​

Agent Training Guide

​

Table of Contents

1. Introduction
2. Knowledge Base vs Rules
3. Base Model vs Finetuned Model
4. Datasets, Training, and Evaluations
5. MCP Tool for Google Drive Documents
6. Best Practices
7. Troubleshooting
8. Conclusion

​

Introduction

• When to use knowledge bases versus rule-based systems
• Choosing between base models and finetuned models
• Creating effective training datasets and evaluation frameworks
• Leveraging MCP (Model Context Protocol) tools for document integration

​

Knowledge Base vs Rules

​

Knowledge Base Approach

​

Advantages:

• Flexibility: Easy to update without retraining
• Scalability: Can handle vast amounts of information
• Context-Aware: Enables semantic search and retrieval
• Dynamic Updates: Real-time information updates possible

​

Disadvantages:

• Retrieval Overhead: May increase response latency
• Context Limitations: Limited by retrieval quality
• Complexity: Requires robust indexing and search infrastructure

​

Best Use Cases:

knowledge_base_ideal_for:
  - FAQ systems
  - Product documentation
  - Dynamic information (prices, inventory)
  - Multi-domain applications
  - Frequently updated content

​

Rule-Based Approach

​

Advantages:

• Predictability: Deterministic outcomes
• Performance: Fast execution without retrieval
• Precision: Exact control over responses
• Auditability: Clear decision paths

​

Disadvantages:

• Rigidity: Hard to scale and maintain
• Limited Flexibility: Cannot handle edge cases well
• Manual Updates: Requires code changes for modifications
• Complexity Growth: Rules become unwieldy as they multiply

​

Best Use Cases:

rules_ideal_for:
  - Compliance requirements
  - Simple decision trees
  - High-stakes operations
  - Regulatory workflows
  - Fixed business logic

​

Hybrid Approach

class HybridAgent:
    def process_query(self, query):
        # Check rules first for critical paths
        if self.check_compliance_rules(query):
            return self.execute_rule_based_action(query)
        
        # Fall back to knowledge base for general queries
        context = self.knowledge_base.retrieve(query)
        return self.generate_response(query, context)

​

Base Model vs Finetuned Model

​

Base Models

​

Characteristics:

• General Purpose: Broad knowledge across domains
• Zero-Shot Capable: Can handle diverse tasks without specific training
• Large Context Windows: Modern base models support extensive context
• Consistent Updates: Benefit from provider improvements

​

When to Use Base Models:

base_model_scenarios:
  - Prototyping and POCs
  - Multi-purpose agents
  - When training data is limited
  - Rapidly changing requirements
  - Cost-sensitive applications

​

Finetuned Models

​

Characteristics:

• Domain Expertise: Superior performance on specific tasks
• Consistency: More predictable outputs
• Efficiency: Often smaller and faster
• Custom Behavior: Tailored to exact requirements

​

When to Use Finetuned Models:

finetuned_model_scenarios:
  - Domain-specific applications
  - High-volume, repetitive tasks
  - When accuracy is critical
  - Custom tone/style requirements
  - Proprietary knowledge encoding

​

Decision Flow: When to Finetune

​

Comparison Matrix

​

Datasets, Training, and Evaluations

​

Dataset Creation

​

Dataset Components:

{
  "training_example": {
    "input": "User query or context",
    "expected_output": "Ideal agent response",
    "metadata": {
      "category": "customer_support",
      "difficulty": "medium",
      "tags": ["refund", "policy"]
    }
  }
}

​

Best Practices for Dataset Creation:

1. Diversity: Cover edge cases and variations
2. Quality: Ensure accuracy and consistency
3. Balance: Represent all categories equally
4. Annotation: Include clear labels and metadata
5. Validation: Have experts review samples

​

Training Process

​

1. Data Preparation

# Example data preprocessing pipeline
def prepare_training_data(raw_data):
    processed_data = []
    for item in raw_data:
        # Clean and normalize text (e.g., lowercase, remove punctuation)
        cleaned_input = normalize_text(item['input'])
        cleaned_output = normalize_text(item['output'])
        
        # Validate data quality (e.g., check for length, language, non-empty)
        if validate_pair(cleaned_input, cleaned_output):
            processed_data.append({
                'input': cleaned_input,
                'output': cleaned_output,
                'metadata': item.get('metadata', {})
            })
    
    # Split into training, validation, and test sets (e.g., 80/10/10)
    return split_data(processed_data)  # train/val/test splits

​

2. Training Configuration

training_config:
  base_model: "claude-3.5-sonnet"  # Updated to recent model
  learning_rate: 1e-5
  batch_size: 32
  epochs: 3
  validation_split: 0.2
  early_stopping:
    patience: 3
    metric: "validation_loss"

​

3. Monitoring Training

• Track loss curves
• Monitor validation metrics
• Check for overfitting
• Evaluate on holdout test set

​

Evaluation Framework

​

Automated Metrics:

evaluation_metrics = {
    "accuracy": calculate_exact_match,
    "bleu_score": calculate_bleu,
    "perplexity": calculate_perplexity,
    "response_time": measure_latency,
    "token_efficiency": count_tokens
}

​

Human Evaluation Criteria:

1. Relevance: Does the response address the query?
2. Accuracy: Is the information correct?
3. Completeness: Are all aspects covered?
4. Tone: Is the style appropriate?
5. Coherence: Is the response well-structured?

​

A/B Testing Framework:

class ABTestFramework:
    def __init__(self, model_a, model_b):
        self.model_a = model_a
        self.model_b = model_b
        self.results = {"model_a": [], "model_b": []}
    
    def run_test(self, test_queries, metric_fn):
        for query in test_queries:
            response_a = self.model_a.generate(query)
            response_b = self.model_b.generate(query)
            
            score_a = metric_fn(query, response_a)
            score_b = metric_fn(query, response_b)
            
            self.results["model_a"].append(score_a)
            self.results["model_b"].append(score_b)
        
        return self.analyze_results()

    def analyze_results(self):
        # Example: Perform statistical test (e.g., t-test)
        # to see if one model is significantly better.
        import numpy as np
        from scipy.stats import ttest_ind

        scores_a = np.array(self.results["model_a"])
        scores_b = np.array(self.results["model_b"])
        
        if len(scores_a) < 2 or len(scores_b) < 2:
            return {"error": "Not enough data to run t-test."}

        t_stat, p_value = ttest_ind(scores_a, scores_b)

        return {
            "mean_score_a": np.mean(scores_a),
            "mean_score_b": np.mean(scores_b),
            "p_value": p_value,
            "winner": "model_a" if np.mean(scores_a) > np.mean(scores_b) else "model_b"
        }

​

Reinforcement Learning in Agent Training

​

Overview

​

Key Advantages:

• Handles Long-Horizon Tasks: RL excels in scenarios requiring multi-step reasoning, such as planning in games or real-world simulations.
• Exploration and Curiosity: Encourages agents to explore novel strategies, reducing compounding errors seen in pure imitation learning.
• Alignment with Human Values: Techniques like RLHF (Reinforcement Learning from Human Feedback) and Constitutional AI align agents with ethical guidelines.

​

Best Practices:

• Combine with Imitation Learning: Start with imitation for basic behaviors, then use RL for refinement.
• Use Reward Models: Train models to evaluate outputs, enabling scalable feedback without constant human input.
• Incorporate Human-in-the-Loop: Platforms like GUIDE allow real-time human feedback for nuanced guidance.
• Benchmark and Avoid Overfitting: Use diverse environments and standardize evaluations to ensure real-world applicability.

​

Example: Training an Agent in a Game Environment

import gym

env = gym.make('CartPole-v1')
# RL training loop example
for episode in range(1000):
    state = env.reset()
    done = False
    while not done:
        action = agent.select_action(state)  # RL policy
        next_state, reward, done, _ = env.step(action)
        agent.update(reward, next_state)  # Update with RL algorithm (e.g., Q-Learning)

​

MCP Tool for Google Drive Documents

​

Overview

​

Setup and Configuration

​

1. Install MCP Server

npm install @modelcontextprotocol/server-gdrive

​

2. Configure Authentication

{
  "google_drive_mcp": {
    "client_id": "your-client-id",
    "client_secret": "your-client-secret",
    "redirect_uri": "http://localhost:3000/callback",
    "scopes": [
      "https://www.googleapis.com/auth/drive.readonly",
      "https://www.googleapis.com/auth/documents.readonly"
    ]
  }
}

​

3. Initialize MCP Tool

const { GoogleDriveMCP } = require('@modelcontextprotocol/server-gdrive');

const driveTool = new GoogleDriveMCP({
  credentials: googleCredentials,
  settings: {
    maxResults: 10,
    includeSharedDrives: true,
    mimeTypes: [
      'application/vnd.google-apps.document',
      'application/vnd.google-apps.spreadsheet',
      'application/pdf'
    ]
  }
});

​

Usage Patterns

​

Document Search

// Search for relevant documents
const searchResults = await driveTool.search({
  query: "customer onboarding procedures",
  filters: {
    modifiedAfter: "2024-01-01",
    owners: ["team@company.com"]
  }
});

​

Content Extraction

// Extract content from a specific document
const documentContent = await driveTool.getDocument({
  documentId: "1234567890abcdef",
  format: "markdown",
  includeMetadata: true
});

​

Integration with Agent

class GoogleDriveAgent:
    def __init__(self, mcp_tool, llm):
        self.mcp_tool = mcp_tool
        self.llm = llm
    
    async def answer_with_docs(self, query):
        # Search relevant documents
        docs = await self.mcp_tool.search({
            "query": query,
            "limit": 5
        })
        
        # Extract content
        context = ""
        for doc in docs:
            content = await self.mcp_tool.get_content(doc.id)
            context += f"\n\n{doc.title}:\n{content[:1000]}"
        
        # Generate response with context
        prompt = f"""
        Based on the following documents, answer the query: {query}
        
        Documents:
        {context}
        """
        
        return await self.llm.generate(prompt)

​

Best Practices

1. Caching: Implement caching for frequently accessed documents
2. Permissions: Respect document permissions and access controls
3. Rate Limiting: Handle API rate limits gracefully
4. Error Handling: Implement robust error handling for network issues
5. Incremental Sync: Use change tokens for efficient updates

​

Best Practices

​

1. Start Simple

• Begin with base models and simple prompts
• Add complexity incrementally
• Measure improvements at each step

​

2. Data Quality Over Quantity

• 1,000 high-quality examples are more valuable than 10,000 mediocre ones.
• Invest in data cleaning and validation:
  • Clarity: Unambiguous input/output pairs.
  • Consistency: Uniform style, format, and tone.
  • Coverage: Represents the full range of expected user interactions, including edge cases.
  • Correctness: Factually accurate and grammatically correct.
• Regularly audit training data to remove outdated or incorrect examples.
• Incorporate Diverse Sources: Use human-synthetic data blends and ensure representativeness to avoid biases (from Toloka guide).

​

3. Continuous Evaluation and Learning

• Set up automated evaluation pipelines to monitor performance on key metrics.
• Regular human evaluation sessions to assess nuanced aspects of quality.
• Monitor production performance closely to catch regressions or new failure modes.
• Implement continuous learning post-deployment for adaptability.

​

4. Version Control

• Use version control for everything: training data, prompts, model configurations, and evaluation results.
• This allows for reproducibility, easy rollbacks, and clear tracking of experiments.
• Tools like Git, DVC (Data Version Control), and MLflow can be invaluable.

​

5. Security Considerations

• Sanitize all user inputs and training data to prevent data poisoning.
• Implement strict access controls for training infrastructure and models.
• Be aware of prompt injection: Design prompts and agent logic to be resilient against adversarial inputs.
• Conduct regular security audits of your entire AI/ML pipeline.
• Align agents with human values using RLHF to prevent misalignment (from arXiv papers).

​

Challenges and Solutions in Agent Training

​

Common Challenges

• Data Scarcity and Quality: Insufficient or biased data leads to poor generalization.
• Misalignment with Human Values: Agents may optimize for wrong objectives, ignoring ethics.
• Overfitting to Benchmarks: High benchmark scores may not translate to real-world performance.
• Exploration in Complex Environments: Agents struggle with long-horizon tasks and sparse rewards.

​

Solutions

• High-Quality Datasets: Prioritize diverse, representative data; use synthetic data generation.
• Alignment Techniques: Employ RLHF, Constitutional AI, and human-in-the-loop feedback.
• Robust Evaluation: Use multi-metric benchmarks and real-world testing (e.g., GUIDE framework for human interactions).
• Advanced Tools: Leverage open-source like PyTorch or proprietary like Azure for scalable training.

​

Troubleshooting

​

Common Issues and Solutions

​

1. Poor Model Performance

symptoms:
  - Inaccurate responses
  - Inconsistent behavior
  - High error rates

solutions:
  - Review training data quality
  - Increase dataset diversity
  - Adjust training parameters
  - Consider different base models

​

2. Knowledge Base Retrieval Issues

symptoms:
  - Irrelevant results
  - Missing information
  - Slow retrieval

solutions:
  - Improve indexing strategy
  - Enhance search queries
  - Optimize embedding models
  - Implement caching

​

3. MCP Integration Problems

symptoms:
  - Authentication failures
  - Timeout errors
  - Missing documents

solutions:
  - Verify credentials
  - Check network connectivity
  - Review permission scopes
  - Implement retry logic

​

Conclusion