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Building Powerful AI Agents Using Multimodal Collaborative Pods

•13 min read
AI Agents
MCP
Multimodal AI
System Design
Building Powerful AI Agents Using Multimodal Collaborative Pods

Building Powerful AI Agents Using Multimodal Collaborative Pods

With Multimodal Collaborative Pods (MCPs) serving as the foundation, we can now build advanced AI agents capable of complex reasoning, planning, and execution. This guide will walk you through the process of creating powerful AI agents using MCP architecture.

Understanding AI Agents

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. When built on top of MCPs, these agents gain several powerful capabilities:

  • Multimodal Understanding: Processing and reasoning across different types of data
  • Collaborative Intelligence: Working with other agents to solve complex problems
  • Contextual Awareness: Maintaining and utilizing relevant context over time
  • Goal-directed Behavior: Planning and executing actions to achieve objectives

Agent Architecture

The architecture of an AI agent built on MCPs typically includes:

+-------------------+    +-------------------+
| Perception System |    | Action System     |
| (MCP Core)        |<-->| (Tool Executors)  |
+-------------------+    +-------------------+
        |  ^                      ^  |
        v  |                      |  v
+-------------------+    +-------------------+
| Planning Module   |<-->| Memory System     |
| (Task Decomposer) |    | (Working Memory)  |
+-------------------+    +-------------------+
             \                  /
              \                /
               v              v
          +-------------------+
          | Learning Module   |
          | (Adaptation)      |
          +-------------------+

Step 1: Define Agent Goals and Capabilities

Begin by clearly defining what your agent needs to accomplish:

const agentDefinition = {
  name: "ResearchAssistant",
  description: "An AI research assistant that can analyze scientific papers, synthesize findings, and draft summaries.",
  goals: [
    "Analyze academic papers and extract key findings",
    "Synthesize information across multiple sources",
    "Generate well-structured research summaries",
    "Answer research-related questions with citations"
  ],
  capabilities: [
    "Document understanding",
    "Image analysis",
    "Web search",
    "Data extraction",
    "Text generation"
  ],
  tools: [
    "DocumentAnalyzer",
    "WebSearcher",
    "CitationManager",
    "SummaryGenerator"
  ]
};

Step 2: Implement the Perception System

The perception system builds on the MCP’s multimodal processing capabilities:

class AgentPerceptionSystem {
  constructor(config) {
    this.mcp = new MultimodalCollaborativePod(config.mcp);
    this.inputParsers = config.inputParsers || defaultInputParsers();
  }
  
  async perceive(inputs) {
    // Pre-process inputs using specialized parsers
    const parsedInputs = {};
    for (const [type, input] of Object.entries(inputs)) {
      if (this.inputParsers[type]) {
        parsedInputs[type] = await this.inputParsers[type](input);
      } else {
        parsedInputs[type] = input; // Pass through
      }
    }
    
    // Process through the MCP
    return this.mcp.process(parsedInputs, "Analyze and understand all inputs");
  }
  
  // Register a new input parser
  registerParser(inputType, parserFunction) {
    this.inputParsers[inputType] = parserFunction;
    return this;
  }
}

// Example input parsers
function defaultInputParsers() {
  return {
    pdf: async (pdfBuffer) => extractTextAndImagesFromPDF(pdfBuffer),
    webpage: async (url) => scrapeWebpage(url),
    image: async (imageBuffer) => extractFeaturesFromImage(imageBuffer),
    text: async (text) => text // Simple passthrough
  };
}

Step 3: Create the Planning Module

The planning module helps the agent break down complex tasks:

class PlanningModule {
  constructor(config) {
    this.reasoningEngine = new ReasoningEngine(config.reasoning);
    this.planTemplates = config.planTemplates || {};
    this.maxPlanSteps = config.maxPlanSteps || 10;
  }
  
  async createPlan(goal, context) {
    // Select a plan template based on the goal, or use a generic one
    const template = this.planTemplates[goal.type] || this.planTemplates.generic;
    
    const planningPrompt = template
      .replace('{GOAL}', goal.description)
      .replace('{CONTEXT}', JSON.stringify(context));
    
    // Generate the plan using the reasoning engine
    const planJson = await this.reasoningEngine.reason(context, planningPrompt);
    
    try {
      const plan = JSON.parse(planJson);
      
      // Validate and limit the plan
      if (Array.isArray(plan.steps) && plan.steps.length > 0) {
        plan.steps = plan.steps.slice(0, this.maxPlanSteps);
        return plan;
      }
      
      throw new Error('Invalid plan structure');
    } catch (e) {
      console.error('Failed to parse plan:', e);
      // Fallback to a simple plan
      return {
        goal: goal.description,
        steps: [{
          id: 1,
          description: 'Directly achieve: ' + goal.description,
          tool: this.suggestToolForGoal(goal)
        }]
      };
    }
  }
  
  suggestToolForGoal(goal) {
    // Logic to map goals to appropriate tools
    // This could be a simple lookup or more sophisticated matching
    const toolMappings = {
      'research': 'WebSearcher',
      'summarize': 'SummaryGenerator',
      'analyze': 'DocumentAnalyzer',
      // Default
      'default': 'GenericExecutor'
    };
    
    const goalLower = goal.description.toLowerCase();
    for (const [keyword, tool] of Object.entries(toolMappings)) {
      if (goalLower.includes(keyword)) {
        return tool;
      }
    }
    
    return toolMappings.default;
  }
}

Step 4: Build the Action System

The action system executes the steps in the plan:

class ActionSystem {
  constructor(config) {
    this.tools = {};
    this.defaultTool = config.defaultTool || 'GenericExecutor';
    
    // Register tools
    for (const [name, toolConfig] of Object.entries(config.tools || {})) {
      this.registerTool(name, new ToolExecutor(name, toolConfig));
    }
  }
  
  registerTool(name, toolInstance) {
    this.tools[name] = toolInstance;
    return this;
  }
  
  async executeStep(step, context) {
    const tool = this.tools[step.tool] || this.tools[this.defaultTool];
    
    if (!tool) {
      throw new Error('No tool available for: ' + step.tool);
    }
    
    console.log('Executing step: ' + step.description + ' with tool: ' + step.tool);
    
    try {
      const result = await tool.execute(step, context);
      return {
        success: true,
        result,
        metadata: {
          toolUsed: step.tool,
          timestamp: new Date().toISOString()
        }
      };
    } catch (error) {
      console.error('Tool execution failed:', error);
      return {
        success: false,
        error: error.message,
        metadata: {
          toolUsed: step.tool,
          timestamp: new Date().toISOString()
        }
      };
    }
  }
  
  async executePlan(plan, context) {
    const results = [];
    let updatedContext = { ...context };
    
    for (const step of plan.steps) {
      const stepResult = await this.executeStep(step, updatedContext);
      results.push(stepResult);
      
      // Update context with the results of this step
      updatedContext = {
        ...updatedContext,
        ['step_' + step.id + '_result']: stepResult
      };
      
      // If a step fails and is marked as critical, stop execution
      if (!stepResult.success && step.critical) {
        break;
      }
    }
    
    return {
      planResults: results,
      finalContext: updatedContext
    };
  }
}

class ToolExecutor {
  constructor(name, config) {
    this.name = name;
    this.config = config;
    this.apiClient = config.apiClient;
  }
  
  async execute(step, context) {
    // Implementation depends on the specific tool
    // For example, a web search tool might look like this:
    if (this.name === 'WebSearcher') {
      const query = this.extractQueryFromStep(step, context);
      const searchResults = await this.apiClient.search(query, {
        numResults: this.config.numResults || 5
      });
      
      return this.processSearchResults(searchResults);
    }
    
    // Document analyzer example
    if (this.name === 'DocumentAnalyzer') {
      const document = context.document || step.document;
      if (!document) {
        throw new Error('No document provided for analysis');
      }
      
      return this.apiClient.analyzeDocument(document, {
        extractTables: this.config.extractTables || false,
        extractFigures: this.config.extractFigures || false
      });
    }
    
    // Implement other tool types...
    
    throw new Error('Tool ' + this.name + ' execution not implemented');
  }
  
  extractQueryFromStep(step, context) {
    // Logic to extract search query from step and context
    return step.parameters?.query || step.description;
  }
  
  processSearchResults(results) {
    // Process and format search results
    return results.map(result => ({
      title: result.title,
      url: result.url,
      snippet: result.snippet,
      source: result.source
    }));
  }
}

Step 5: Implement the Memory System

The memory system maintains the agent’s working memory:

class AgentMemorySystem {
  constructor(config) {
    this.workingMemory = config.initialWorkingMemory || {};
    this.episodicMemory = [];
    this.mcpMemory = config.mcpMemory; // Reference to the underlying MCP memory
  }
  
  // Update working memory with new information
  updateWorkingMemory(updates) {
    this.workingMemory = {
      ...this.workingMemory,
      ...updates,
      lastUpdated: new Date().toISOString()
    };
    
    return this.workingMemory;
  }
  
  // Record a complete interaction episode
  recordEpisode(episode) {
    const timestampedEpisode = {
      ...episode,
      timestamp: new Date().toISOString(),
      episodeId: 'ep_' + (this.episodicMemory.length + 1)
    };
    
    this.episodicMemory.push(timestampedEpisode);
    
    // Also add to MCP memory for semantic search
    if (this.mcpMemory) {
      this.mcpMemory.addToContext(timestampedEpisode);
    }
    
    return timestampedEpisode;
  }
  
  // Get the current working memory
  getWorkingMemory() {
    return { ...this.workingMemory };
  }
  
  // Retrieve recent episodes
  getRecentEpisodes(count = 5) {
    return this.episodicMemory.slice(-count);
  }
  
  // Search for relevant episodes
  async searchRelevantEpisodes(query) {
    if (this.mcpMemory) {
      return this.mcpMemory.retrieveRelevantContext(query);
    }
    
    // Fallback to simple keyword matching if no MCP memory
    return this.episodicMemory.filter(ep => 
      JSON.stringify(ep).toLowerCase().includes(query.toLowerCase())
    ).slice(-5);
  }
}

Step 6: Build the Learning Module

The learning module helps the agent improve over time:

class LearningModule {
  constructor(config) {
    this.feedbackStore = [];
    this.adaptationEngine = config.adaptationEngine;
    this.learningRate = config.learningRate || 0.1;
    this.lastAdaptationTime = null;
  }
  
  // Record feedback for learning
  recordFeedback(feedback) {
    const processedFeedback = {
      ...feedback,
      timestamp: new Date().toISOString(),
      processed: false
    };
    
    this.feedbackStore.push(processedFeedback);
    return processedFeedback;
  }
  
  // Learn from accumulated feedback
  async adapt() {
    if (!this.adaptationEngine) {
      console.warn('No adaptation engine configured, learning disabled');
      return null;
    }
    
    // Get unprocessed feedback
    const unprocessedFeedback = this.feedbackStore.filter(fb => !fb.processed);
    
    if (unprocessedFeedback.length === 0) {
      return null;
    }
    
    try {
      // Generate adaptations based on feedback
      const adaptations = await this.adaptationEngine.generateAdaptations(unprocessedFeedback);
      
      // Apply the adaptations
      const appliedAdaptations = await this.applyAdaptations(adaptations);
      
      // Mark feedback as processed
      unprocessedFeedback.forEach(fb => {
        fb.processed = true;
      });
      
      this.lastAdaptationTime = new Date().toISOString();
      
      return appliedAdaptations;
    } catch (error) {
      console.error('Adaptation failed:', error);
      return null;
    }
  }
  
  async applyAdaptations(adaptations) {
    const appliedAdaptations = [];
    
    for (const adaptation of adaptations) {
      try {
        switch (adaptation.type) {
          case 'planTemplate':
            // Update planning templates
            this.updatePlanTemplate(adaptation.templateId, adaptation.update);
            appliedAdaptations.push(adaptation);
            break;
            
          case 'toolConfiguration':
            // Update tool configurations
            this.updateToolConfig(adaptation.toolId, adaptation.update);
            appliedAdaptations.push(adaptation);
            break;
            
          case 'promptImprovement':
            // Improve prompts based on feedback
            this.updatePrompt(adaptation.promptId, adaptation.update);
            appliedAdaptations.push(adaptation);
            break;
            
          default:
            console.warn('Unknown adaptation type: ' + adaptation.type);
        }
      } catch (error) {
        console.error('Failed to apply adaptation ' + adaptation.type + ':', error);
      }
    }
    
    return appliedAdaptations;
  }
  
  // Helper methods for specific adaptation types
  updatePlanTemplate(templateId, update) {
    // Implementation depends on how plan templates are stored
  }
  
  updateToolConfig(toolId, update) {
    // Implementation depends on how tools are configured
  }
  
  updatePrompt(promptId, update) {
    // Implementation depends on how prompts are managed
  }
}

Step 7: Assembling the Agent

Now we can bring all components together:

class MCP_Agent {
  constructor(config) {
    // Initialize core components
    this.perceptionSystem = new AgentPerceptionSystem(config.perception);
    this.planningModule = new PlanningModule(config.planning);
    this.actionSystem = new ActionSystem(config.action);
    this.memorySystem = new AgentMemorySystem(config.memory);
    this.learningModule = new LearningModule(config.learning);
    
    // Agent metadata
    this.id = config.id || 'agent_' + Date.now();
    this.name = config.name || 'Generic MCP Agent';
    this.description = config.description || '';
    this.capabilities = config.capabilities || [];
    
    // For tracking current tasks
    this.currentTask = null;
  }
  
  async processInput(input, goal) {
    // Start tracking a new task
    this.currentTask = {
      id: 'task_' + Date.now(),
      startTime: new Date().toISOString(),
      goal,
      status: 'in_progress'
    };
    
    try {
      // Step 1: Perception - understand the input
      console.log('Agent ' + this.name + ' perceiving input...');
      const perception = await this.perceptionSystem.perceive(input);
      
      // Step 2: Retrieve relevant context from memory
      console.log('Retrieving relevant context...');
      const relevantContext = await this.memorySystem.searchRelevantEpisodes(goal);
      
      // Step 3: Create context for planning
      const planningContext = {
        perception,
        relevantEpisodes: relevantContext,
        workingMemory: this.memorySystem.getWorkingMemory(),
        input
      };
      
      // Step 4: Create a plan
      console.log('Creating plan for goal: ' + goal + '...');
      const plan = await this.planningModule.createPlan({ description: goal }, planningContext);
      
      // Step 5: Execute the plan
      console.log('Executing plan with ' + plan.steps.length + ' steps...');
      const { planResults, finalContext } = await this.actionSystem.executePlan(plan, planningContext);
      
      // Step 6: Generate the final response
      const response = await this.generateResponse(goal, plan, planResults, finalContext);
      
      // Step 7: Update memory
      const episode = {
        task: this.currentTask.id,
        input,
        goal,
        plan,
        planResults,
        response
      };
      
      this.memorySystem.recordEpisode(episode);
      this.currentTask.status = 'completed';
      this.currentTask.endTime = new Date().toISOString();
      
      // Step 8: Trigger adaptation if needed
      if (this.shouldAdapt()) {
        this.learningModule.adapt().catch(e => 
          console.error('Adaptation failed:', e)
        );
      }
      
      return response;
      
    } catch (error) {
      console.error('Agent ' + this.name + ' encountered an error:', error);
      
      this.currentTask.status = 'failed';
      this.currentTask.error = error.message;
      this.currentTask.endTime = new Date().toISOString();
      
      // Return a graceful error response
      return {
        success: false,
        error: error.message,
        fallbackResponse: 'I encountered an issue while processing your request: ' + error.message
      };
    }
  }
  
  async generateResponse(goal, plan, planResults, context) {
    // This would typically use the MCP's reasoning engine to generate a coherent response
    // based on the results of the plan execution
    
    // For now, we'll create a simple structured response
    const successfulSteps = planResults.filter(r => r.success);
    const failedSteps = planResults.filter(r => !r.success);
    
    if (failedSteps.length > 0 && failedSteps.some(step => step.critical)) {
      return {
        success: false,
        message: "I wasn't able to fully complete the task because some critical steps failed.",
        completedSteps: successfulSteps.length,
        failedSteps: failedSteps.length,
        details: planResults
      };
    }
    
    // Combine results from successful steps
    const combinedResults = successfulSteps.map(step => step.result);
    
    return {
      success: true,
      message: "I've completed the task: " + goal,
      completedSteps: successfulSteps.length,
      results: combinedResults,
      details: planResults
    };
  }
  
  shouldAdapt() {
    // Logic to determine if adaptation should be triggered
    // For example, after a certain number of episodes or on a schedule
    if (!this.learningModule.lastAdaptationTime) {
      return true; // First time
    }
    
    const hoursSinceLastAdaptation = 
      (new Date() - new Date(this.learningModule.lastAdaptationTime)) / (1000 * 60 * 60);
      
    return hoursSinceLastAdaptation > 24; // Adapt once per day
  }
  
  // Methods for external feedback
  async provideFeedback(feedback) {
    return this.learningModule.recordFeedback(feedback);
  }
  
  // Get agent status
  getStatus() {
    return {
      id: this.id,
      name: this.name,
      description: this.description,
      capabilities: this.capabilities,
      currentTask: this.currentTask,
      memoryStats: {
        episodesStored: this.memorySystem.episodicMemory.length,
        workingMemorySize: Object.keys(this.memorySystem.workingMemory).length
      },
      lastAdaptation: this.learningModule.lastAdaptationTime
    };
  }
}

Step 8: Deployment and Integration

Finally, deploy your agent and integrate it with your application:

// Example agent configuration
const agentConfig = {
  id: 'research_assistant_v1',
  name: 'Research Assistant',
  description: 'An AI research assistant that can analyze scientific papers, synthesize findings, and draft summaries.',
  capabilities: ['document-analysis', 'web-search', 'summarization'],
  
  perception: {
    mcp: mcpConfig, // Configuration for the underlying MCP
    inputParsers: customInputParsers // Custom parsers for your domain
  },
  
  planning: {
    reasoning: reasoningConfig,
    planTemplates: {
      research: "Given the goal: {GOAL}\nAnd this context: {CONTEXT}\nCreate a detailed research plan with steps.",
      summarization: "Create a plan to summarize the following: {CONTEXT}\nTo achieve: {GOAL}",
      generic: "Create a step-by-step plan to achieve: {GOAL}\nUsing this context: {CONTEXT}"
    }
  },
  
  action: {
    tools: {
      WebSearcher: { apiClient: webSearchClient },
      DocumentAnalyzer: { apiClient: documentAnalysisClient },
      CitationManager: { apiClient: citationClient },
      SummaryGenerator: { apiClient: summaryClient }
    }
  },
  
  memory: {
    initialWorkingMemory: {
      userPreferences: {
        citationStyle: 'APA',
        detailLevel: 'comprehensive'
      }
    },
    mcpMemory: mcpMemoryInstance
  },
  
  learning: {
    adaptationEngine: adaptationEngine,
    learningRate: 0.2
  }
};

// Create the agent
const researchAgent = new MCP_Agent(agentConfig);

// Example API endpoint for using the agent
async function handleAgentRequest(req, res) {
  const { input, goal } = req.body;
  
  try {
    const result = await researchAgent.processInput(input, goal);
    res.json(result);
  } catch (error) {
    res.status(500).json({
      error: 'Agent processing failed',
      message: error.message
    });
  }
}

Advanced Agent Capabilities

With the foundation in place, you can extend your agent with more sophisticated capabilities:

Multi-Agent Collaboration

Enable your agent to work with other agents:

class AgentCollaborationManager {
  constructor(config) {
    this.mainAgent = config.mainAgent;
    this.specialistAgents = config.specialistAgents || {};
    this.collaborationProtocols = config.protocols || {};
  }
  
  async collaborateOn(task, context) {
    // Determine which specialists to involve
    const relevantSpecialists = this.identifyRelevantSpecialists(task);
    
    // Create a collaboration plan
    const collaborationPlan = await this.createCollaborationPlan(task, relevantSpecialists);
    
    // Execute the collaboration
    return this.executeCollaboration(collaborationPlan, context);
  }
  
  identifyRelevantSpecialists(task) {
    // Logic to determine which specialist agents should be involved
    // based on the task requirements and agent capabilities
  }
  
  async createCollaborationPlan(task, specialists) {
    // Logic to create a plan that coordinates multiple agents
  }
  
  async executeCollaboration(plan, context) {
    // Logic to execute the plan across multiple agents
  }
}

Self-Improvement

Implement mechanisms for agents to improve their own capabilities:

class SelfImprovementModule {
  constructor(config) {
    this.agent = config.agent;
    this.improvementStrategies = config.strategies || {};
  }
  
  async analyzePerformance() {
    // Analyze past episodes to identify improvement opportunities
  }
  
  async generateImprovements() {
    // Generate specific improvements based on analysis
  }
  
  async implementImprovements(improvements) {
    // Apply the improvements to the agent configuration
  }
}

Conclusion

Building AI agents with MCP architecture provides a powerful framework for creating intelligent, adaptive systems that can work across different types of data and collaborate effectively. The modular design allows for continuous improvement and extension of capabilities.

As this field evolves, we can expect even more sophisticated agent architectures that push the boundaries of what’s possible with AI. By understanding the fundamental components and how they interact, you’re well-positioned to build and deploy effective AI agents for a wide range of applications.