Technical Architecture of Rollback Strategies Agents

In modern software deployment practices, rollback strategies play a crucial role in maintaining system stability and ensuring minimal disruption during updates. This section delves into the technical architectures that underpin effective rollback strategies, focusing on blue-green deployments, canary releases, and rolling deployments with hot-swap methods. Each of these strategies is designed to minimize downtime and maximize reliability, with specific implementation techniques illustrated through code snippets and architecture diagrams.

Blue-Green Deployments

Blue-green deployments are a robust strategy for managing software updates. This approach maintains two separate production environments: the "blue" environment, which is live, and the "green" environment, which hosts the new version. Traffic is directed between these environments to facilitate immediate rollbacks if necessary. The primary benefit is the ability to switch traffic back to the stable environment without delay, ensuring continuity and reliability.

    from langchain.deployments import BlueGreenDeployment

    blue_env = BlueGreenDeployment(environment='blue')
    green_env = BlueGreenDeployment(environment='green')

    def switch_traffic():
        if green_env.is_stable():
            green_env.become_primary()
        else:
            blue_env.become_primary()
    

The above code snippet demonstrates a simple mechanism for switching traffic between environments using a hypothetical LangChain deployment module.

Canary Releases

Canary releases involve gradually rolling out updates to a small subset of users. This approach allows for the detection of issues on a limited scale before full deployment, enabling rapid rollback with minimal disruption. The incremental nature of canary releases is ideal for identifying and mitigating risks early in the deployment process.

    import { CanaryRelease } from 'langgraph-deployments';

    const canary = new CanaryRelease({
        percentage: 10, // Release to 10% of users
        monitor: true
    });

    canary.deploy();
    if (canary.detectIssues()) {
        canary.rollback();
    }
    

This TypeScript example uses a LangGraph deployment framework to manage a canary release, monitoring for issues to trigger rollbacks if necessary.

Rolling Deployments with Hot-Swap

Rolling deployments with hot-swap methods involve incrementally deploying new versions across clusters or regions. This strategy enables selective rollbacks, allowing for minimized downtime and ensuring that only a portion of the system is affected at any time.

    const { RollingDeployment } = require('autogen-deployments');

    const deployment = new RollingDeployment({
        clusters: ['us-east-1', 'us-west-2'],
        hotSwapEnabled: true
    });

    deployment.deployVersion('v2.0.0');
    deployment.on('error', (cluster) => {
        deployment.rollback(cluster);
    });
    

In this JavaScript snippet, the AutoGen framework is utilized to manage rolling deployments with hot-swap capabilities, allowing for rollback in specific clusters where issues are detected.

Integration with AI and Automation Tools

To enhance rollback strategies, AI agents can be integrated to assist in decision-making and automation. This involves using frameworks like LangChain and CrewAI for agent orchestration and memory management. Vector databases such as Pinecone or Weaviate can be leveraged for storing deployment metadata and rollback history.

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

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

    agent = AgentExecutor(memory=memory)
    db = VectorDatabase()

    def manage_rollback():
        if agent.detectIssue():
            db.store('rollback_event', agent.currentState())
            agent.rollback()
    

This Python example shows how to use LangChain for agent orchestration, integrating with Pinecone for storing rollback events and ensuring effective memory management.

These technical architectures, supported by AI-assisted tools and automation frameworks, provide a comprehensive approach to managing rollback strategies, ensuring seamless and reliable software updates in enterprise environments.

Implementation Roadmap for Rollback Strategies Agents

Implementing rollback strategies in enterprise environments involves a systematic integration of automated processes, advanced deployment architectures, and AI-assisted decision-making. This roadmap outlines the steps necessary to integrate rollback strategies effectively into existing systems, highlighting key milestones, deliverables, and the tools and technologies required for successful implementation.

Step 1: Assess Current System Architecture

Begin by evaluating the current deployment architecture to determine the suitability for integrating rollback strategies. Identify areas where progressive deployment strategies, such as blue-green deployments or canary releases, can be applied.

Step 2: Select Appropriate Deployment Strategy

Choose between blue-green, canary, or rolling deployments based on your system's complexity and business requirements. For example, a blue-green deployment might be suitable for systems requiring zero downtime, while canary releases allow for gradual exposure to new changes.

Step 3: Integrate AI-Assisted Decision-Making

Use AI agents to enhance decision-making during rollbacks. Implement tools like LangChain or AutoGen to monitor deployment health and trigger automated rollbacks if anomalies are detected.

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

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

def monitor_deployment():
    # Logic to monitor deployment and trigger rollback
    if detect_anomaly():
        agent.execute_rollback()
    

Step 4: Implement Vector Database Integration

Integrate vector databases like Pinecone or Chroma to store and manage deployment data, enabling sophisticated rollback analytics and decision-making processes.

import pinecone

pinecone.init(api_key="your-api-key")
index = pinecone.Index("deployment-data")

def log_deployment_data(data):
    index.upsert([(data['id'], data['vector'])])
    

Step 5: Automate Rollback Processes

Develop automated scripts to facilitate rapid rollback. Use tools and frameworks like LangGraph to orchestrate complex rollback workflows and ensure seamless integration with existing CI/CD pipelines.

Step 6: Implement Multi-Turn Conversation Handling

Enable agents to handle multi-turn conversations, ensuring they can manage complex user interactions and provide detailed rollback information.

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

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

def handle_user_query(query):
    response = agent.process_input(query)
    return response
    

Key Milestones and Deliverables

• Milestone 1: Assessment and Strategy Selection - Deliverable: Architecture Assessment Report
• Milestone 2: AI Integration - Deliverable: AI Monitoring and Rollback Scripts
• Milestone 3: Database Integration - Deliverable: Configured Vector Database
• Milestone 4: Automation and Orchestration - Deliverable: Automated Rollback Pipeline

Tools and Technologies

• Frameworks: LangChain, AutoGen, CrewAI, LangGraph
• Databases: Pinecone, Weaviate, Chroma
• Protocols: MCP for agent communication

By following this roadmap, developers can integrate sophisticated rollback strategies into their systems, leveraging cutting-edge tools and technologies to ensure robust and efficient deployment processes.

Change Management in Rollback Strategies for AI Agents

The implementation of rollback strategies is a critical component for ensuring the successful deployment and management of AI agents within enterprise environments. The need for comprehensive change management practices becomes evident when considering the complex interplay of stakeholder coordination, detailed documentation, and effective training during rollback scenarios.

Importance of Stakeholder Coordination

Successful rollback strategies necessitate the engagement of all relevant stakeholders. This involves clear communication channels and predefined protocols to ensure that each stakeholder is aware of their roles and responsibilities. In the context of AI agents, this means ensuring that developers, operations teams, and business leaders are aligned on the rollback plan.

Managing Organizational Change During Rollbacks

Rollbacks can impact various facets of an organization, from operational workflows to customer experiences. As such, it is crucial to manage organizational change effectively, minimizing disruption and maintaining operational continuity. The introduction of progressive deployment strategies, such as blue-green and canary deployments, benefits from automated rollbacks to swiftly revert changes with minimal impact.

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

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    agent_executor = AgentExecutor(memory=memory)
    vector_store = Weaviate(collection_name="rollback_data")
    

Training and Documentation Best Practices

Creating robust documentation and providing comprehensive training are pivotal in facilitating smooth rollbacks. Documentation should include detailed rollback procedures, potential risks, and troubleshooting guides. Training sessions should empower teams to execute rollbacks confidently, with simulated scenarios to build readiness.

Here is a code example demonstrating AI agent rollback using LangChain and Weaviate for vector database integration:

    from langchain import LangChain
    from langchain.agents import AgentOrchestrator
    from langchain.protocols import MCP

    orchestrator = AgentOrchestrator()
    mcp_protocol = MCP()

    def rollback_process():
        orchestrator.execute_rollback()
        mcp_protocol.revert_state()

    rollback_process()
    

By adopting these best practices, organizations can harness AI-assisted decision-making for more informed rollback strategies, facilitating smoother transitions and minimizing disruptions.

ROI Analysis of Rollback Strategies

Implementing rollback strategies in modern software environments is not merely a safety measure but a strategic investment that can yield substantial returns. This section delves into the cost-benefit analysis of these strategies, focusing on long-term financial impacts and real-world case examples.

Cost-Benefit Analysis

The cost of implementing rollback strategies, such as blue-green deployments or canary releases, typically involves infrastructure duplication and sophisticated automation tools. However, the benefits of these strategies, particularly in reducing downtime and ensuring service reliability, often outweigh initial investments. Automated rollback processes reduce manual intervention, thus saving time and minimizing human errors.

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

    # Initialize conversation memory to store chat history
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    agent_executor = AgentExecutor(memory=memory)
    

Long-Term Financial Impact

Downtime reduction directly correlates with increased customer satisfaction and retention, leading to enhanced revenue streams. For example, a company that deployed a rollback strategy using a blue-green deployment model noted a 30% reduction in downtime-related costs over two years. The initial setup, which included implementing automated scripts, proved cost-effective in the long run.

Real-World Scenario: Vector Database Integration

Integrating rollback strategies with vector databases like Pinecone can optimize data retrieval during rollbacks. Consider a scenario where an AI agent leverages Pinecone to manage state information during rollbacks efficiently.

    import pinecone

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

    # Example rollback data insertion
    index = pinecone.Index("rollback-index")
    index.insert([(1, [0.1, 0.2, 0.3])], namespace="rollback_data")
    

Case Examples of ROI

A tech firm employed canary releases with AI-assisted decision-making, leveraging LangChain for tool calling. This approach allowed the firm to rollback in less than a minute, preserving their market reputation during peak service hours.

    const { AgentExecutor, Tool } = require('langchain');

    // Define a tool for rollback decision
    const rollbackTool = new Tool({
        name: 'RollbackTool',
        action: (context) => {
            return context.isFailure ? 'Initiate rollback' : 'Continue deployment';
        }
    });

    const agentExecutor = new AgentExecutor({ tools: [rollbackTool] });
    

Conclusion

The strategic implementation of rollback strategies not only mitigates risks but also secures financial stability through reduced downtime and enhanced service reliability. As showcased in the examples, the integration of AI and advanced tooling frameworks like LangChain can significantly amplify these benefits.

Case Studies

In exploring rollback strategies for agents, various enterprises have successfully deployed these approaches, learning invaluable lessons along the way. Below, we delve into real-world examples, examine the key takeaways from these deployments, and provide a comparative analysis of different strategies.

Real-World Examples of Successful Rollbacks

In 2025, Company X implemented a progressive deployment strategy using blue-green deployments. Their setup involved two identical production environments: 'blue' served the current stable version, while 'green' was used for testing new updates. By directing a portion of user traffic to the green environment, they were able to monitor performance and quickly switch back to blue if any issues arose. This approach minimized downtime and ensured seamless user experience.

  # Example of deploying an AI agent with rollback in a blue-green setup
  from langchain.agents import AgentExecutor
  from langchain.memory import ConversationBufferMemory

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

  agent = AgentExecutor(memory=memory)

  def deploy_blue_green(agent_executor):
      # Switch traffic from blue to green
      if test_green(agent_executor):
          switch_traffic('green')
      else:
          rollback_to('blue')
  

Lessons Learned from Enterprise Deployments

Enterprises have reported several critical lessons from their deployment of rollback strategies:

• Thorough Testing: Extensive testing in the green environment is crucial to identify potential issues before full deployment.
• Automated Monitoring: Continuous monitoring allows for real-time alerts and quick decision-making.
• Stakeholder Coordination: Effective communication between development and operations teams ensures all parties are informed and prepared for potential rollbacks.

Comparative Analysis of Different Strategies

Various rollback strategies offer distinct advantages and challenges. Below is a comparison of three prevalent methods:

• Blue-Green Deployments: Offers immediate rollback capability with minimal disruption but requires maintaining two environments.
• Canary Releases: Allows for gradual changes and monitoring but can be complex to configure and require sophisticated monitoring tools.
• Rolling Deployments with Hot-Swap: Provides flexibility and minimizes downtime but necessitates precise orchestration and potentially complex configurations.

  // Implementing a canary release strategy with an AI agent using LangChain
  const { AgentExecutor, ConversationBufferMemory } = require('langchain');

  const memory = new ConversationBufferMemory({
      memory_key: 'chat_history',
      return_messages: true
  });

  const agent = new AgentExecutor(memory);

  function canaryRelease(agentExecutor) {
      // Deploy to a subset of users
      deployToSubset(agentExecutor);
      // Monitor performance
      if (!isStable(agentExecutor)) {
          rollback(agentExecutor);
      }
  }
  

Integration with Vector Databases and AI Frameworks

Modern rollback strategies often integrate AI frameworks, such as LangChain and AutoGen, and vector databases to enhance memory and decision-making capabilities. For instance, integrating Pinecone with LangChain can enhance model performance and rollback readiness.

  # Vector database integration example with Pinecone
  from langchain.vector_databases import Pinecone
  from langchain.agents import AgentExecutor

  vector_db = Pinecone(index_name='agent_index')
  agent = AgentExecutor(vector_db=vector_db)

  def rollback_with_vector_db(agent_executor):
      # Utilize vector database to manage state and rollback
      if agent_executor.detect_issue():
          agent_executor.rollback()
  

These implementations illustrate the evolving landscape of rollback strategies, highlighting the importance of automation, comprehensive testing, and strategic planning in enterprise environments.

Risk Mitigation in Rollback Strategies

Rollback strategies are essential in modern software deployment, offering a safety net for unforeseen issues. However, they come with inherent risks that must be proactively managed. This section delves into identifying these risks and implementing effective mitigation strategies, with a focus on monitoring and alerts to sustain robust rollback processes.

Identifying Potential Risks

Rollback processes can encounter several risks such as incomplete rollbacks, data inconsistencies, and prolonged downtime. These can stem from insufficient automation, lack of clarity in rollback sequences, or inadequate monitoring infrastructures.

Strategies to Mitigate and Manage Risks

• Automated Rollback Processes: Employing automation reduces human error and accelerates rollback initiation. Scripts can be designed to trigger rollbacks based on predefined thresholds or anomalies detected in the deployment process.
• Progressive Deployment Strategies: Utilizing blue-green deployments or canary releases allows for controlled rollback, minimizing impact. For instance, issues detected during a canary release can trigger an automated rollback, reverting only the affected subset of users without affecting the entire system.
• Comprehensive Documentation and AI-Assisted Decision Making: Documentation aids in understanding rollback procedures, while AI tools can predict potential rollback scenarios and recommend actions.

Role of Monitoring and Alerts in Risk Management

Effective monitoring systems are critical for identifying when rollbacks need to be initiated. Alerts based on real-time metrics such as server load, response times, and error rates can automatically trigger rollbacks or alert system administrators.

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

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

# Example of setting up an alert system
alert_system = AlertSystem(
    thresholds={'error_rate': 0.05, 'response_time': 200},
    actions=['trigger_rollback']
)

# Vector store integration for state management
pinecone = Pinecone(api_key="your_api_key")
agent_executor = AgentExecutor(memory=memory, alert_system=alert_system, vector_store=pinecone)

def monitor_and_rollback():
    if alert_system.check('error_rate'):
        agent_executor.execute_rollback()
    

Implementation Example

Consider a scenario where the system employs LangChain for agent orchestration. The following architecture diagram (described below) outlines a rollback strategy:

Architecture Diagram Description: The system includes a deployment controller connected to a vector database (Pinecone) for state management, a monitoring service that uses AI to analyze deployment metrics, and an alert system integrated with an agent executor that automates rollback actions based on predefined conditions.

Through a blend of automated processes, comprehensive monitoring, and AI-driven insights, risks associated with rollback strategies can be effectively mitigated, ensuring smooth and reliable deployments.

Governance

In the evolving landscape of enterprise software deployment, effective governance frameworks for rollback strategies are essential. These frameworks ensure that rollback mechanisms are compliant with regulatory standards and aligned with organizational policies. Key governance considerations include automated rollback processes, progressive deployment architectures, and AI-assisted decision-making.

Governance Frameworks for Rollback Strategies

Governance frameworks must incorporate automation and tooling for efficient rollback strategies. Automation is crucial in blue-green deployments, canary releases, and rolling deployments with hot-swap capabilities. For instance, using AI agents for decision-making can significantly enhance the precision of rollbacks.

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

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

    agent = AgentExecutor(
        memory=memory,
        tools=[...],  # Define tools for rollback decision-making
        verbose=True
    )
    

Compliance and Regulatory Considerations

Compliance with industry regulations is critical when implementing rollback strategies. The use of AI and machine learning tools, such as LangChain and AutoGen, must adhere to data protection and security standards. Integration with vector databases like Pinecone ensures secure storage and retrieval of rollback-related data.

    import { PineconeClient } from '@pinecone-database/client';

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

    async function rollbackDecision(data) {
      const response = await pinecone.query({
        vector: data.vector,
        topK: 5,
        includeMetadata: true
      });
      return response.matches;
    }
    

Ensuring Alignment with Organizational Policies

Alignment with organizational policies demands a robust architecture for rollback strategies. This architecture often includes multi-turn conversation handling and agent orchestration patterns to facilitate comprehensive stakeholder communication.

    import { AgentOrchestrator } from 'crewai';

    const orchestrator = new AgentOrchestrator({
      agents: [...],  // Define agents for orchestration
      policies: {
        rollbackPolicy: 'conservative'  // Define rollback policy
      }
    });

    orchestrator.run();
    

An architecture diagram would illustrate the integration of rollback agents, vector databases, and memory management systems to facilitate seamless deployment and rollback operations. Such a diagram would demonstrate the flow of data and decision-making processes across various components.

Overall, a well-defined governance framework ensures that rollback strategies are not only technically sound but also compliant and aligned with the broader organizational vision.

Metrics and KPIs

In evaluating rollback strategies, key performance indicators (KPIs) serve as crucial benchmarks for assessing effectiveness and efficiency. Critical metrics include rollback speed, success rate, and impact scope. These indicators enable developers and teams to gauge the impact and efficiency of rollback operations.

Rollback Speed measures the time taken to revert to a stable state. Fast rollbacks minimize downtime and user disruption, making this a vital KPI. For automated rollback processes, integrating analytics tools like LangChain can enhance decision-making and efficiency.

Success Rate tracks the percentage of rollbacks executed without additional errors. Achieving a high success rate is crucial, particularly in complex distributed systems.

Impact Scope assesses the user base affected by a rollback. Effective rollback strategies, such as canary releases, minimize the impact scope by initially targeting a smaller audience.

Leveraging data-driven decision-making in rollbacks involves integrating AI agents using modern frameworks such as LangChain. Below is a Python example implementing rollback strategy using AI-driven analysis:

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

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

    analytics = RollbackAnalytics()
    agent = AgentExecutor(memory, analytics)

    def evaluate_rollback():
        success_rate = analytics.calculate_success_rate()
        speed = analytics.evaluate_rollback_speed()
        if success_rate > 95 and speed < 5:
            print("Rollback strategy is optimal.")
        else:
            print("Adjust strategies for improved results.")
    

To further enhance rollback strategies, integrate a vector database like Pinecone for real-time data analysis. Here's a TypeScript example:

    import { VectorDatabase } from '@pinecone/vector-db';

    const db = new VectorDatabase({ apiKey: 'your-api-key' });

    async function logRollbackData(data) {
        await db.insert({ vector: data });
    }
    

The use of MCP protocols can streamline the orchestration of rollback strategies. Here's a JavaScript snippet illustrating tool calling patterns:

    import { MCP } from 'mcp-protocol';

    const mcp = new MCP();
    mcp.on('rollback', (data) => {
        console.log("Executing rollback:", data);
    });
    

Effective rollback strategies rely on comprehensive metrics and KPIs, automation, and AI-assisted tools for optimal performance and minimal user disruption. By leveraging frameworks like LangChain, Pinecone, and MCP, teams can improve the implementation and management of rollback processes.

Vendor Comparison: Rollback Strategies Agents

In the rapidly evolving landscape of rollback strategies, several vendors offer cutting-edge solutions that integrate automation, AI-assisted decision making, and advanced deployment architectures. Here, we compare some of the leading vendors: LangChain, AutoGen, and CrewAI, highlighting their features, benefits, drawbacks, and considerations for selection.

LangChain

LangChain is a robust framework that specializes in AI agent orchestration and memory management, making it ideal for applications requiring multi-turn conversation handling.

Features and Benefits:

• Seamless integration with vector databases like Pinecone for enhanced search capabilities.
• Strong support for memory management, allowing for detailed historical context retention.
• Efficient tool calling patterns for diverse operations.

Drawbacks:

• Complex setup process that may require additional training.
• Limited documentation on specific rollback strategy implementations.

Implementation Example:

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

    pinecone.init(api_key='your-api-key')

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )
    # Further implementation details...
    

AutoGen

AutoGen offers powerful automation capabilities especially suited for environments requiring rapid rollback and deployment.

Features and Benefits:

• Extensive support for progressive deployment strategies like blue-green and canary.
• Comprehensive AI-assisted decision-making tools for efficient rollback.

Drawbacks:

• Primarily focused on automation, with less emphasis on manual control.
• Limited support for memory-based operations.

Implementation Example:

    const { initiateRollback } = require('autogen');

    initiateRollback({
        strategy: 'blue-green',
        onFail: () => console.log('Rollback successful.')
    });
    

CrewAI

CrewAI is known for its versatility in managing both memory-related and tool-calling protocols, making it suitable for complex environments.

Features and Benefits:

• Flexible vector database integrations, such as Weaviate.
• Robust tool calling schemas for diverse operations.

Drawbacks:

• Higher cost compared to other vendors.
• Steeper learning curve for integrating AI functionalities.

Implementation Example:

    import { CrewAI } from 'crewai';
    import { ToolCaller } from 'crewai/tools';

    const toolCaller = new ToolCaller();

    CrewAI.setup({
        memoryProtocol: 'MCP',
        onToolCall: toolCaller.execute
    });
    

Considerations for Choosing a Vendor

When selecting a rollback strategy provider, consider the following:

• Technical Compatibility: Ensure the vendor supports your existing infrastructure and deployment architectures.
• Scalability: Evaluate the vendor's ability to handle growth and evolving requirements.
• Cost: Assess the pricing structure against your budget and anticipated ROI.
• Support and Documentation: Consider the quality and availability of support and resources.

By carefully evaluating these vendors and their offerings, developers can select a rollback solution tailored to their specific needs, leveraging advanced technologies for efficient and reliable deployment management.

Frequently Asked Questions about Rollback Strategies Agents

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

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

from pinecone import Index

index = Index("rollback-status")
index.upsert(items=[("deployment-1", state_vector)])
        

const mcpSchema = {
    toolName: "deploymentTool",
    actions: ["rollback", "deploy"],
    parameters: { version: "string" }
};

function callTool(action, params) {
    // Implement tool calling logic here
}