )
        
        return results

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from aimatrix import ImprovementPipeline, ExperienceReplay

class SelfImprovingAgent(Agent):
    def __init__(self):
        super().__init__()
        
        self.improvement_pipeline = ImprovementPipeline(
            experience_buffer_size=10000,
            batch_size=32,
            learning_rate=0.001,
            update_frequency=100
        )
    
    async def execute_with_learning(self, task):
        # Record initial state
        state = await self.capture_state(task)
        
        # Select action based on learned policy
        action = await self.select_action(state)
        
        # Execute action
        result = await self.execute_action(action, task)
        
        # Calculate reward
        reward = self.calculate_reward(result)
        
        # Store experience
        self.improvement_pipeline.store(
            state=state,
            action=action,
            reward=reward,
            next_state=await self.capture_state(result)
        )
        
        # Periodic learning
        if self.improvement_pipeline.should_update():
            await self.improvement_pipeline.update_policy()
        
        return result

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# ❌ Don't over-specify
agent = Agent(
    specific_model="gpt-4",  # Don't force model
    fixed_pipeline=[step1, step2, step3]  # Don't predefine steps
)

# ✅ Let it adapt
agent = SuperAgent(
    capabilities=["analyze", "process"],
    learning_enabled=True  # Let it figure out optimal approach
)

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def good_reward_function(outcome):
    """Clear signals for learning"""
    reward = 0
    
    # Business metrics
    reward += outcome.revenue_impact * 0.4
    reward += outcome.cost_savings * 0.3
    reward += outcome.user_satisfaction * 0.2
    reward += outcome.speed_improvement * 0.1
    
    return reward

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# Built-in monitoring
agent = Agent(
    monitoring={
        "metrics": ["accuracy", "latency", "cost"],
        "dashboard": True,
        "alerts": {
            "accuracy_drop": 0.1,
            "latency_spike": 2000
        }
    }
)

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# AutoGen code
from autogen import AssistantAgent
agent = AssistantAgent(name="assistant", llm_config=config)

# AIMatrix equivalent (much more powerful)
from aimatrix import SuperAgent
agent = SuperAgent(name="assistant", auto_configure=True)
# Automatically selects models, learns, and improves

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# LangChain code
from langchain import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# AIMatrix equivalent (self-building chains)
from aimatrix import Agent
agent = Agent()
result = await agent.execute(task)  # Builds optimal chain automatically

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# CrewAI code
from crewai import Crew
crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])

# AIMatrix equivalent (dynamic teams)
from aimatrix import SuperAgent
agent = SuperAgent()
result = await agent.solve(problem)  # Assembles team automatically