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Agentic Design Pattern: Ensemble Committee

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    """
Pattern 013: Ensemble/Committee

Description:
    The Ensemble/Committee pattern uses multiple agents working independently on the same
    task, then aggregates their outputs to reach a final decision. This pattern leverages
    diversity to improve robustness, reduce errors, and provide more reliable results.

Components:
    - Multiple independent agents (diverse models, prompts, or strategies)
    - Aggregation mechanism (voting, averaging, weighted combination)
    - Result synthesis and confidence scoring

Use Cases:
    - Classification tasks requiring high accuracy
    - Prediction problems where uncertainty quantification is important
    - Reducing bias through diverse perspectives
    - Quality assurance through redundancy

LangChain Implementation:
    Uses multiple ChatOpenAI instances with different configurations, implements
    various aggregation strategies (majority voting, weighted voting, consensus),
    and provides confidence metrics based on agreement levels.

Key Features:
    - Diverse agent configurations (temperature, models, prompts)
    - Multiple aggregation strategies
    - Confidence scoring based on agreement
    - Detailed breakdown of individual votes
"""

import os
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from collections import Counter
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

load_dotenv()


class AggregationStrategy(Enum):
    """Aggregation strategies for combining agent outputs."""
    MAJORITY_VOTE = "majority_vote"
    WEIGHTED_VOTE = "weighted_vote"
    CONSENSUS = "consensus"
    AVERAGING = "averaging"


@dataclass
class AgentVote:
    """Represents a single agent's vote/output."""
    agent_id: str
    output: str
    confidence: float
    reasoning: Optional[str] = None


@dataclass
class EnsembleResult:
    """Result from ensemble aggregation."""
    final_output: str
    confidence: float
    individual_votes: List[AgentVote]
    agreement_score: float
    strategy_used: str


class EnsembleAgent:
    """
    Ensemble agent that combines outputs from multiple independent agents.
    
    This implementation creates diverse agents through different configurations
    (temperature, system prompts) and provides multiple aggregation strategies.
    """
    
    def __init__(
        self,
        num_agents: int = 3,
        base_model: str = "gpt-3.5-turbo",
        temperatures: Optional[List[float]] = None
    ):
        """
        Initialize ensemble with multiple agents.
        
        Args:
            num_agents: Number of agents in ensemble
            base_model: Base model for all agents
            temperatures: List of temperatures for diversity (default: [0.3, 0.7, 1.0])
        """
        self.num_agents = num_agents
        self.base_model = base_model
        
        # Create diverse temperature settings if not provided
        if temperatures is None:
            temperatures = [0.3, 0.5, 0.7, 0.9, 1.0][:num_agents]
        
        # Create diverse agents with different temperatures
        self.agents = []
        for i, temp in enumerate(temperatures[:num_agents]):
            agent = ChatOpenAI(
                model=base_model,
                temperature=temp,
                model_kwargs={"seed": i * 42}  # Different seeds for diversity
            )
            self.agents.append(agent)
        
        # Create a meta-agent for confidence estimation
        self.meta_agent = ChatOpenAI(model=base_model, temperature=0.2)
    
    def _estimate_confidence(
        self,
        question: str,
        answer: str,
        reasoning: str
    ) -> float:
        """Estimate confidence score for an individual answer."""
        confidence_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert at evaluating answer quality and confidence."),
            ("user", """Given this question and answer, estimate the confidence level (0.0-1.0).
Consider clarity, completeness, and certainty in the reasoning.

Question: {question}

Answer: {answer}

Reasoning: {reasoning}

Provide only a number between 0.0 and 1.0.""")
        ])
        
        chain = confidence_prompt | self.meta_agent | StrOutputParser()
        
        try:
            confidence_str = chain.invoke({
                "question": question,
                "answer": answer,
                "reasoning": reasoning
            })
            confidence = float(confidence_str.strip())
            return max(0.0, min(1.0, confidence))
        except:
            return 0.5  # Default moderate confidence
    
    def query_agents(
        self,
        question: str,
        system_prompt: Optional[str] = None
    ) -> List[AgentVote]:
        """
        Query all agents independently and collect their votes.
        
        Args:
            question: Question to ask all agents
            system_prompt: Optional system prompt (default: general assistant)
            
        Returns:
            List of agent votes with outputs and confidence scores
        """
        if system_prompt is None:
            system_prompt = "You are a helpful AI assistant. Provide clear, accurate answers."
        
        prompt_template = ChatPromptTemplate.from_messages([
            ("system", system_prompt + "\n\nProvide your answer followed by your reasoning."),
            ("user", "{question}")
        ])
        
        votes = []
        
        for i, agent in enumerate(self.agents):
            chain = prompt_template | agent | StrOutputParser()
            
            try:
                response = chain.invoke({"question": question})
                
                # Split response into answer and reasoning
                parts = response.split("\n", 1)
                answer = parts[0].strip()
                reasoning = parts[1].strip() if len(parts) > 1 else ""
                
                # Estimate confidence
                confidence = self._estimate_confidence(question, answer, reasoning)
                
                vote = AgentVote(
                    agent_id=f"agent_{i}",
                    output=answer,
                    confidence=confidence,
                    reasoning=reasoning
                )
                votes.append(vote)
                
            except Exception as e:
                print(f"Agent {i} error: {e}")
                # Add a low-confidence fallback vote
                votes.append(AgentVote(
                    agent_id=f"agent_{i}",
                    output="Error generating response",
                    confidence=0.0,
                    reasoning=f"Error: {str(e)}"
                ))
        
        return votes
    
    def aggregate_majority_vote(
        self,
        votes: List[AgentVote]
    ) -> Tuple[str, float]:
        """
        Aggregate using simple majority voting.
        
        Returns:
            (winning answer, agreement score)
        """
        # Count votes for each unique answer
        answer_counts = Counter(vote.output for vote in votes)
        most_common = answer_counts.most_common(1)[0]
        winning_answer = most_common[0]
        vote_count = most_common[1]
        
        agreement_score = vote_count / len(votes)
        
        return winning_answer, agreement_score
    
    def aggregate_weighted_vote(
        self,
        votes: List[AgentVote]
    ) -> Tuple[str, float]:
        """
        Aggregate using confidence-weighted voting.
        
        Returns:
            (winning answer, weighted agreement score)
        """
        # Weight each answer by confidence
        answer_weights: Dict[str, float] = {}
        total_confidence = sum(vote.confidence for vote in votes)
        
        for vote in votes:
            if vote.output in answer_weights:
                answer_weights[vote.output] += vote.confidence
            else:
                answer_weights[vote.output] = vote.confidence
        
        # Find answer with highest weighted vote
        winning_answer = max(answer_weights.items(), key=lambda x: x[1])[0]
        winning_weight = answer_weights[winning_answer]
        
        # Calculate weighted agreement score
        agreement_score = winning_weight / total_confidence if total_confidence > 0 else 0.0
        
        return winning_answer, agreement_score
    
    def aggregate_consensus(
        self,
        votes: List[AgentVote],
        question: str
    ) -> Tuple[str, float]:
        """
        Generate consensus answer by synthesizing all votes.
        
        Returns:
            (consensus answer, confidence score)
        """
        # Compile all answers and reasoning
        vote_summary = "\n\n".join([
            f"Agent {vote.agent_id} (confidence: {vote.confidence:.2f}):\n"
            f"Answer: {vote.output}\n"
            f"Reasoning: {vote.reasoning}"
            for vote in votes
        ])
        
        consensus_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert at synthesizing multiple perspectives into a consensus."),
            ("user", """Given the following question and multiple agent responses, create a 
consensus answer that incorporates the best elements from all responses.

Question: {question}

Agent Responses:
{vote_summary}

Provide a consensus answer that:
1. Incorporates accurate information from all agents
2. Resolves any contradictions
3. Provides a clear, unified response

Consensus Answer:""")
        ])
        
        chain = consensus_prompt | self.meta_agent | StrOutputParser()
        
        consensus_answer = chain.invoke({
            "question": question,
            "vote_summary": vote_summary
        })
        
        # Calculate confidence based on average agent confidence
        avg_confidence = sum(vote.confidence for vote in votes) / len(votes)
        
        return consensus_answer.strip(), avg_confidence
    
    def solve(
        self,
        question: str,
        strategy: AggregationStrategy = AggregationStrategy.WEIGHTED_VOTE,
        system_prompt: Optional[str] = None
    ) -> EnsembleResult:
        """
        Solve a problem using the ensemble of agents.
        
        Args:
            question: Question to solve
            strategy: Aggregation strategy to use
            system_prompt: Optional custom system prompt
            
        Returns:
            EnsembleResult with final answer and metadata
        """
        # Get votes from all agents
        votes = self.query_agents(question, system_prompt)
        
        # Aggregate based on strategy
        if strategy == AggregationStrategy.MAJORITY_VOTE:
            final_output, agreement_score = self.aggregate_majority_vote(votes)
            
        elif strategy == AggregationStrategy.WEIGHTED_VOTE:
            final_output, agreement_score = self.aggregate_weighted_vote(votes)
            
        elif strategy == AggregationStrategy.CONSENSUS:
            final_output, agreement_score = self.aggregate_consensus(votes, question)
            
        else:
            raise ValueError(f"Unsupported strategy: {strategy}")
        
        # Calculate overall confidence
        confidence = agreement_score
        
        return EnsembleResult(
            final_output=final_output,
            confidence=confidence,
            individual_votes=votes,
            agreement_score=agreement_score,
            strategy_used=strategy.value
        )


def demonstrate_ensemble():
    """Demonstrate the Ensemble/Committee pattern with various examples."""
    
    print("=" * 80)
    print("ENSEMBLE/COMMITTEE PATTERN DEMONSTRATION")
    print("=" * 80)
    
    # Create ensemble with 5 agents
    ensemble = EnsembleAgent(num_agents=5)
    
    # Test 1: Classification task
    print("\n" + "=" * 80)
    print("TEST 1: Sentiment Classification")
    print("=" * 80)
    
    sentiment_question = """Classify the sentiment of this review as Positive, Negative, or Neutral:

"The product works okay, but the customer service was terrible. Mixed feelings overall."

Your answer should be exactly one word: Positive, Negative, or Neutral."""
    
    result = ensemble.solve(
        sentiment_question,
        strategy=AggregationStrategy.WEIGHTED_VOTE
    )
    
    print(f"\nQuestion: Sentiment classification of mixed review")
    print(f"\nFinal Answer: {result.final_output}")
    print(f"Confidence: {result.confidence:.2f}")
    print(f"Agreement Score: {result.agreement_score:.2f}")
    print(f"Strategy: {result.strategy_used}")
    
    print("\nIndividual Agent Votes:")
    for vote in result.individual_votes:
        print(f"  {vote.agent_id}: {vote.output} (confidence: {vote.confidence:.2f})")
    
    # Test 2: Factual question with consensus
    print("\n" + "=" * 80)
    print("TEST 2: Factual Question with Consensus")
    print("=" * 80)
    
    factual_question = "What are the main causes of climate change?"
    
    result = ensemble.solve(
        factual_question,
        strategy=AggregationStrategy.CONSENSUS
    )
    
    print(f"\nQuestion: {factual_question}")
    print(f"\nConsensus Answer:\n{result.final_output}")
    print(f"\nConfidence: {result.confidence:.2f}")
    print(f"Agreement Score: {result.agreement_score:.2f}")
    
    # Test 3: Multiple choice with majority vote
    print("\n" + "=" * 80)
    print("TEST 3: Multiple Choice with Majority Vote")
    print("=" * 80)
    
    mc_question = """Which of the following is the capital of Australia?
A) Sydney
B) Melbourne
C) Canberra
D) Brisbane

Answer with only the letter (A, B, C, or D)."""
    
    result = ensemble.solve(
        mc_question,
        strategy=AggregationStrategy.MAJORITY_VOTE
    )
    
    print(f"\nQuestion: Capital of Australia (multiple choice)")
    print(f"\nFinal Answer: {result.final_output}")
    print(f"Agreement Score: {result.agreement_score:.2f}")
    
    print("\nIndividual Agent Votes:")
    for vote in result.individual_votes:
        print(f"  {vote.agent_id}: {vote.output}")
    
    # Test 4: Comparison of strategies
    print("\n" + "=" * 80)
    print("TEST 4: Strategy Comparison")
    print("=" * 80)
    
    comparison_question = "Is artificial intelligence dangerous? Answer Yes or No, then explain."
    
    print(f"\nQuestion: {comparison_question}\n")
    
    for strategy in [AggregationStrategy.MAJORITY_VOTE, 
                     AggregationStrategy.WEIGHTED_VOTE,
                     AggregationStrategy.CONSENSUS]:
        
        result = ensemble.solve(comparison_question, strategy=strategy)
        
        print(f"\nStrategy: {strategy.value}")
        print(f"Answer: {result.final_output[:150]}...")
        print(f"Confidence: {result.confidence:.2f}")
        print(f"Agreement: {result.agreement_score:.2f}")
    
    # Summary
    print("\n" + "=" * 80)
    print("SUMMARY")
    print("=" * 80)
    print("""
The Ensemble/Committee pattern demonstrates several key benefits:

1. **Robustness**: Multiple agents reduce the impact of individual errors
2. **Diversity**: Different temperatures create varied perspectives
3. **Confidence Scoring**: Agreement levels indicate reliability
4. **Flexibility**: Multiple aggregation strategies for different use cases

Aggregation Strategies:
- **Majority Vote**: Simple, works well for discrete choices
- **Weighted Vote**: Considers confidence, better for uncertain domains
- **Consensus**: Synthesizes responses, best for complex questions

Use Cases:
- High-stakes classification tasks
- Fact-checking and validation
- Reducing model bias
- Quality assurance through redundancy

The pattern is particularly effective when:
- Individual agent accuracy is moderate but independent
- Diversity in approaches improves results
- Confidence/uncertainty quantification is important
- Explainability of decisions is needed
""")


if __name__ == "__main__":
    demonstrate_ensemble()

Agentic Design Pattern: Ensemble Committee

Pattern 013: Ensemble Committee – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Ensemble Committee pattern implementation.

Pattern Description

Pattern 013: Ensemble/Committee

Description: The Ensemble/Committee pattern uses multiple agents working independently on the same task, then aggregates their outputs to reach a final decision. This pattern leverages diversity to improve robustness, reduce errors, and provide more reliable results.

Components: – Multiple independent agents (diverse models, prompts, or strategies) – Aggregation mechanism (voting, averaging, weighted combination) – Result synthesis and confidence scoring

Use Cases: – Classification tasks requiring high accuracy – Prediction problems where uncertainty quantification is important – Reducing bias through diverse perspectives – Quality assurance through redundancy

LangChain Implementation: Uses multiple ChatOpenAI instances with different configurations, implements various aggregation strategies (majority voting, weighted voting, consensus), and provides confidence metrics based on agreement levels.

Key Features: – Diverse agent configurations (temperature, models, prompts) – Multiple aggregation strategies – Confidence scoring based on agreement – Detailed breakdown of individual votes

File Statistics

  • Total Lines: 483
  • Classes: 4
  • Functions: 1
  • Constants: 0

Module Documentation

"""
Pattern 013: Ensemble/Committee

Description:
    The Ensemble/Committee pattern uses multiple agents working independently on the same
    task, then aggregates their outputs to reach a final decision. This pattern leverages
    diversity to improve robustness, reduce errors, and provide more reliable results.

Components:
    - Multiple independent agents (diverse models, prompts, or strategies)
    - Aggregation mechanism (voting, averaging, weighted combination)
    - Result synthesis and confidence scoring

Use Cases:
    - Classification tasks requiring high accuracy
    - Prediction problems where uncertainty quantification is important
    - Reducing bias through diverse perspectives
    - Quality assurance through redundancy

LangChain Implementation:
    Uses multiple ChatOpenAI instances with different configurations, implements
    various aggregation strategies (majority voting, weighted voting, consensus),
    and provides confidence metrics based on agreement levels.

Key Features:
    - Diverse agent configurations (temperature, models, prompts)
    - Multiple aggregation strategies
    - Confidence scoring based on agreement
    - Detailed breakdown of individual votes
"""

Purpose: This module implements the Ensemble Committee pattern, providing a structured approach to agent design and implementation.

Import Statements

Standard Library Imports

import collections.Counter
import dataclasses.dataclass
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple

Purpose: Core Python functionality: – collections.Counter: Specialized container datatypes – dataclasses.dataclass: Simplified class definitions with @dataclass – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Tuple: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

Classes

Class: AggregationStrategy

Lines 46-51

Inherits from: Enum

Purpose:

Aggregation strategies for combining agent outputs.

Class: AgentVote

Lines 55-60

Purpose:

Represents a single agent's vote/output.

Class: EnsembleResult

Lines 64-70

Purpose:

Result from ensemble aggregation.

Class: EnsembleAgent

Lines 73-348

Purpose:

Ensemble agent that combines outputs from multiple independent agents.

This implementation creates diverse agents through different configurations
(temperature, system prompts) and provides multiple aggregation strategies.

Methods:

__init__(self, num_agents, base_model, temperatures)

Lines 81-113

Purpose:

Initialize ensemble with multiple agents.

Args:
    num_agents: Number of agents in ensemble
    base_model: Base model for all agents
    temperatures: List of temperatures for diversity (default: [0.3, 0.7, 1.0])

Parameters: – num_agents: Num agents – base_model: Language model instance – temperatures: Sampling temperature (0-1)

_estimate_confidence(self, question, answer, reasoning)

Lines 115-147

Purpose:

Estimate confidence score for an individual answer.

Parameters: – question: Question to answer – answer: Answer – reasoning: Reasoning

Returns: float

query_agents(self, question, system_prompt)

Lines 149-206

Purpose:

Query all agents independently and collect their votes.

Args:
    question: Question to ask all agents
    system_prompt: Optional system prompt (default: general assistant)

Returns:
    List of agent votes with outputs and confidence scores

Parameters: – question: Question to answer – system_prompt: Prompt template or text

Returns: <ast.Subscript object at 0x000001A06D17AC50>

aggregate_majority_vote(self, votes)

Lines 208-226

Purpose:

Aggregate using simple majority voting.

Returns:
    (winning answer, agreement score)

Parameters: – votes: Votes

Returns: <ast.Subscript object at 0x000001A06D169190>

aggregate_weighted_vote(self, votes)

Lines 228-255

Purpose:

Aggregate using confidence-weighted voting.

Returns:
    (winning answer, weighted agreement score)

Parameters: – votes: Votes

Returns: <ast.Subscript object at 0x000001A06D6F7A90>

aggregate_consensus(self, votes, question)

Lines 257-304

Purpose:

Generate consensus answer by synthesizing all votes.

Returns:
    (consensus answer, confidence score)

Parameters: – votes: Votes – question: Question to answer

Returns: <ast.Subscript object at 0x000001A06D7038D0>

solve(self, question, strategy, system_prompt)

Lines 306-348

Purpose:

Solve a problem using the ensemble of agents.

Args:
    question: Question to solve
    strategy: Aggregation strategy to use
    system_prompt: Optional custom system prompt

Returns:
    EnsembleResult with final answer and metadata

Parameters: – question: Question to answer – strategy: Strategy – system_prompt: Prompt template or text

Returns: EnsembleResult

Functions

demonstrate_ensemble()

Lines 351-478

Purpose:

Demonstrate the Ensemble/Committee pattern with various examples.

Key Concepts Summary

Pattern Overview

The Ensemble Committee pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

  1. Agent Class: Main implementation of the pattern logic
  2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
  3. Tool Integration: Optional tools for extended functionality
  4. Memory Management: State handling across interactions
  5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

Best Practices Demonstrated

1. Environment Configuration

  • ✅ Use .env files for API keys
  • ✅ Never hardcode credentials
  • ✅ Validate environment variables on startup

2. Error Handling

  • ✅ Graceful degradation on failures
  • ✅ Informative error messages
  • ✅ Logging for debugging

3. Prompt Engineering

  • ✅ Clear, specific instructions
  • ✅ Structured output formats
  • ✅ Few-shot examples when beneficial

4. Code Organization

  • ✅ Separation of concerns (class-based design)
  • ✅ Type hints for clarity
  • ✅ Docstrings for documentation

5. Performance Optimization

  • ✅ Appropriate temperature settings
  • ✅ Token budget management
  • ✅ Caching where applicable

LangChain Components Used

  • ChatOpenAI: OpenAI model interface
  • Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

Performance Considerations

Computational Cost

  • LLM Calls: Primary cost factor (based on tokens)
  • Latency: Depends on model and input size
  • Memory: Minimal for most patterns

Optimization Strategies

  1. Prompt Optimization: Minimize tokens while maintaining clarity
  2. Caching: Cache repeated queries/responses
  3. Model Selection: Use smaller models when appropriate
  4. Batch Processing: Group similar requests
  5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

  • GPT-3.5-turbo: ~$0.002 per request
  • GPT-4: ~$0.03 per request
  • GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

Further Improvements

Production Enhancements

  1. Monitoring: Add logging, metrics, tracing
  2. Validation: Input/output schema validation
  3. Rate Limiting: Implement request throttling
  4. Caching: Redis or in-memory cache for responses
  5. Async Support: Non-blocking operations for scalability

Feature Extensions

  1. Multi-Model Support: Switch between providers
  2. Fallback Models: Graceful degradation strategy
  3. Custom Tools: Domain-specific function calling
  4. Advanced Memory: Vector stores for long-term memory
  5. Evaluation: Automated testing and benchmarking

Code Quality

  1. Testing: Unit tests, integration tests
  2. Documentation: API docs, usage examples
  3. Type Safety: Stricter type hints
  4. Configuration: Externalized config files

Related Patterns

  • Pattern 012
  • Pattern 014
  • Other Multi-Agent Patterns (012-019)

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

Summary

The Ensemble Committee pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

File: 013_ensemble_committee.py
Total Lines: 483
Pattern Category: AI Agent Design Pattern #013

Generated from actual implementation code
For educational and reference purposes

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