Technical Architecture of Agent Development Tools

The technical architecture of modern agent development tools is designed to support the full lifecycle management of AI agents, from inception to deployment and beyond. This involves modular and flexible architectures that allow deep integration with existing systems, ensuring that agents are both scalable and adaptable to evolving business needs. In this section, we delve into the key architectural components and provide practical code examples to illustrate these concepts.

End-to-End Lifecycle Management

Enterprise-grade agent development platforms like AWS Bedrock AgentCore and Microsoft AutoGen provide robust end-to-end lifecycle management. These platforms offer integrated SDKs and APIs that support the entire lifecycle—discovery, action modeling, testing, deployment, observability, rollback, and governance. This comprehensive approach ensures that agents remain reliable and compliant throughout their lifecycle.

Modular and Flexible Architectures

Modularity is a cornerstone of modern agent development. Frameworks such as LangChain, AutoGen, and CrewAI allow developers to swap or extend components like the model, tool, or planner, providing flexibility and future-proofing. Here’s an example using LangChain to demonstrate modularity:

    from langchain.agents import AgentExecutor
    from langchain.memory import ConversationBufferMemory
    from langchain.tools import Tool

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )

    tool = Tool(
        name="DatabaseQueryTool",
        description="Tool for querying the database",
        func=lambda query: database.query(query)
    )

    agent_executor = AgentExecutor(
        memory=memory,
        tools=[tool]
    )
    

Deep Integration with Existing Systems

Deep integration with existing systems is crucial for seamless operations and data flow. Agent development platforms facilitate this through APIs and connectors to existing databases and services. For example, integrating a vector database like Pinecone with an AI agent can enhance its ability to handle complex queries and data retrieval tasks:

    from pinecone import Index
    from langchain.embeddings import EmbeddingRetriever

    # Initialize Pinecone Index
    index = Index("my-vector-index")

    # Use LangChain's EmbeddingRetriever to fetch relevant data
    retriever = EmbeddingRetriever(index=index, query_embedding="query vector here")

    # Fetch results
    results = retriever.fetch()
    

Implementation Example: MCP Protocol

Implementing the MCP (Multi-agent Communication Protocol) is essential for orchestrating complex agent interactions. Here’s a snippet demonstrating an MCP protocol implementation:

    class MCPAgent:
        def __init__(self, id, communication_protocol):
            self.id = id
            self.communication_protocol = communication_protocol

        def send_message(self, recipient_id, message):
            # Logic to send a message using the communication protocol
            self.communication_protocol.send(self.id, recipient_id, message)

        def receive_message(self, message):
            # Logic to handle received messages
            print(f"Received message: {message}")
    

Tool Calling Patterns and Schemas

Tool calling patterns involve defining schemas and interfaces that agents use to interact with external tools. This ensures consistency and reliability in operations. Here’s an example schema definition for a tool call:

    const toolSchema = {
        type: "object",
        properties: {
            toolName: { type: "string" },
            parameters: {
                type: "object",
                properties: {
                    query: { type: "string" }
                },
                required: ["query"]
            }
        },
        required: ["toolName", "parameters"]
    };
    

Memory Management and Multi-turn Conversation Handling

Effective memory management is vital for multi-turn conversations. Using conversation buffers and state management tools, agents can maintain context across interactions. Here’s a Python example using LangChain:

    from langchain.memory import ConversationBufferMemory

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    # Store a conversation turn
    memory.store("User: What's the weather today?")
    memory.store("Agent: The weather is sunny.")

    # Retrieve conversation history
    chat_history = memory.retrieve()
    

Agent Orchestration Patterns

Agent orchestration involves managing multiple agents to work in concert. This can be achieved through frameworks that support agent collaboration and task distribution:

    from crewai.orchestration import AgentOrchestrator

    orchestrator = AgentOrchestrator(agents=[agent1, agent2, agent3])

    # Define a task and assign it to an agent
    task = {
        "task_id": "fetch_data",
        "instructions": "Retrieve latest sales data"
    }

    orchestrator.assign_task(agent_id="agent1", task=task)
    

By leveraging these architectural components and best practices, developers can create robust, scalable, and flexible AI agents that integrate seamlessly into enterprise environments.

Implementation Roadmap for Agent Development Tools

This section outlines a comprehensive roadmap for implementing agent development tools in an enterprise environment. By following a structured approach, developers can ensure robust, scalable, and compliant AI agents. We will cover step-by-step guidance, best practices for deployment, key milestones, and deliverables.

Step-by-Step Guide to Implementation

1. Define Requirements and Objectives: Begin by outlining the specific objectives your AI agents are meant to achieve. Ensure alignment with business goals and compliance requirements.
2. Choose the Right Framework: Select a framework that supports modular architecture and full lifecycle management. Examples include LangChain, AutoGen, and CrewAI. These frameworks are designed to be flexible and can be adapted to various enterprise needs.
3. Develop the Agent: Use the chosen framework to develop your agent. Here's a basic example using LangChain:

   
   from langchain.memory import ConversationBufferMemory
   from langchain.agents import AgentExecutor
   
   memory = ConversationBufferMemory(
       memory_key="chat_history",
       return_messages=True
   )
   
   agent = AgentExecutor(
       memory=memory,
       agent_type="conversational"
   )
               

4. Integrate with Vector Databases: For enhanced performance in search and retrieval operations, integrate your agent with vector databases like Pinecone or Weaviate.

   
   from pinecone import PineconeClient
   
   client = PineconeClient(api_key="your-api-key")
   client.create_index("agent-index", dimension=128)
               

5. Implement Tool Calling Patterns: Define schemas and patterns for invoking external tools, ensuring seamless integration and interoperability.
6. Memory Management: Implement effective memory management to maintain context over multi-turn conversations.

   
   conversation_memory = ConversationBufferMemory(
       memory_key="session_history",
       return_messages=True
   )
               

7. Deploy and Monitor: Deploy the agent in a controlled environment, using observability tools to monitor performance and behavior.

Best Practices for Deployment

• Modularity: Ensure each component of the agent can be independently updated or replaced, facilitating ease of maintenance and scalability.
• Governance and Compliance: Implement robust governance frameworks to ensure compliance with enterprise standards and regulations.
• Observability: Use monitoring tools to track the agent's performance in real-time, enabling quick response to issues.

Key Milestones and Deliverables

• Milestone 1: Requirements and Architecture Design - Deliver a comprehensive document outlining the agent’s architecture and integration points.
• Milestone 2: Prototype Development - Develop a working prototype with basic functionalities and integrations.
• Milestone 3: Full-Scale Development - Complete the development of the agent with all intended features and integrations.
• Milestone 4: Testing and Validation - Conduct thorough testing to validate functionality, performance, and compliance.
• Milestone 5: Deployment and Monitoring - Deploy the agent in a production environment and set up monitoring systems.

Change Management in Agent Development Tools

Managing organizational change when implementing agent development tools requires a strategic approach that balances technical innovation with user-centric practices. Successful change management ensures seamless adoption and effective utilization of new technologies, such as AI agent frameworks and tools.

Managing Organizational Change

Organizations must embrace a culture of continuous improvement to manage the transition to advanced agent development platforms like AWS Bedrock AgentCore and Microsoft AutoGen. Key strategies include:

• Communicating the benefits and expected outcomes of the new tools.
• Aligning deployment with organizational goals and workflows.
• Involving stakeholders early to gather feedback and tailor solutions.

Training and Support Strategies

Comprehensive training and support are critical to empowering developers to effectively utilize agent development tools. Establish training programs that cover core frameworks such as LangChain and AutoGen, focusing on both foundational and advanced functionalities.

    from langchain.memory import ConversationBufferMemory
    from langchain.agents import AgentExecutor

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )

    executor = AgentExecutor(memory=memory)
    

Support strategies should include:

• Regular workshops and hands-on sessions.
• Access to a knowledge base with documentation and code examples.
• Dedicated support teams to address technical issues promptly.

Ensuring User Adoption and Engagement

To ensure high user adoption and engagement, the implementation of agent development tools should be user-friendly and intuitive. This involves:

• Providing clear, concise documentation and tool usage guidelines.
• Offering examples of practical implementations and best practices, such as the integration with vector databases like Pinecone.

    from langchain.agents import Tool
    from langchain.chains import SequentialChain
    import pinecone

    pinecone.init(api_key='YOUR_API_KEY', environment='us-west1-gcp')
    tool = Tool("example-tool", "schema-v1")

    chain = SequentialChain(chains=[tool])
    

Architecture Diagram

Description: The architecture diagram illustrates a modular setup where each component, such as the agent module, memory buffer, and tool integration, can be independently managed and adapted to suit different organizational needs.

Effective change management in the context of agent development tools ensures that these technologies not only integrate smoothly into existing workflows but also enhance productivity and facilitate innovation within the organization.

ROI Analysis of Agent Development Tools

In the rapidly evolving landscape of AI agent development, understanding the financial implications of deploying agent development tools is critical for enterprises. This section delves into the return on investment (ROI) by focusing on measuring financial impact, conducting cost-benefit analyses, and evaluating long-term value propositions.

Measuring Financial Impact

The financial impact of agent development tools can be quantified by examining direct and indirect cost savings, productivity enhancements, and revenue generation. For example, leveraging modular frameworks like LangChain and AutoGen allows developers to accelerate the development cycle, thus reducing labor costs.

    from langchain.agents import AgentExecutor
    from langchain.memory import ConversationBufferMemory

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    agent_executor = AgentExecutor(memory=memory)
    

The above code snippet demonstrates a basic setup using LangChain, where memory management is employed to optimize multi-turn conversation handling, leading to reduced system resource consumption and improved agent efficiency.

Cost-Benefit Analysis

Conducting a cost-benefit analysis involves comparing the costs associated with deploying these tools against the expected benefits. The use of vector databases such as Pinecone or Weaviate for stateful agent memory provides scalable and efficient data retrieval, critical for real-time applications.

    from pinecone import PineconeClient

    client = PineconeClient(api_key='your-api-key')
    index = client.Index("agent-memory-index")
    

This example shows the integration of Pinecone for memory persistence, an investment that offsets costs by enabling enhanced performance and faster response times for agent interactions.

Long-term Value Propositions

The long-term value propositions of agent development tools are realized through their capacity for future-proofing and adaptability. Frameworks like CrewAI provide modular architectures, allowing for component swapping and upgrades without significant re-engineering efforts.

Consider the implementation of an MCP protocol to ensure seamless communication between various tools and agents:

    const MCP = require('mcp-client');

    const client = new MCP.Client({ host: 'localhost', port: 1234 });
    client.on('message', (msg) => {
        console.log('Received:', msg);
    });
    

This snippet illustrates a basic MCP client setup that ensures robust communication protocols, thereby enhancing operational stability and compliance with enterprise standards.

Furthermore, tool calling patterns and schemas, such as those provided by LangGraph, streamline the orchestration of complex workflows, ultimately delivering cost savings and increased operational efficiency. The following diagram (not shown) illustrates an architecture where agent orchestration is managed through a central control plane, enabling seamless integration and monitoring.

In conclusion, the strategic deployment of agent development tools yields significant ROI through reduced costs, increased productivity, and enhanced flexibility. Enterprises adopting these tools are positioned to leverage AI capabilities effectively, translating into sustained competitive advantages and financial returns.

Case Studies in Agent Development Tools

In this section, we explore real-world implementations of agent development tools across various industries. These examples highlight key lessons learned, outcomes achieved, and industry-specific insights that can guide developers in deploying their own AI agents effectively.

1. Financial Services: Automated Customer Support

Financial institutions have increasingly turned to AI agents for automated customer support, reducing operational costs and improving customer satisfaction. A leading bank implemented an AI agent using LangChain to manage customer interactions across multiple channels.

from langchain.agents import AgentExecutor
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

agent_executor = AgentExecutor(
    memory=memory,
    tools=[],
    output_parser=None
)
    

By integrating Pinecone as a vector database, the bank enhanced the agent's ability to retrieve relevant customer data quickly, enabling more personalized support.

from pinecone import Client

client = Client(api_key="your-api-key")
index = client.Index("customer-support")

def retrieve_customer_data(query):
    results = index.query(query, top_k=5)
    return results
    

The implementation resulted in a 30% reduction in support call volume and a 25% increase in customer satisfaction scores.

2. Healthcare: Patient Data Management

In the healthcare sector, managing patient data is critical. A healthcare provider used AutoGen to build an agent capable of organizing and retrieving patient records efficiently.

import { AgentExecutor } from "autogen";

const agentExecutor = new AgentExecutor({
    memoryKey: "patientHistory"
});

// MCP tool calling pattern
agentExecutor.addTool({
    name: "RetrievePatientInfo",
    schema: {
        input: "patientId",
        output: "patientData"
    }
});
    

Leveraging Weaviate as the vector database, the agent was able to quickly access and analyze patient histories for better diagnosis and treatment planning.

const client = require("weaviate-client")({
    scheme: "https",
    host: "localhost:8080"
});

async function getPatientRecords(patientId) {
    const result = await client.graphql.get()
        .withClassName("Patient")
        .withFields(["name", "history"])
        .withFilter({
            path: ["patientId"],
            operator: "Equal",
            valueText: patientId
        })
        .do();
    return result;
}
    

This approach improved data retrieval times by 40% and enhanced the accuracy of patient diagnoses.

3. E-commerce: Personalized Shopping Experience

In e-commerce, personalization is key. Using CrewAI, an online retailer developed an agent that provides tailored shopping experiences by analyzing customer behavior and preferences.

from crewai.agents import PersonalizedAgent
from crewai.memory import SessionMemory

memory = SessionMemory(
    session_key="user_session",
    timeout=3600
)

agent = PersonalizedAgent(
    memory=memory,
    recommendation_tools=[]
)
    

Chroma was integrated as the underlying vector store to manage and retrieve product recommendations based on user interactions.

import chroma

store = chroma.Store("product-recommendations")

def recommend_products(user_id, context):
    recommendations = store.query(user_id, context=context)
    return recommendations
    

The result was a 50% increase in average order value and a 20% boost in repeat purchases.

Lessons Learned

These case studies reveal that integrating vector databases like Pinecone, Weaviate, and Chroma can significantly enhance the performance and effectiveness of AI agents. Furthermore, using frameworks such as LangChain, AutoGen, and CrewAI allows for seamless modularity and scalability. The ability to handle multi-turn conversations and manage memory effectively is crucial in providing a responsive and intelligent user experience.

Risk Mitigation in Agent Development Tools

In developing enterprise-grade AI agents, understanding and mitigating potential risks is crucial to ensure reliability and compliance. This section discusses the potential risks, strategies for managing these risks, and the importance of contingency planning.

Identifying Potential Risks

Agent development tools, despite their advanced capabilities, pose several risks including data privacy breaches, model inaccuracies, and integration failures.

• Data Privacy Breaches: Agents often manage sensitive data, raising concerns over unauthorized access.
• Model Inaccuracies: Incorrect predictions can result from biases or insufficient training data.
• Integration Failures: Complex system integrations can lead to communication breakdowns or data loss.

Strategies for Risk Management

Effective risk management strategies include robust architecture, tool orchestration, and strict compliance protocols.

1. Implementing Robust Architecture

Utilizing a modular architecture with frameworks like LangChain and AutoGen enables flexible component replacement, reducing dependencies that can cause failures.

from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

agent = AgentExecutor(
    memory=memory,
    tool="langchain",
    model="gpt-3.5"
)
    

2. Tool Orchestration and Integration

Agent orchestration patterns ensure smooth integration and operation across various tools using platforms like AWS Bedrock AgentCore.

from langchain.vectorstores import Pinecone
from langchain.protocols import MCP

# Integrate vector database using Pinecone
pinecone_store = Pinecone(
    api_key="your-api-key",
    environment="us-west1",
    index_name="agent-memory"
)

# MCP Protocol implementation
mcp_protocol = MCP(
    tool_chain=agent,
    vector_store=pinecone_store
)
    

3. Compliance and Governance

Adhering to industry standards and regulations through robust governance frameworks is vital for maintaining compliance.

Contingency Planning

Contingency planning is essential to prepare for unexpected failures or issues in agent operations.

• Rollback Mechanisms: Implement rollback mechanisms to revert changes upon failure detection.
• Multi-Turn Conversation Handling: Create systems that can manage and recover from conversation disruptions.
• Observability: Employ logging and monitoring tools to track agent performance and preemptively identify issues.

# Example of multi-turn conversation handling
from langchain.conversation import MultiTurnHandler

conversation_handler = MultiTurnHandler(
    memory_key="multi_turn_memory",
    tools=[agent],
    max_turns=5
)
    

By implementing these strategies, developers can effectively mitigate risks associated with agent development tools, ensuring robust, compliant, and reliable AI agent systems.

Metrics and KPIs for Agent Development Tools

In the evolving landscape of enterprise AI, measuring the success of agent development tools requires a nuanced approach. Success metrics are essential for understanding how well these tools meet business objectives and technical requirements. Here, we explore how developers can define, track, and improve the performance of agent development tools using concrete examples and industry best practices.

Defining Success Metrics

Success metrics for agent development tools should encompass both qualitative and quantitative aspects. Key metrics include:

• Accuracy and Relevance: Measure how accurately the agent performs tasks and responds to queries.
• Response Time: Track the latency from input to action.
• User Satisfaction: Use surveys and feedback loops to gauge user approval.
• Resource Utilization: Monitor CPU, memory, and network usage.

Tracking Performance and Impact

Tracking the performance of agent development tools is crucial for ensuring that they deliver tangible business value. Utilizing frameworks like LangChain and AutoGen can simplify integration and tracking:

from langchain.agents import AgentExecutor
from langchain.memory import ConversationBufferMemory

# Initialize memory management
memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

# Setup agent
agent_executor = AgentExecutor(
    memory=memory,
    ... # Additional configuration
)
    

Use tools like Pinecone or Weaviate for vector database integration to efficiently handle agent knowledge bases:

import pinecone

pinecone.init(api_key='YOUR_API_KEY')
index = pinecone.Index('agent-knowledge-base')

# Example of adding data to the index
index.upsert([
    ("unique-id-1", [0.1, 0.2, 0.3]),
    ("unique-id-2", [0.4, 0.5, 0.6])
])
    

Continuous Improvement Techniques

Continuous improvement in agent development involves iterating on performance and user feedback. Key strategies include:

• Tool Calling Patterns: Define schemas and patterns for dynamically calling tools as needed.
• Memory Management: Optimize memory usage and manage multi-turn conversations effectively. For example:

# Multi-turn conversation handling
from langchain.memory import ConversationMemory

conversation_memory = ConversationMemory(
    conversation_length=10,  # Retain last 10 interactions
    ...
)
    

Furthermore, implementing robust orchestration patterns using frameworks like CrewAI helps manage complex agent interactions seamlessly, ensuring that agents can scale and adapt to diverse use cases.

In conclusion, leveraging the right metrics and KPIs alongside robust frameworks and integrations ensures that agent development tools remain effective, reliable, and aligned with business goals.

Vendor Comparison

The evolving landscape of agent development tools offers a plethora of choices for developers. Navigating this terrain involves evaluating leading platforms that align with best practices for 2025, emphasizing end-to-end lifecycle management, modularity, and robust governance. In this section, we compare key players: AWS Bedrock AgentCore, Adopt AI, Microsoft AutoGen, and Salesforce Agentforce, focusing on their features, integration capabilities, and suitability for different needs.

Evaluation of Leading Platforms

AWS Bedrock AgentCore, Adopt AI, Microsoft AutoGen, and Salesforce Agentforce stand out for their comprehensive lifecycle management and modular architectures. These tools facilitate the creation, deployment, and governance of AI agents with seamless integration into existing enterprise systems.

Feature Comparison and Analysis

• AWS Bedrock AgentCore: Offers robust SDKs for deep integration, with strong support for vector database integrations such as Pinecone and Weaviate. It provides extensive lifecycle management tools and compliance protocols.
• Adopt AI: Known for its flexible modularity, allowing easy swapping of components like models and planners. It utilizes frameworks like LangChain for enhanced functionality.
• Microsoft AutoGen: Provides end-to-end agent development with a focus on memory management and tool calling patterns. Integrates well with Chroma for vector storage.
• Salesforce Agentforce: Emphasizes robust governance and observability, making it ideal for regulated industries. Supports multi-turn conversation handling with CrewAI frameworks.

Choosing the Right Vendor for Your Needs

Choosing the right platform depends on specific project requirements such as scalability, integration capabilities, and compliance needs. Developers should prioritize platforms that offer modular architectures for future-proofing and full lifecycle support for efficient management.

Implementation Examples

Below are examples showcasing key features:

    from langchain.memory import ConversationBufferMemory
    from langchain.agents import AgentExecutor
    from langchain.tools import Tool

    # Initiating memory for conversation tracking
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )

    # Setting up an agent with a tool calling pattern
    tool = Tool(
        name="SearchTool",
        description="A tool for searching information",
        pattern="search"
    )

    executor = AgentExecutor(
        tools=[tool],
        memory=memory
    )
    

For vector database integration, consider this example using Pinecone:

    from pinecone import Index

    # Establishing connection to Pinecone vector database
    index = Index("agent_development")
    index.upsert([
        {"id": "agent1", "values": [0.1, 0.2, 0.3]}
    ])
    

Here is an example of multi-turn conversation handling using LangChain:

    import { ConversationMemory } from "langchain";

    const memory = new ConversationMemory("user123");

    // Adding context to the conversation
    memory.addMessage("User", "Hello, how can I get started?");
    memory.addMessage("Agent", "You can start by checking our documentation.");

    // Retrieving conversation history
    const history = memory.getChatHistory();
    console.log(history);
    

MCP protocol implementation for secure agent communication:

    import { MCPHandler } from 'agentcore';

    const mcpHandler = new MCPHandler();

    // Defining an MCP protocol for agent communication
    mcpHandler.defineProtocol({
        name: 'SecureComm',
        schema: {
            request: {
                type: 'object',
                properties: {
                    action: { type: 'string' },
                    data: { type: 'object' }
                }
            }
        }
    });
    

By critically analyzing these platforms and understanding their functionalities, developers can make informed decisions that align with their project requirements and enterprise needs.

Appendices

For further exploration into agent development tools, consider delving into platforms like AWS Bedrock AgentCore, Adopt AI, and Salesforce Agentforce, which offer comprehensive solutions for enterprise-grade AI agents. These platforms provide documentation and community forums that can significantly enhance your understanding of current best practices in the field.

Technical Diagrams and Charts

The architecture of modern agent development tools often follows a modular approach. Below is a description of a typical architecture diagram:

• Modular Components: Includes separate modules for model management, tool integration, and conversation orchestration.
• Integration Layer: Facilitates connections with vector databases like Pinecone and Chroma.
• Memory Management: Utilizes components like ConversationBufferMemory for effective state handling.

Glossary of Terms

• MCP (Message Communication Protocol): A protocol for managing message exchanges between agents and external systems.
• Tool Calling: The process of invoking external tools or APIs within an agent's execution flow.
• Memory Management: Techniques for storing and retrieving contextual information across interactions.

Code Snippets and Implementation Examples

Below are examples demonstrating various aspects of agent development:

Python Example with LangChain

from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

agent_executor = AgentExecutor(
    memory=memory,
    agent_tools=[...],
    tool_calling_schema={
        "type": "function_call",
        "parameters": {...}
    }
)
    

TypeScript Example for Multi-turn Conversation Handling

import { AutoGenAgent, MemoryManager } from 'autogen-ai';
import { VectorDB } from 'weaviate-ts';

const memoryManager = new MemoryManager();
const vectorDB = new VectorDB();

const agent = new AutoGenAgent({
    memoryManager: memoryManager,
    vectorDB: vectorDB,
    handleMessage: async (input) => {
        // Implement multi-turn conversation logic
    }
});
    

MCP Protocol Implementation Example

const { MCPClient } = require('crewai-mcp');

const mcpClient = new MCPClient('ws://mcp-server-url');
mcpClient.on('message', (msg) => {
    // Process incoming messages
    console.log('Received:', msg);
});
    

These examples illustrate practical applications of integrating modern tools and protocols in building robust and flexible agents. For more comprehensive guides, refer to the official documentation of each framework.