Business Context

In today's rapidly evolving enterprise landscape, event sourcing has emerged as a critical architectural pattern that aligns closely with modern business objectives. It enables organizations to build resilient, scalable systems that can robustly handle changes and ensure data integrity. By capturing every state change as a sequence of events, businesses can achieve a high degree of traceability and auditability, which is essential for compliance and strategic decision-making.

Aligning Event Sourcing with Business Objectives

Event sourcing allows enterprises to align technical solutions with business goals, such as enhancing customer experience, improving operational efficiency, and maintaining regulatory compliance. By designing atomic, self-describing events that contain all necessary business-context data, organizations ensure that their systems are future-proofed and adaptable. This approach also facilitates seamless integration with AI-driven tools and multi-agent architectures, providing a foundation for advanced analytics and machine learning applications.

Use Cases Across Various Industries

Event sourcing finds applications across numerous industries. In finance, it supports transaction auditing and fraud detection; in healthcare, it enhances patient data management and compliance with data protection regulations. Retailers leverage event sourcing for real-time inventory tracking and personalized customer experiences. The pattern's versatility allows it to be adapted to virtually any domain where complex state management and auditability are crucial.

Technical Implementation Examples

For developers implementing event sourcing agents, leveraging frameworks like LangChain and CrewAI can streamline development. These frameworks facilitate the encapsulation of business logic with event handling routines within agents. Below is an example of setting up a conversation buffer memory using LangChain. This memory setup is crucial for maintaining context in multi-turn conversations:

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

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

Moreover, integrating with vector databases like Pinecone enables efficient storage and retrieval of event data, enhancing system performance. Here's a quick illustration of how you might integrate Pinecone for vector storage:

    import pinecone

    # Initialize Pinecone
    pinecone.init(api_key='your-api-key', environment='us-west1-gcp')

    # Create a new index
    index = pinecone.Index("event-data")

    # Insert data
    index.upsert(items=[
        ("event1", [0.1, 0.2, 0.3]),
        ("event2", [0.4, 0.5, 0.6])
    ])
    

These examples demonstrate the practical application of event sourcing in modern enterprises, showcasing how event-driven architectures can be leveraged to align technical implementations with strategic business goals. As businesses continue to evolve, the adoption of these practices is expected to grow, making event sourcing an indispensable component of enterprise systems.

Implementation Roadmap for Event Sourcing Agents

Deploying event sourcing agents in enterprise systems requires a structured approach to ensure robust, scalable, and efficient systems. This roadmap outlines key phases, milestones, and resource allocations necessary for successful implementation. We'll delve into architecture, code examples, and best practices to guide developers through this process.

Phase 1: Planning and Design

The planning phase is crucial for setting the foundation. Focus on designing atomic, self-describing events that are explicit and contain all the necessary business-context data. This includes:

• Defining standardized naming conventions.
• Including metadata such as timestamps, user IDs, and correlation vectors for traceability.

Consider using agent frameworks like CrewAI or AutoGen to encapsulate business logic and event handling routines.

Phase 2: Development and Integration

During development, leverage frameworks and tools to streamline the creation and management of event sourcing agents. Here's a Python code example using LangChain for memory management:

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_name="EventSourcingAgent"
)
    

Integrate with vector databases like Pinecone for efficient data retrieval and storage, crucial for handling large event streams:

from pinecone import PineconeClient

client = PineconeClient(api_key='your-api-key')
index = client.Index('event-source-index')
index.upsert([("eventID", {"eventData": "data"})])
    

Phase 3: Deployment

Deploy the agents ensuring robust communication protocols. Implement the MCP protocol for secure and efficient messaging:

class MCPClient:
    def __init__(self, endpoint):
        self.endpoint = endpoint

    def send_event(self, event):
        # Send event to MCP endpoint
        pass
    

Ensure agents can handle tool calling patterns and schemas for dynamic interactions:

def call_tool(tool_name, parameters):
    # Define tool calling schema
    tool_schema = {"tool": tool_name, "params": parameters}
    # Execute tool call
    pass
    

Phase 4: Testing and Optimization

Conduct thorough testing focusing on multi-turn conversation handling and memory management:

def handle_conversation(input_text, memory):
    # Process input and update memory
    response = memory.process(input_text)
    return response
    

Optimize performance with snapshotting and scalable projections for efficient event replay and state reconstruction.

Phase 5: Monitoring and Maintenance

After deployment, continuously monitor the system using advanced monitoring tools to ensure stability and performance. Implement modular agent orchestration patterns to facilitate maintenance and scalability:

class AgentOrchestrator:
    def __init__(self, agents):
        self.agents = agents

    def orchestrate(self):
        # Orchestrate agent interactions
        pass
    

Team Roles and Resource Allocation

A successful deployment requires a well-structured team with clear roles:

• Project Manager: Oversees the project timeline and resource allocation.
• Lead Developer: Guides the technical implementation and ensures code quality.
• Data Engineer: Manages data integration and storage solutions.
• Quality Assurance: Conducts testing and validation of the system.

Allocate resources efficiently to cover all aspects of development, from initial planning to ongoing maintenance.

This roadmap provides a comprehensive guide for enterprises to implement event sourcing agents effectively, aligning with best practices and technological advancements in 2025.

Change Management in Event Sourcing Agents

Adopting event sourcing in an organization requires a keen focus on change management to ensure seamless integration and minimal disruption. This involves handling organizational change, providing training and support to teams, and deploying effective communication strategies. Below, we explore these dimensions, accompanied by implementation details and code snippets.

Handling Organizational Change

When introducing event sourcing agents, it is crucial to align the technical transition with organizational objectives. This can be achieved by incremental adoption rather than a big-bang approach. Ensure that project managers and stakeholders are involved in every step, facilitating a culture of transparency and cooperation.

Training and Support for Teams

Comprehensive training programs are essential to equip teams with the necessary skills to operate new systems efficiently. Utilizing frameworks like LangChain and CrewAI can simplify the development of event sourcing agents. For example, training sessions can include building agents that utilize memory management and multi-turn conversation handling:

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

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

  agent_executor = AgentExecutor(
      agent=CustomAgent(), memory=memory
  )
  

Communication Strategies

Consistent and clear communication is vital during the transition phase. Establishing forums where feedback can be shared and queries addressed ensures that teams feel supported. Additionally, using communication tools integrated with agent frameworks can enhance efficiency. Consider the following example for tool calling patterns:

  const agent = new CrewAI.Agent();
  const toolPattern = {
    name: "notifyEvent",
    parameters: {
      type: "event",
      timestamp: Date.now()
    }
  };

  agent.callTool(toolPattern);
  

Integration with vector databases like Pinecone can also enhance event management by providing scalable and efficient data access:

  import pinecone

  pinecone.init(api_key="your-api-key", environment="environment-name")

  index = pinecone.Index("event-index")
  index.upsert([
      {"id": "event1", "values": [0.1, 0.2, 0.3]},
      {"id": "event2", "values": [0.4, 0.5, 0.6]}
  ])
  

By focusing on these change management aspects, organizations can effectively integrate event sourcing agents, improving both technological and business processes. Such integration ensures the alignment of technical capabilities with strategic goals, bolstering overall enterprise resilience.

ROI Analysis of Event Sourcing Agents

Event sourcing is a pattern that records changes to an application’s state as a sequence of immutable events. This approach offers a detailed audit trail and allows for state reconstruction, which can be invaluable for debugging, compliance, and business intelligence. In this section, we will explore the cost-benefit analysis of implementing event sourcing agents, their long-term financial impacts, and enhancements in business efficiency.

Cost-Benefit Analysis

The initial cost of adopting event sourcing includes the design and setup of the event store, the integration of event-driven architectures, and potential refactoring of existing systems. However, the benefits often outweigh these costs through improved data integrity, enhanced system reliability, and enriched analytics capabilities. For example, utilizing frameworks like LangChain for agent orchestration and incorporating vector databases like Pinecone for efficient data retrieval can streamline operations.

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

  memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
  vector_store = Pinecone(api_key="your-api-key", index_name="events")

  agent_executor = AgentExecutor(memory=memory, vectorstore=vector_store)
  

Long-term Financial Impacts

Long-term financial impacts include reduced costs associated with data recovery and compliance audits, as event sourcing naturally supports these processes. Additionally, businesses can leverage historical event data to drive predictive analytics and informed decision-making. Implementing Multi-Contextual Protocols (MCP) ensures robust inter-agent communication, facilitating better coalition management and strategic planning.

  import { MCPClient } from 'langgraph';

  const client = new MCPClient({
    host: 'mcp.example.com',
    protocol: 'mcp',
    port: 443
  });

  client.sendEvent({ type: 'OrderProcessed', data: { orderId: 12345 } });
  

Enhancements in Business Efficiency

By utilizing event sourcing agents, businesses can achieve higher efficiency through modular and scalable architectures. Frameworks such as CrewAI and AutoGen enable the encapsulation of business logic and event handling routines within agents. These agents can subscribe to specific domains of events, allowing for distributed processing and improved resource management.

The following architecture diagram (not shown here) illustrates how agents interact with an event bus, enabling real-time processing and feedback loops. This design promotes rapid response to business events and aligns with compliance objectives by ensuring traceability and accountability.

Moreover, memory management and multi-turn conversation handling are critical for maintaining context and delivering consistent results. Here's how you can handle multi-turn conversations:

  from langchain.memory import ConversationBufferMemory

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

  def handle_conversation(input_message):
      response = memory.process(input_message)
      return response
  

In conclusion, while the initial investment in event sourcing agents can be significant, the long-term benefits in terms of efficiency, compliance, and strategic insight make it a worthwhile endeavor for enterprises aiming for sustainable growth and competitive advantage.

Case Studies: Successful Implementations of Event Sourcing Agents

Event sourcing agents have become pivotal in modern enterprise systems, providing robust solutions for tracking changes and enabling seamless system evolution. Here, we explore several case studies, showcasing successful implementations, the lessons learned, and best practices specific to various industries.

Example 1: Retail Industry with CrewAI and Pinecone Integration

A leading retail company leveraged CrewAI to redesign its inventory management system using a robust event sourcing architecture. By integrating CrewAI with Pinecone for vector database storage, the company achieved real-time tracking of stock levels and demand forecasting.

    from crewai.agents import RetailAgent
    from pinecone import VectorDatabase

    # Initialize vector database
    pinecone_db = VectorDatabase(index_name='inventory')

    class InventoryAgent(RetailAgent):
        def handle_event(self, event):
            # Code for processing inventory-related events
            self.update_inventory(event)

    # Example event processing
    agent = InventoryAgent()
    event = {"type": "STOCK_UPDATE", "data": {"item_id": "123", "quantity": 50}}
    agent.handle_event(event)
    

Lessons Learned: Critical success factors included ensuring atomic event design and employing snapshotting techniques to optimize performance. The use of metadata helped in tracing events for compliance purposes. The team also learned the importance of aligning agent patterns with business objectives to ensure scalability.

Example 2: Financial Services with LangChain and Chroma

In the financial services sector, a company implemented event sourcing for transaction processing using LangChain and Chroma. This allowed them to efficiently manage transaction history and enhance fraud detection capabilities.

    from langchain.agents import TransactionAgent
    from chroma import VectorStore

    vector_store = VectorStore(index_name='transactions')

    class FinancialTransactionAgent(TransactionAgent):
        def handle_event(self, event):
            # Code for processing financial transactions
            self.process_transaction(event)

    # Example event
    transaction_event = {"type": "TRANSACTION", "data": {"transaction_id": "txn_456", "amount": 1000}}
    agent = FinancialTransactionAgent()
    agent.handle_event(transaction_event)
    

Best Practices: The integration of vector databases like Chroma demonstrated increased efficiency in handling historical transaction queries. The team emphasized the importance of robust security measures and real-time monitoring to ensure compliance with financial regulations.

Industry-Specific Insights

For industries like logistics and healthcare, employing event sourcing agents has allowed for significant improvements in operational efficiency and data accuracy. AutoGen and LangGraph have been instrumental in orchestrating complex multi-agent systems, improving decision-making processes and enabling real-time analytics.

    from langgraph.agents import LogisticsAgent
    from autogen import EventOrchestrator

    orchestrator = EventOrchestrator()

    class ShipmentAgent(LogisticsAgent):
        def handle_event(self, event):
            # Code to handle shipment-related events
            self.track_shipment(event)

    # Multi-turn conversation handling
    orchestrator.add_agent(ShipmentAgent())
    orchestrator.process_event(event)
    

Implementation Insights: The use of AutoGen for orchestrating events across multiple agents has been particularly effective in complex environments. Lessons learned highlight the necessity of implementing efficient memory management and multi-turn conversation handling for scalable and responsive systems.

Risk Mitigation for Event Sourcing Agents

Event sourcing agents, while powerful, introduce specific risks that require careful management. This section explores potential risks, strategies to minimize their impact, and the importance of continuous monitoring and improvement.

Identifying Potential Risks

The primary risks involve data consistency, event replay issues, and system performance under high load. Specific attention should be given to memory management and multi-turn conversation handling within agent orchestration patterns.

Strategies for Minimizing Impact

To mitigate these risks, developers should employ robust event design and utilize appropriate frameworks. For example, using frameworks like LangChain and CrewAI can help manage agent orchestration and memory utilization efficiently.

from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.vectorstores import Pinecone
from langchain.protocols.mcp import MCPAgent

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

pinecone_index = Pinecone.connect('your-pinecone-api-key')
mcp_agent = MCPAgent(index=pinecone_index)

def process_event(event):
    with agent_executor:
        mcp_agent.execute(event)
    

Continuous Monitoring and Improvement

Continuous monitoring is crucial for detecting issues early and improving the system over time. Implementing logging and tracing tools, along with regularly reviewing event handling patterns, ensures system reliability and performance.

Here is a high-level architecture diagram description:

• Event Producers: Capture and publish domain events.
• Agent Handlers: Process events using frameworks like CrewAI, ensuring business logic and event handling are decoupled.
• Vector Database: Integrate with a vector database like Pinecone to store and retrieve complex event data efficiently.
• Monitoring Layer: Employ tools for tracking event flow and performance metrics.

By adhering to these strategies, developers can significantly reduce risks associated with event sourcing agents and ensure their systems remain resilient and scalable.

Governance

In the implementation of event sourcing agents, governance plays a critical role in ensuring compliance with industry standards, maintaining data security, and developing comprehensive policies for event management. This section delves into the aspects of compliance, data governance, and policy development for event management within the context of modern enterprise systems.

Compliance with Industry Standards

Compliance is paramount in event sourcing, ensuring that all events adhere to industry standards like ISO/IEC 27001 for information security. Implementing standardized protocols helps maintain data integrity and traceability. Here’s an example snippet demonstrating how to use LangChain in Python for managing event compliance:

    from langchain.security import ComplianceManager

    compliance_manager = ComplianceManager(standards=["ISO/IEC 27001"])
    event_data = {"event_type": "user_login", "user_id": 12345}

    if compliance_manager.is_compliant(event_data):
        print("Event is compliant with industry standards.")
    else:
        print("Event does not meet compliance requirements.")
    

Data Governance and Security Protocols

Effective data governance involves implementing robust security protocols to safeguard sensitive information. Security measures should be integrated at the architectural level, ideally with support from frameworks like AutoGen for agentic systems. Below is a diagram description and a Python snippet using LangChain to demonstrate data governance:

Diagram Description: The architecture diagram shows a multi-agent system with secure data flows between event sources, agents, and the central event store, ensuring encrypted communication layers via TLS.

    from langchain.security import SecureAgent
    from langchain.database import VectorStore

    agent = SecureAgent(security_protocol="TLS")
    vector_db = VectorStore(database_type="Pinecone")

    agent.connect_to_database(vector_db)
    

Policy Development for Event Management

Developing clear policies for event management ensures consistency and reliability across systems. Policies should be codified into agent behaviors using a policy framework. Here's a TypeScript example using CrewAI for implementing event policies:

    import { EventPolicy, CrewAI } from 'crewai';

    const policy: EventPolicy = {
        maxRetryAttempts: 3,
        eventProcessingTimeout: 5000,
    };

    const agent = new CrewAI.Agent({ policy });
    agent.processEvent({ type: "data_update", data: { id: 1 } });
    

Advanced Implementation Examples

Beyond basic governance, advanced implementations involve handling multi-turn conversations and orchestrating multiple agents to work collaboratively. Here is a Python example utilizing LangChain for memory management and conversation handling:

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

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )
    executor = AgentExecutor(memory=memory)
    executor.handle_conversation("What updates have been processed today?")
    

In summary, effective governance in event sourcing agents involves a multi-faceted approach encompassing compliance, data governance, and policy development, all embedded within the architectural design to ensure secure, efficient, and compliant systems.

Metrics and KPIs for Event Sourcing Agents

In the realm of event sourcing agents, identifying and employing the right metrics and KPIs is crucial for gauging success. These indicators measure the effectiveness and efficiency of event sourcing implementations and facilitate continuous improvement. By leveraging frameworks like LangChain, AutoGen, and integrating with vector databases such as Pinecone or Weaviate, developers can optimize their agent architectures for higher performance and reliability.

Key Performance Indicators for Success

Success in event sourcing is often measured by the accuracy, speed, and reliability of event handling. Key performance indicators include:

• Event Processing Latency: The time taken for an event to be processed and persisted.
• Event Throughput: The number of events processed per second.
• Data Consistency: Ensuring that the state derived from event replay matches the expected state.
• Error Rate: The frequency of event processing failures.

Measuring Effectiveness and Efficiency

Implementing robust monitoring and logging is essential. For example, integrating with vector databases like Pinecone allows for efficient query and retrieval of event data. A Python implementation might look like this:

    from pinecone import init, Index

    # Initialize Pinecone
    init(api_key='YOUR_API_KEY')
    index = Index("events")

    # Example: Storing and querying events
    event = {"event_id": "123", "payload": "data"}
    index.upsert([(event["event_id"], event)])

    # Query
    query_result = index.query("123")
    

Monitoring tools, combined with frameworks like LangChain, facilitate the efficient handling of multi-turn conversations and memory management:

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

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

Continuous Improvement Metrics

Continuous improvement is driven by metrics such as deployment frequency, changes in processing efficiency, and feedback cycle times. By utilizing advanced agent orchestration patterns, such as those provided by CrewAI, developers can track and iterate on agent performance:

    from crewai.agents import Orchestrator

    orchestrator = Orchestrator(agents=[AgentExecutor(memory=memory)])
    orchestrator.run()
    

To further enhance event sourcing systems, adherence to the MCP protocol ensures secure and standardized messaging between agents:

    // MCP Protocol Implementation Example
    const mcpMessage = {
        type: "event",
        eventId: "456",
        data: { key: "value" },
        metadata: { timestamp: Date.now() }
    };
    

Embedding these metrics into the design and operation of event sourcing agents provides a framework for ongoing performance tuning and scalability, aligning with best practices for 2025.

Vendor Comparison of Event Sourcing Agents

In 2025, the landscape of event sourcing frameworks offers developers several robust options, each with unique strengths and weaknesses. This section delves into the leading agent frameworks such as LangChain, AutoGen, CrewAI, and LangGraph, comparing their capabilities and offering insights into selecting the right solution for your needs.

Leading Agent Frameworks

LangChain stands out for its seamless integration with vector databases like Pinecone, allowing efficient retrieval and storage of event data. The framework supports modular design and excels in performance optimization through techniques like snapshotting.

    from langchain.vectorstores import Pinecone
    from langchain.agents import AgentExecutor

    vector_store = Pinecone(
        api_key="your-pinecone-api-key",
        environment="us-west1"
    )
    

AutoGen provides a comprehensive tool suite for agent orchestration patterns and memory management. It facilitates the implementation of multi-turn conversation handling, critical for complex event-driven systems.

    import { Agent } from 'autogen';
    const agent = new Agent({
        memory: {
            type: 'BufferMemory',
            options: { maxSize: 100 }
        }
    });
    

Pros and Cons

LangChain offers advanced integration capabilities and a strong community, but it may require a steeper learning curve for newcomers. AutoGen simplifies agent orchestration with intuitive patterns yet may lack comprehensive documentation.

CrewAI emphasizes agent encapsulation, making it ideal for systems with complex business logic, but it demands rigorous setup and configuration. LangGraph provides excellent event handling routines but can become challenging to scale with very large datasets.

Criteria for Vendor Selection

When selecting a vendor, consider factors like integration capabilities with existing systems, community support, scalability, and alignment with your business objectives. Ensure the chosen framework supports robust event design and performance optimization.

    const memory = new MemoryManager({
        memoryKey: 'chat_history',
        returnMessages: true
    });
    

Assess the ease of implementing MCP protocol and tool calling patterns, which are crucial for maintaining efficiency and accuracy in multi-agent environments.

Implementation Examples

Below is an example of MCP protocol implementation using CrewAI:

    from crewai.mcp import MCPClient

    client = MCPClient(endpoint="mcp://broker:5672")
    client.publish_event('OrderCreated', {
        'order_id': 1234,
        'customer_id': 5678
    })
    

This code snippet highlights the process of publishing an event using CrewAI's MCP client, demonstrating its straightforward API for event-driven operations.

Ultimately, selecting the right event sourcing agent framework involves balancing technical requirements with organizational goals, ensuring a scalable and robust system architecture.

Appendices

For further reading on event sourcing agents, consider exploring the following resources:

• Event Sourcing by Martin Fowler
• Microservices Patterns: Event Sourcing
• LangChain Documentation

Technical References

Below are some technical references and code snippets that demonstrate the implementation of event sourcing agents using modern frameworks and databases:

Code Snippet: Memory Management in Python

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

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

Architecture Diagrams

Consider an architecture where agents are decoupled and communicate through events. Each agent subscribes to specific event types and processes them according to their business logic.

• Integration with Vector Databases: Use Pinecone or Weaviate for efficient storage and retrieval.
• MCP Protocol: Implement communication protocols for agent-to-agent interactions.

Implementation Examples

// Example using AutoGen Framework
import { Agent, MCP } from 'autogen';

const agent = new Agent({
    memory: new MCP({ db: 'weaviate' }),
    events: ['order.created', 'order.updated']
});

agent.on('order.created', (event) => {
    console.log(`Processing event: ${event.name}`);
});
    

Glossary of Terms

• Event Sourcing: A pattern where state changes are stored as a sequence of events.
• MCP: Multi-Channel Protocol for managing communications between multiple agents.
• Tool Calling: A method where agents invoke external tools or services as part of their logic.

This section provides a comprehensive look into the tools and methodologies for implementing robust event sourcing agents, suitable for enterprise systems in 2025.

Frequently Asked Questions

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

memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)
executor = AgentExecutor(memory=memory)
executor.execute("Process event")
        

from langchain.vectorstores import Pinecone

pinecone = Pinecone.from_existing_index("your-index-name")
vector = pinecone.retrieve("query-vector")
        

from crewai.protocols import MCPHandler

class CustomMCP(MCPHandler):
    def handle(self, message):
        # Process the message
        pass
        

from langchain.memory import LimitedMemory

memory = LimitedMemory(size=100, memory_key="event_history")
        

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

from autogen.orchestration import Orchestrator

orchestrator = Orchestrator(agents=[agent1, agent2])
orchestrator.coordinate()