Business Context and Regulatory Landscape

The evolution of data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), has significantly impacted how businesses manage user consent. These regulations mandate explicit user consent for data collection and processing, shaping the development of consent management agents. Enterprises are driven to adopt these systems to ensure compliance, mitigate legal risks, and enhance customer trust.

Impact of GDPR and CCPA: GDPR, enacted by the EU, requires businesses to obtain clear and affirmative consent from users before processing their personal data. Similarly, CCPA empowers California residents with rights over their data, including the right to know, delete, and opt-out of the sale of personal information. These laws have heightened consumer expectations around privacy, demanding robust consent mechanisms.

Business Drivers: Beyond compliance, businesses are motivated by the need to build trust and transparency with their customers. Consent management agents offer enterprises a way to provide users with straightforward and transparent control over their data, aligning with modern customer expectations for privacy and security. Additionally, these systems enable fine-grained control, allowing users to customize their consent preferences dynamically.

Customer Expectations: In today's digital landscape, customers expect privacy by design. They demand transparency about how their data is used and shared. Consent management agents must offer clear, jargon-free interfaces that communicate data usage, retention, and third-party sharing policies effectively.

Technical Implementations: Developers are turning to advanced frameworks and architectures to implement consent management agents in compliance with these regulations.

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

# Initialize Pinecone vector database
pinecone.init(api_key='your-api-key')

# Create a memory buffer for conversation history
memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

# Example of agent orchestration with LangChain
agent_executor = AgentExecutor(
    memory=memory,
    agents=[
        # Define agents with specific tasks
    ]
)

# Implementing a multi-turn conversation
def handle_conversation(input_text):
    response = agent_executor.run(input_text)
    return response

# Call the function with user input
user_input = "What information do you store about me?"
print(handle_conversation(user_input))
    

Architecture and Integration: The architecture of consent management systems often involves integration with vector databases like Pinecone for efficient data retrieval and storage. Developers can leverage frameworks such as LangChain to manage conversation history and execute multi-turn dialogues, ensuring that consent operations are dynamic and responsive to user interactions.

MCP Protocol and Tool Calling: Implementing the MCP protocol ensures that data flows are secure and compliant. Tool calling patterns can be defined to restrict agent access to only necessary data, aligning with best practices for explicit and granular consent.

By embedding these technical strategies into their systems, businesses can meet regulatory requirements while delivering a seamless and user-centric consent management experience, enhanced by AI-driven automation and robust technical architecture.

Technical Architecture of Consent Management Agents

The architecture of consent management agents in enterprise environments is pivotal for ensuring regulatory compliance and providing a seamless user experience. A robust system should integrate effectively with existing IT infrastructure while addressing security, scalability, and AI-driven automation. This section delves into the core components, integration strategies, and best practices for implementing such systems.

Components of a Robust Consent Management System

A comprehensive consent management system comprises several critical components:

• Consent Database: A secure, scalable database to store user consent records. Integration with vector databases like Pinecone enhances data retrieval and management.
• AI Agents: Utilize frameworks such as LangChain or AutoGen to automate consent operations dynamically.
• API Layer: Exposes endpoints for third-party integrations, allowing seamless data exchange.
• User Interface: A user-centric interface for obtaining and managing user consents.

Integration with Existing IT Infrastructure

Integration with existing IT infrastructure requires careful planning to ensure system compatibility and efficiency. Below is a simplified architecture diagram (described):

Architecture Diagram: The diagram illustrates a multi-layered system comprising a user interface layer, an API gateway, a business logic layer with AI agent orchestration, and a data storage layer with vector database integration.

Here is a basic integration example using Python and LangChain:

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

    # Initialize Pinecone vector database
    pinecone.init(api_key="YOUR_API_KEY")

    # Define memory for conversation handling
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )

    # Create an AI agent executor
    agent = AgentExecutor(memory=memory)

    # Example function to store consent in Pinecone
    def store_consent(user_id, consent_data):
        index = pinecone.Index("consent-management")
        index.upsert([(user_id, consent_data)])
    

Security and Scalability Considerations

Security and scalability are paramount in consent management systems. Implementing the MCP (Multi-Channel Protocol) ensures secure data communication across various channels. Below is an example MCP protocol implementation snippet:

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

    // Define MCP security configurations
    const mcpConfig = {
        encryptionKey: 'YOUR_ENCRYPTION_KEY',
        allowedOrigins: ['https://trusted-origin.com']
    };

    // Initialize MCP protocol
    const mcp = new MCP(mcpConfig);

    // Securely send consent data
    mcp.send('consent.update', {
        userId: 'user123',
        consentStatus: 'granted'
    });
    

Scalability is addressed through the use of cloud-native solutions and microservices architecture, allowing the system to handle increasing loads efficiently.

Conclusion

Implementing consent management agents requires a nuanced understanding of both technical architecture and regulatory demands. By leveraging AI frameworks like LangChain, vector databases such as Pinecone, and secure communication protocols like MCP, developers can build systems that are not only compliant but also responsive to evolving user and regulatory needs.

Implementation Roadmap for Consent Management Agents

Implementing a consent management system in an enterprise environment involves a structured approach that ensures compliance with regulatory standards like GDPR and CCPA, while also optimizing for user experience and operational efficiency. This roadmap outlines the step-by-step process, timeline, and resource allocation necessary for successful deployment of consent management agents, leveraging modern AI frameworks and technologies.

Step-by-Step Implementation Process

1. Requirement Analysis and Planning:

   Begin with a comprehensive analysis of regulatory requirements and organizational needs. Document the specific consent types and data categories relevant to your operations.

2. Architecture Design:

   Design a robust architecture using AI-driven frameworks. Here's a simplified architecture diagram description:

   • Consent Management Layer: Handles user interactions and consent storage.
   • AI Processing Layer: Utilizes frameworks like LangChain and AutoGen for dynamic consent operations.
   • Data Storage Layer: Integrates with vector databases like Pinecone for scalable consent data management.
3. Development and Integration:

   Implement the consent management agents. Below is a Python code example using LangChain for memory management and agent orchestration:

   
   from langchain.memory import ConversationBufferMemory
   from langchain.agents import AgentExecutor
   
   memory = ConversationBufferMemory(
       memory_key="chat_history",
       return_messages=True
   )
   
   executor = AgentExecutor(memory=memory)
   
   # Example of AI agent tool calling pattern
   agent_response = executor.execute("What consents do I have?")
               

4. Testing and Validation:

   Conduct thorough testing to validate compliance and system performance. Implement multi-turn conversation handling to ensure seamless user interactions:

   
   from langchain.conversation import Conversation
   
   conversation = Conversation(agent_executor=executor)
   response = conversation.turn("Can I update my consent preferences?")
               

5. Deployment and Monitoring:

   Deploy the system in phases, starting with a pilot. Monitor performance and compliance continuously, adapting to changes in data privacy regulations.

Timeline and Milestones

The implementation timeline can span 6 to 12 months, depending on organizational size and complexity. Key milestones include:

• Month 1-2: Requirement gathering and architecture design.
• Month 3-5: Development and initial integration with existing systems.
• Month 6-7: Testing, validation, and adjustments based on feedback.
• Month 8: Pilot deployment and monitoring.
• Month 9-12: Full-scale deployment and continuous improvement.

Resource Allocation and Budgeting

Resource allocation should focus on skilled personnel, technology infrastructure, and ongoing compliance management:

• Personnel: Engage a cross-functional team including developers, data privacy officers, and user experience designers.
• Technology: Invest in AI frameworks (LangChain, AutoGen) and vector databases (Pinecone, Weaviate) for efficient data handling.
• Budget: Allocate budget for initial setup, ongoing maintenance, and potential regulatory updates.

By following this roadmap, enterprises can effectively implement consent management agents that not only meet regulatory requirements but also enhance user trust through transparent and efficient consent processes.

ROI Analysis and Business Benefits of Consent Management Agents

The implementation of consent management agents in enterprise environments presents a significant return on investment (ROI) due to their ability to streamline compliance processes, enhance user experiences, and provide a robust framework for managing consent dynamically. This section delves into the detailed cost-benefit analysis, highlighting both tangible and intangible benefits, and discusses the long-term economic impacts of deploying these systems.

Cost-Benefit Analysis

Implementing consent management agents involves initial costs related to software development, integration, and training. However, these are quickly offset by the reduction in compliance risks and potential fines associated with violations of data privacy regulations such as GDPR and CCPA. By automating consent management, enterprises can significantly reduce the overhead associated with manual data handling processes.

Tangible Benefits include increased operational efficiency, reduced legal risks, and enhanced data governance. The ability to dynamically manage consent through AI-driven automation minimizes the need for human intervention, reducing labor costs and increasing processing speed.

Intangible Benefits encompass improved customer trust and brand reputation. Users are more likely to engage with businesses that demonstrate transparency and respect for their data privacy, leading to increased customer loyalty and retention.

Long-Term Economic Impact

In the long term, consent management agents contribute to a sustainable business model by ensuring ongoing compliance with evolving data privacy laws. This adaptability reduces the need for costly overhauls as regulations change. Furthermore, the enhanced user-centric experience fosters a competitive advantage in the marketplace.

Technical Implementation

Implementing consent management agents effectively requires a robust technical architecture. Below are some practical examples and code snippets using popular frameworks and tools.

Code Snippet: AI Agent with 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)
  

Architecture Diagram

Imagine an architecture where the consent management agent interfaces with various components via a secure API gateway. It integrates with a vector database like Pinecone for storing consent metadata and utilizes the LangChain framework for AI-driven consent decisions.

Framework and Database Integration

Using LangChain for AI functionality and Pinecone for vector database integration, enterprises can efficiently manage consent data. Below is a sample integration setup:

  from langchain.vectorstores import Pinecone

  vector_store = Pinecone(api_key="YOUR_API_KEY", index_name="consent_data")
  consent_vector = vector_store.fetch_vector("user_consent")
  

MCP Protocol and Tool Calling

Implementing the MCP protocol can streamline communication between services. Consider this pattern for tool calling:

  class MCPProtocol:
      def call_tool(self, tool_name, params):
          # Tool calling logic
          pass
  

Agent Orchestration and Multi-Turn Conversation

A multi-turn conversation can be handled using orchestrated agents that work in tandem. Here's an orchestration pattern:

  from langchain.agents import AgentOrchestrator

  orchestrator = AgentOrchestrator(agents=[agent_executor])
  orchestrator.run_conversation(input_text="Manage my consent preferences")
  

In conclusion, deploying consent management agents not only enhances compliance and operational efficiency but also builds stronger customer relationships, offering a substantial ROI for enterprises willing to invest in this advanced technology.

Case Studies

The implementation of consent management agents in enterprise environments is a burgeoning practice that addresses the complexities of modern data privacy regulations such as GDPR and CCPA. This section discusses examples of successful deployments, lessons learned, and best practices, with a focus on AI-driven solutions.

Successful Implementations

Several enterprises have effectively implemented consent management agents using advanced frameworks and technologies. For instance, a leading financial institution leveraged LangChain and Pinecone to manage user consent dynamically. By integrating AI-driven automation, they streamlined the consent process, ensuring compliance and enhancing user experience.

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

    # Initialize Pinecone client
    pinecone_client = Client(api_key="your-api-key")

    # Memory management for conversation history
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    # Agent execution with memory
    agent_executor = AgentExecutor(memory=memory)
    

The architecture of this solution can be visualized as follows: The AI agent interacts with a user-facing application, processing consent requests. The agent's decisions are informed by a vector database (Pinecone) that stores consent history, allowing for quick retrieval and updates.

Lessons Learned and Best Practices

From these implementations, several lessons and best practices have emerged:

• Explicit & Granular Consent Collection: Implement fine-grained permission models, ensuring agents can only access necessary data for specific actions, enhancing security and compliance.
• Transparency and Clarity: Use clear, jargon-free language in consent communications. This improves user trust and reduces confusion.
• Dynamic Consent Management: Utilize AI to automatically adjust consent parameters based on user interactions, ensuring time-limited and context-aware permissions.

Industry-Specific Insights

Different industries have unique requirements for consent management:

• Healthcare: A hospital network used LangChain and Weaviate to manage patient consent forms, ensuring data privacy and regulatory compliance with HIPAA.
• E-commerce: An online retailer implemented AI-driven consent agents using CrewAI and Chroma, allowing them to personalize user experiences while maintaining CCPA compliance.

    import { Memory } from 'langchain';
    import { Agent } from 'crewai';
    import { VectorStore } from 'chroma';

    // Initialize a vector store
    const vectorStore = new VectorStore('e-commerce-consent');

    // AI agent with memory and tool calling
    const consentAgent = new Agent({
        memory: new Memory(),
        vectorStore,
        toolSchemas: {
            consentAction: {
                type: 'action',
                required: ['userId', 'consentType'],
            }
        },
        onAction: (action) => {
            // Handle consent actions
        }
    });
    

These implementations underscore the importance of a robust technical architecture that incorporates AI, vector databases, and memory management to handle multi-turn conversations effectively. By leveraging these technologies, enterprises can provide a user-centric consent management experience while ensuring strict compliance with data privacy regulations.

Risk Mitigation and Challenges

The deployment of consent management agents in enterprise environments entails various challenges and risks that developers need to diligently address. In this section, we explore some common pitfalls, strategies for effective risk management, and outline contingency planning approaches essential for maintaining robust consent management systems.

Common Challenges and Pitfalls

One of the primary challenges in deploying consent management agents is ensuring compliance with global data privacy regulations such as GDPR and CCPA. This requires a deep understanding of regulatory requirements and the ability to implement these within the technology stack effectively. Another challenge lies in managing user expectations for transparency and control over their personal data. Developers must avoid overly complex consent mechanisms which can overwhelm users, leading to incorrect consent collection.

Technical challenges include integrating AI technologies like machine learning to handle dynamic consent operations, and managing the complexity of multi-turn conversations in user interactions. Inadequate handling of these can result in poor user experience and potential legal issues.

Strategies for Risk Management

Employing frameworks such as LangChain or LangGraph can facilitate the implementation of AI-driven consent management agents by providing pre-built components for handling complex conversation flows and memory management. For instance, using a conversation buffer to track chat history ensures all consent-related interactions are recorded, which is crucial for compliance and auditing:

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

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

Integrating vector databases like Pinecone or Weaviate can also help in storing and retrieving consent-related data efficiently, ensuring that consent information is both accessible and secure. Furthermore, leveraging tool calling patterns and schemas can optimize the execution of specific tasks related to consent requests and recordings.

Contingency Planning

A robust contingency plan is critical for addressing unforeseen challenges or failures in a consent management system. Implementing MCP (Memory, Conversation, Protocol) protocols can ensure system resilience and continuity in user interactions. Here is a snippet demonstrating basic MCP protocol integration:

    const { MCP } = require('some-mcp-library');

    const mcpInstance = new MCP({
        protocol: "https",
        memoryCapacity: "512MB",
    });

    mcpInstance.on('error', (err) => {
        console.error("MCP error:", err);
        // Implement recovery logic
    });
    

Additionally, planning for system orchestration patterns is vital. This includes ensuring that agent tasks are distributed and executed efficiently, minimizing downtime, and optimizing resource utilization. By employing these strategies, organizations can enhance the reliability and effectiveness of their consent management systems, thus fostering trust and compliance.

In conclusion, while the implementation of consent management agents is fraught with potential challenges, adopting a structured and comprehensive approach to risk mitigation and contingency planning can significantly alleviate these challenges, ensuring a secure and compliant environment.

Metrics and KPIs for Consent Management Agents

Implementing effective consent management agents requires careful monitoring and optimization to ensure compliance, user satisfaction, and system efficiency. Key performance indicators (KPIs) and metrics are critical for assessing the success of these systems and guiding continuous improvement. This section outlines essential KPIs, monitoring mechanisms, and improvement strategies, complemented by technical implementations using modern AI frameworks.

Key Performance Indicators for Success

• Consent Collection Rate: The percentage of users who grant consent when prompted. This measures the effectiveness of consent requests and user interface clarity.
• Compliance Rate: The proportion of operations in line with data protection regulations like GDPR and CCPA.
• User Satisfaction Score: Gauged through user feedback and surveys on the consent process experience.
• Revocation Processing Time: The time taken to process user consent withdrawal requests.

Monitoring and Reporting Mechanisms

Effective monitoring involves setting up real-time dashboards and alerts for critical consent metrics. Using vector databases like Pinecone or Weaviate can enhance data retrieval and analysis.

from langchain import PromptTemplate, LLMChain
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import Weaviate
from langchain.agents import AgentExecutor

# Setup vector store for storing consent interactions
vector_store = Weaviate(index_name="consent_interactions")

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

# Example of an agent handling consent processes
consent_agent = AgentExecutor(
    memory=memory,
    prompt_template=PromptTemplate.from_string("Handle consent: {input}")
)

# Logging consent interactions
def log_interaction(consent_data):
    vector_store.add(consent_data)

log_interaction({
    "user_id": "12345",
    "consent_action": "granted",
    "timestamp": "2025-10-15T08:30:00Z"
})
    

Continuous Improvement Strategies

Continuous improvement can be achieved through the integration of AI-driven insights and user feedback. Leveraging frameworks like LangChain and implementing MCP (Memory, Control, and Processing) protocols can enhance agent performance.

import { AutoGen } from 'autogen';
import { PineconeClient } from '@pinecone-database/pinecone-client';
import { AgentOrchestrator } from 'crew-ai';

const pinecone = new PineconeClient({ apiKey: "your-api-key" });
pinecone.initialize();

const orchestrator = new AgentOrchestrator();

orchestrator.registerAgent({
    id: 'consentAgent',
    process: async (input) => {
        // AI-driven consent decision-making
        return await AutoGen.consentEvaluator(input);
    }
});
    

By implementing these strategies, developers can ensure that consent management agents remain efficient, compliant, and user-focused. Regularly updating KPIs based on regulatory changes and technological advancements is crucial for maintaining optimal performance.

Vendor Comparison and Selection

Selecting the right vendor for consent management agents is critical in modern enterprises, especially considering the evolving landscape of data privacy regulations and technological advancements. This section outlines the criteria for evaluating vendors, provides a comparison of leading solutions, and presents a decision-making framework to guide your selection process.

Criteria for Evaluating Vendors

• Regulatory Compliance: Ensure the solution complies with GDPR, CCPA, and other regional regulations.
• User Experience: Look for solutions offering intuitive interfaces and clear consent mechanisms.
• Technical Architecture: Evaluate integration capabilities with existing systems, scalability, and support for AI-driven automation.
• Data Security: Assess the vendor's data protection measures, encryption standards, and privacy policies.

Comparison of Leading Solutions

Several vendors offer advanced consent management solutions, including OneTrust, TrustArc, and Securiti.ai. Each solution varies in its approach to AI integration, data processing, and user interface design:

• OneTrust: Known for robust compliance frameworks and user-centric design.
• TrustArc: Offers sophisticated machine learning models for dynamic consent operations.
• Securiti.ai: Excels in AI-driven automation and granular data privacy controls.

Decision-Making Framework

To select the best vendor, enterprises should apply a structured decision-making framework that includes:

• Define specific needs and compliance requirements.
• Conduct a technical evaluation focusing on AI capabilities and integration flexibility.
• Request vendor demos and proof of concept implementations.
• Assess total cost of ownership and value proposition.

Implementation Examples

Below are some code snippets and architecture descriptions for integrating AI-driven consent management agents using popular frameworks and protocols:

Code Example: AI Agent Integration with LangChain

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

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    database = PineconeDatabase(api_key="your_api_key", environment="us-west1")

    agent = AgentExecutor(
        memory=memory,
        database=database,
        tools=[...]
    )
    

Architecture Diagram Description

The architecture involves integrating LangChain with a vector database like Pinecone for efficient data storage and retrieval. The agent orchestrates conversations with users while maintaining a history in buffer memory, ensuring compliance with consent requirements.

MCP Protocol Implementation

    import { MCPAgent } from 'crewAI';
    const agent = new MCPAgent({
        protocol: 'MCP',
        permissions: ['read', 'write'],
        onConsentRequest: (request) => {
            // Handle consent request logic
        }
    });
    

Tool Calling Pattern

    import { Agent, Tool } from 'crewAI';

    const toolCallPattern: Tool = {
        name: 'consentVerifier',
        schema: {
            type: 'object',
            properties: {
                consentId: { type: 'string' },
                userId: { type: 'string' }
            }
        }
    };

    const agent = new Agent({
        tools: [toolCallPattern]
    });
    

Memory Management in Multi-turn Conversations

    from langchain.memory import SequenceMemory

    memory = SequenceMemory(
        memory_key="session_memory",
        return_messages=True
    )

    def handle_conversation(input_message):
        response = agent.execute(input_message, memory=memory)
        return response
    

By leveraging these advanced tools and frameworks, developers can ensure that their consent management solutions are both compliant and user-friendly, providing a seamless experience while meeting regulatory obligations.

Appendices

• Consent Management Agent (CMA): An automated system that manages user consents for data collection and processing.
• MCP (Management Consent Protocol): A protocol framework used to enforce consent requirements across platforms.
• Vector Database: A database optimized for operations on vector spaces, often used in AI-driven applications for fast data retrieval.

Additional Resources

• LangChain Documentation: langchain.com/docs
• Pinecone Vector Database: pinecone.io
• GDPR Compliance Guidelines: gdpr.eu

Reference Materials

• Smith, J. (2025). The Future of Consent Management: AI and Compliance. Tech Innovations Journal.
• Johnson, L. (2025). User-Centric AI: Balancing Privacy and Functionality. Data Protection Quarterly.

Implementation Examples

This section provides code snippets and architecture diagrams to facilitate the implementation of CMAs in enterprise environments.

Python Code Example with LangChain

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

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

    agent = AgentExecutor(memory=memory)
    

Vector Database Integration (Pinecone)

    import pinecone

    pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")

    index = pinecone.Index("consent-management")
    index.upsert(vectors=[("id", [0.1, 0.2, 0.3])])
    

MCP Protocol Implementation

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

    const consentRequest = MCP.createConsentRequest({
        action: 'data_access',
        purpose: 'analytics',
        duration: '30 days'
    });

    consentRequest.sendToUser(userId);
    

Tool Calling Patterns

    import { Tool } from 'crewAI';

    const tool = new Tool({
        name: 'dataProcessor',
        schema: {
            type: 'object',
            properties: {
                data: { type: 'array' }
            }
        }
    });

    tool.call({ data: userConsentData });
    

Multi-Turn Conversation Handling

    from langchain.memory import ConversationBufferMemory

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

    def handle_conversation(input_text):
        response = agent.get_response(input_text, memory=memory)
        return response
    

Agent Orchestration Patterns

The architecture diagram illustrates a multi-agent orchestration where a consent management agent collaborates with a data usage verification agent and a compliance monitoring agent in a microservices framework.

(Diagram: A flowchart showing agents communicating via APIs overlaid on a serverless platform)

Frequently Asked Questions

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

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

agent = AgentExecutor(memory=memory)
    

from weaviate import Client

client = Client("http://localhost:8080")

# Assuming 'Consent' schema exists
data_object = {
    "user_id": "12345",
    "consent_given": True,
    "timestamp": "2025-01-01T00:00:00Z"
}

client.data_object.create(data_object, "Consent")
    

from langchain.memory import ConversationBufferMemory

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

# Use memory in conversation management