Multi-Agent Systems in Frontend: RAG, Generative UI & LLM Orchestration

Multi-agent systems represent the natural evolution of AI in frontend development. Instead of a single model trying to do everything, multiple specialized agents collaborate to solve complex problems. In this article, we'll explore how to implement multi-agent architectures in React/Next.js applications, with emphasis on RAG (Retrieval-Augmented Generation), GenUI (Generative UI), and LLM coordination.

What is a multi-agent system?

A multi-agent system coordinates several specialized AI agents working together toward a common goal. Unlike a single generalist LLM, each agent has:

• Specialization: Specific domain of knowledge or skill
• Autonomy: Ability to make independent decisions
• Communication: Protocol for exchanging information with other agents
• Coordination: Mechanism to synchronize actions

Basic architecture

interface Agent {
  id: string;
  role: string;
  capabilities: string[];
  execute: (task: Task) => Promise<AgentResult>;
  communicate: (message: Message, toAgent: string) => Promise<void>;
}

interface MultiAgentSystem {
  agents: Map<string, Agent>;
  coordinator: Coordinator;
  messageQueue: MessageQueue;
  
  orchestrate: (goal: Goal) => Promise<SystemResult>;
}

Advantages over single-agent systems

Aspect	Single agent	Multi-agent system
Specialization	Generalist	Domain-specific experts
Scalability	Limited by context	Distributed across agents
Maintenance	All or nothing	Per-agent updates
Performance	Central bottleneck	Parallel processing
Reliability	Single point of failure	Redundancy and recovery

RAG Agent: Retrieval and generation

RAG (Retrieval-Augmented Generation) combines semantic search with LLM generation to produce accurate responses based on your knowledge base.

RAG architecture in frontend

import { createClient } from '@supabase/supabase-js';
import Anthropic from '@anthropic-ai/sdk';

class RAGAgent implements Agent {
  private vectorDB: SupabaseClient;
  private llm: Anthropic;
  private embeddings: EmbeddingModel;
  
  id = 'rag-agent';
  role = 'knowledge-retrieval';
  capabilities = ['search', 'retrieve', 'augment'];
  
  constructor() {
    this.vectorDB = createClient(
      process.env.SUPABASE_URL!,
      process.env.SUPABASE_KEY!
    );
    this.llm = new Anthropic();
    this.embeddings = new OpenAIEmbeddings();
  }
  
  async execute(task: Task): Promise<AgentResult> {
    // STEP 1: Generate query embedding
    const queryEmbedding = await this.embeddings.embed(task.query);
    
    // STEP 2: Semantic search in vector DB
    const { data: documents } = await this.vectorDB
      .rpc('match_documents', {
        query_embedding: queryEmbedding,
        match_threshold: 0.78,
        match_count: 5
      });
    
    // STEP 3: Build augmented context
    const context = documents
      .map(doc => doc.content)
      .join('\n\n');
    
    // STEP 4: Generate response with context
    const response = await this.llm.messages.create({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 2000,
      messages: [{
        role: 'user',
        content: `
          Relevant context:
          ${context}
          
          User question: ${task.query}
          
          Answer ONLY based on the provided context.
          If the information is not in the context, say "I don't have enough information".
        `
      }]
    });
    
    return {
      answer: response.content[0].text,
      sources: documents.map(d => d.metadata),
      confidence: this.calculateConfidence(documents)
    };
  }
  
  private calculateConfidence(docs: Document[]): number {
    // Confidence based on semantic similarity
    const avgSimilarity = docs.reduce((sum, d) => sum + d.similarity, 0) / docs.length;
    return Math.min(avgSimilarity * 1.2, 1.0);
  }
  
  async communicate(message: Message, toAgent: string): Promise<void> {
    // Send retrieved documents to other agents
    await this.messageQueue.send({
      from: this.id,
      to: toAgent,
      type: 'knowledge-transfer',
      payload: message
    });
  }
}

Vector database implementation

// Create table in Supabase with pgvector extension
/*
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  metadata JSONB,
  embedding VECTOR(1536)
);

CREATE INDEX ON documents 
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);
*/

// Semantic search function
/*
CREATE FUNCTION match_documents (
  query_embedding VECTOR(1536),
  match_threshold FLOAT,
  match_count INT
)
RETURNS TABLE (
  id BIGINT,
  content TEXT,
  metadata JSONB,
  similarity FLOAT
)
LANGUAGE SQL STABLE
AS $$
  SELECT
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (documents.embedding <=> query_embedding) > match_threshold
  ORDER BY similarity DESC
  LIMIT match_count;
$$;
*/

React component with RAG

'use client';

import { useState } from 'react';
import { useRAG } from '@/hooks/useRAG';

export function RAGChat() {
  const [query, setQuery] = useState('');
  const { ask, isLoading, result } = useRAG();
  
  const handleSubmit = async (e: React.FormEvent) => {
    e.preventDefault();
    await ask(query);
  };
  
  return (
    <div className="flex flex-col gap-4">
      <form onSubmit={handleSubmit} className="flex gap-2">
        <input
          type="text"
          value={query}
          onChange={(e) => setQuery(e.target.value)}
          placeholder="Ask about the documentation..."
          className="flex-1 px-4 py-2 border rounded-lg"
        />
        <button 
          type="submit" 
          disabled={isLoading}
          className="px-6 py-2 bg-blue-600 text-white rounded-lg"
        >
          {isLoading ? 'Searching...' : 'Ask'}
        </button>
      </form>
      
      {result && (
        <div className="border rounded-lg p-4">
          <p className="text-gray-800 mb-4">{result.answer}</p>
          
          {result.sources.length > 0 && (
            <div className="mt-4 pt-4 border-t">
              <h4 className="text-sm font-semibold mb-2">Sources:</h4>
              <ul className="text-sm text-gray-600 space-y-1">
                {result.sources.map((source, i) => (
                  <li key={i}>
                    <a 
                      href={source.url} 
                      className="hover:underline"
                      target="_blank"
                      rel="noopener"
                    >
                      {source.title}
                    </a>
                  </li>
                ))}
              </ul>
            </div>
          )}
          
          <div className="mt-2 text-xs text-gray-500">
            Confidence: {(result.confidence * 100).toFixed(0)}%
          </div>
        </div>
      )}
    </div>
  );
}

GenUI Agent: Dynamic interface generation

GenUI allows LLMs to generate dynamic user interfaces based on context and user needs.

GenUI architecture

interface UIComponent {
  type: 'form' | 'card' | 'list' | 'chart' | 'table';
  props: Record<string, any>;
  children?: UIComponent[];
}

class GenUIAgent implements Agent {
  id = 'genui-agent';
  role = 'ui-generation';
  capabilities = ['generate-ui', 'adapt-layout', 'optimize-ux'];
  
  private llm: Anthropic;
  private componentLibrary: Map<string, React.ComponentType>;
  
  async execute(task: Task): Promise<AgentResult> {
    const { userIntent, context, data } = task;
    
    // STEP 1: Analyze user intent
    const analysis = await this.analyzeIntent(userIntent, context);
    
    // STEP 2: Generate UI structure
    const uiSpec = await this.generateUISpec(analysis, data);
    
    // STEP 3: Render components
    const component = this.renderUI(uiSpec);
    
    return {
      component,
      reasoning: analysis.reasoning,
      alternatives: analysis.alternatives
    };
  }
  
  private async generateUISpec(
    analysis: IntentAnalysis,
    data: any
  ): Promise<UIComponent> {
    const prompt = `
      User wants: ${analysis.intent}
      Context: ${analysis.context}
      Available data: ${JSON.stringify(data, null, 2)}
      
      Generate an optimal UI specification in JSON with this structure:
      {
        "type": "form" | "card" | "list" | "chart" | "table",
        "props": { ... },
        "children": [ ... ]
      }
      
      Consider:
      - Modern UX patterns
      - Accessibility (WCAG AA)
      - Responsive design
      - Minimal cognitive load
    `;
    
    const response = await this.llm.messages.create({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 3000,
      messages: [{ role: 'user', content: prompt }]
    });
    
    return JSON.parse(response.content[0].text);
  }
  
  private renderUI(spec: UIComponent): React.ReactElement {
    const Component = this.componentLibrary.get(spec.type);
    
    if (!Component) {
      throw new Error(`Unknown component type: ${spec.type}`);
    }
    
    return (
      <Component {...spec.props}>
        {spec.children?.map((child, i) => (
          <React.Fragment key={i}>
            {this.renderUI(child)}
          </React.Fragment>
        ))}
      </Component>
    );
  }
  
  async communicate(message: Message, toAgent: string): Promise<void> {
    // Share successful UI patterns with other agents
    await this.messageQueue.send({
      from: this.id,
      to: toAgent,
      type: 'ui-pattern',
      payload: message
    });
  }
}

Practical example: Adaptive form

'use client';

import { useState } from 'react';
import { useGenUI } from '@/hooks/useGenUI';

export function AdaptiveForm({ initialData }: { initialData: any }) {
  const { generateUI, isGenerating } = useGenUI();
  const [generatedUI, setGeneratedUI] = useState<React.ReactElement | null>(null);
  
  const handleGenerate = async () => {
    const ui = await generateUI({
      intent: 'collect user contact information',
      context: 'business contact form',
      data: initialData
    });
    
    setGeneratedUI(ui);
  };
  
  if (isGenerating) {
    return <div className="animate-pulse">Generating optimal form...</div>;
  }
  
  if (generatedUI) {
    return generatedUI;
  }
  
  return (
    <button onClick={handleGenerate}>
      Generate adapted form
    </button>
  );
}

Dynamically generated components

// The GenUI agent can generate specifications like this:
const generatedSpec: UIComponent = {
  type: 'form',
  props: {
    className: 'space-y-4',
    onSubmit: 'handleContactSubmit'
  },
  children: [
    {
      type: 'input',
      props: {
        name: 'name',
        label: 'Full name',
        type: 'text',
        required: true,
        autoComplete: 'name',
        'aria-label': 'Enter your full name'
      }
    },
    {
      type: 'input',
      props: {
        name: 'email',
        label: 'Business email',
        type: 'email',
        required: true,
        pattern: '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$',
        'aria-label': 'Enter your work email'
      }
    },
    {
      type: 'textarea',
      props: {
        name: 'message',
        label: 'How can we help you?',
        rows: 4,
        required: true,
        'aria-label': 'Describe your project or inquiry'
      }
    },
    {
      type: 'button',
      props: {
        type: 'submit',
        className: 'bg-blue-600 text-white px-6 py-3 rounded-lg',
        children: 'Submit inquiry'
      }
    }
  ]
};

Multi-agent coordinator

The coordinator orchestrates collaboration between specialized agents.

Coordinator implementation

type AgentMessage = {
  from: string;
  to: string;
  type: string;
  payload: any;
  timestamp: Date;
};

class MultiAgentCoordinator {
  private agents: Map<string, Agent>;
  private messageQueue: AgentMessage[] = [];
  private executionGraph: Map<string, string[]>;
  
  constructor(agents: Agent[]) {
    this.agents = new Map(agents.map(a => [a.id, a]));
    this.executionGraph = this.buildExecutionGraph(agents);
  }
  
  async orchestrate(goal: Goal): Promise<SystemResult> {
    console.log(`🎯 Goal: ${goal.description}`);
    
    // STEP 1: Plan which agents we need
    const plan = await this.planExecution(goal);
    console.log(`📋 Plan: ${plan.steps.length} steps`);
    
    // STEP 2: Execute plan with coordination
    const results: AgentResult[] = [];
    
    for (const step of plan.steps) {
      console.log(`▶️  Executing: ${step.agentId} - ${step.task}`);
      
      const agent = this.agents.get(step.agentId);
      if (!agent) {
        throw new Error(`Agent ${step.agentId} not found`);
      }
      
      // Execute agent with context from previous results
      const result = await agent.execute({
        ...step,
        context: this.buildContext(results, step)
      });
      
      results.push({
        agentId: step.agentId,
        stepId: step.id,
        result
      });
      
      // Process messages between agents
      await this.processMessages();
    }
    
    // STEP 3: Consolidate results
    return this.consolidateResults(results, goal);
  }
  
  private async planExecution(goal: Goal): Promise<ExecutionPlan> {
    // The coordinator uses an LLM to plan
    const prompt = `
      Goal: ${goal.description}
      
      Available agents:
      ${Array.from(this.agents.values()).map(a => 
        `- ${a.id}: ${a.role} (${a.capabilities.join(', ')})`
      ).join('\n')}
      
      Generate an optimal execution plan:
      1. Which agent should execute
      2. In what order
      3. What information it needs from other agents
      4. Success criteria
      
      JSON format:
      {
        "steps": [
          {
            "id": "step-1",
            "agentId": "rag-agent",
            "task": "description",
            "dependencies": ["step-0"],
            "successCriteria": "condition"
          }
        ]
      }
    `;
    
    const plannerLLM = new Anthropic();
    const response = await plannerLLM.messages.create({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 4000,
      messages: [{ role: 'user', content: prompt }]
    });
    
    return JSON.parse(response.content[0].text);
  }
  
  private buildContext(
    previousResults: AgentResult[],
    currentStep: ExecutionStep
  ): Context {
    // Build relevant context for current agent
    const dependencies = currentStep.dependencies || [];
    
    const relevantResults = previousResults.filter(r =>
      dependencies.includes(r.stepId)
    );
    
    return {
      previousResults: relevantResults,
      sharedKnowledge: this.extractSharedKnowledge(relevantResults),
      timestamp: new Date()
    };
  }
  
  private async processMessages(): Promise<void> {
    // Process message queue between agents
    while (this.messageQueue.length > 0) {
      const message = this.messageQueue.shift()!;
      const recipient = this.agents.get(message.to);
      
      if (recipient) {
        await recipient.communicate(message, message.from);
      }
    }
  }
  
  private consolidateResults(
    results: AgentResult[],
    goal: Goal
  ): SystemResult {
    // Consolidate results from all agents
    return {
      goal,
      success: results.every(r => r.result.success),
      results: results.map(r => r.result),
      insights: this.extractInsights(results),
      metrics: this.calculateMetrics(results)
    };
  }
  
  private extractInsights(results: AgentResult[]): string[] {
    // Extract cross-agent insights
    return results
      .flatMap(r => r.result.insights || [])
      .filter((insight, i, arr) => arr.indexOf(insight) === i);
  }
  
  private calculateMetrics(results: AgentResult[]): Metrics {
    return {
      totalTime: results.reduce((sum, r) => sum + (r.result.duration || 0), 0),
      agentsUsed: new Set(results.map(r => r.agentId)).size,
      messagesExchanged: this.messageQueue.length,
      successRate: results.filter(r => r.result.success).length / results.length
    };
  }
}

Complete system: Practical case

Let's see a complete example combining RAG, GenUI, and coordination:

Scenario: Intelligent documentation assistant

// Define agents
const ragAgent = new RAGAgent();
const genUIAgent = new GenUIAgent();
const validatorAgent = new ValidatorAgent();

// Create coordinator
const coordinator = new MultiAgentCoordinator([
  ragAgent,
  genUIAgent,
  validatorAgent
]);

// Execute complex goal
const result = await coordinator.orchestrate({
  description: 'User asks how to implement OAuth authentication in Next.js',
  context: {
    userLevel: 'intermediate',
    preferredFramework: 'Next.js 15',
    existingAuth: 'none'
  }
});

/*
Execution flow:

1️⃣ COORDINATOR plans:
   - Step 1: RAG searches OAuth + Next.js docs
   - Step 2: GenUI generates interactive tutorial
   - Step 3: Validator verifies generated code

2️⃣ RAG AGENT executes:
   - Searches Next.js docs, OAuth providers
   - Retrieves relevant examples
   - Sends context to GenUI

3️⃣ GENUI AGENT executes:
   - Receives context from RAG
   - Generates step-by-step tutorial with:
     * Executable code
     * Contextual explanations
     * Interactive forms
   - Sends code to Validator

4️⃣ VALIDATOR AGENT executes:
   - Verifies syntax
   - Checks best practices
   - Validates security (no exposed secrets)
   - Gives feedback to GenUI if errors

5️⃣ COORDINATOR consolidates:
   - Complete and validated tutorial
   - Documentation sources
   - Ready-to-copy code
   - Confidence metrics
*/

React component of complete system

'use client';

import { useState } from 'react';
import { MultiAgentSystem } from '@/lib/multi-agent';

export function IntelligentDocsAssistant() {
  const [query, setQuery] = useState('');
  const [result, setResult] = useState<SystemResult | null>(null);
  const [isProcessing, setIsProcessing] = useState(false);
  const [progress, setProgress] = useState<string[]>([]);
  
  const system = new MultiAgentSystem({
    agents: ['rag', 'genui', 'validator'],
    onProgress: (step) => {
      setProgress(prev => [...prev, `${step.agent}: ${step.message}`]);
    }
  });
  
  const handleAsk = async () => {
    setIsProcessing(true);
    setProgress([]);
    
    try {
      const result = await system.orchestrate({
        description: query,
        context: {
          userLevel: 'intermediate'
        }
      });
      
      setResult(result);
    } catch (error) {
      console.error('Error in multi-agent system:', error);
    } finally {
      setIsProcessing(false);
    }
  };
  
  return (
    <div className="max-w-4xl mx-auto p-6">
      <div className="mb-6">
        <h2 className="text-2xl font-bold mb-4">
          Intelligent Documentation Assistant
        </h2>
        
        <div className="flex gap-2">
          <input
            type="text"
            value={query}
            onChange={(e) => setQuery(e.target.value)}
            placeholder="What do you want to learn?"
            className="flex-1 px-4 py-3 border rounded-lg"
            onKeyPress={(e) => e.key === 'Enter' && handleAsk()}
          />
          <button
            onClick={handleAsk}
            disabled={isProcessing || !query}
            className="px-6 py-3 bg-blue-600 text-white rounded-lg disabled:opacity-50"
          >
            {isProcessing ? 'Processing...' : 'Ask'}
          </button>
        </div>
      </div>
      
      {/* Progress tracker */}
      {isProcessing && (
        <div className="mb-6 border rounded-lg p-4 bg-gray-50">
          <h3 className="font-semibold mb-2">System progress:</h3>
          <ul className="space-y-1 text-sm">
            {progress.map((step, i) => (
              <li key={i} className="flex items-center gap-2">
                <span className="text-green-600">✓</span>
                {step}
              </li>
            ))}
          </ul>
        </div>
      )}
      
      {/* Results */}
      {result && (
        <div className="space-y-6">
          {/* Generated UI from GenUI agent */}
          <div className="border rounded-lg p-6">
            {result.generatedUI}
          </div>
          
          {/* Sources from RAG agent */}
          {result.sources && result.sources.length > 0 && (
            <div className="border rounded-lg p-4">
              <h3 className="font-semibold mb-2">Consulted sources:</h3>
              <ul className="text-sm space-y-1">
                {result.sources.map((source, i) => (
                  <li key={i}>
                    <a href={source.url} className="text-blue-600 hover:underline">
                      {source.title}
                    </a>
                  </li>
                ))}
              </ul>
            </div>
          )}
          
          {/* Metrics */}
          <div className="grid grid-cols-4 gap-4 text-center">
            <div className="border rounded-lg p-3">
              <div className="text-2xl font-bold">{result.metrics.agentsUsed}</div>
              <div className="text-sm text-gray-600">Agents</div>
            </div>
            <div className="border rounded-lg p-3">
              <div className="text-2xl font-bold">
                {(result.metrics.totalTime / 1000).toFixed(1)}s
              </div>
              <div className="text-sm text-gray-600">Time</div>
            </div>
            <div className="border rounded-lg p-3">
              <div className="text-2xl font-bold">
                {(result.metrics.successRate * 100).toFixed(0)}%
              </div>
              <div className="text-sm text-gray-600">Success</div>
            </div>
            <div className="border rounded-lg p-3">
              <div className="text-2xl font-bold">
                {result.metrics.messagesExchanged}
              </div>
              <div className="text-sm text-gray-600">Messages</div>
            </div>
          </div>
        </div>
      )}
    </div>
  );
}

Best practices for multi-agent systems

1. Efficient communication design

// ❌ Bad: Unstructured communication
agent1.send(agent2, "here is the data");

// ✅ Good: Defined protocol
interface AgentMessage {
  type: 'request' | 'response' | 'notification';
  priority: 'high' | 'medium' | 'low';
  payload: {
    action: string;
    data: any;
    metadata: {
      timestamp: Date;
      correlationId: string;
    };
  };
}

2. Conflict handling between agents

class ConflictResolver {
  async resolve(conflicts: AgentConflict[]): Promise<Resolution> {
    // Strategy 1: Consensus voting
    const votes = conflicts.map(c => ({
      agent: c.agentId,
      decision: c.proposal,
      confidence: c.confidence
    }));
    
    // Strategy 2: Priority by specialization
    const expert = this.findMostQualifiedAgent(conflicts);
    
    // Strategy 3: Meta-agent arbitrator
    const arbiter = new ArbiterAgent();
    const decision = await arbiter.arbitrate(conflicts);
    
    return decision;
  }
}

3. Performance optimization

class PerformanceOptimizer {
  async optimize(system: MultiAgentSystem) {
    // Parallelize independent agents
    const independentTasks = this.findIndependentTasks(system.plan);
    await Promise.all(independentTasks.map(t => t.execute()));
    
    // Cache common results
    const cache = new AgentCache({ ttl: 300 }); // 5 minutes
    
    // Rate limiting per agent
    const limiter = new RateLimiter({
      maxConcurrent: 5,
      minInterval: 100 // ms
    });
  }
}

4. Observability and debugging

class AgentMonitor {
  trackExecution(system: MultiAgentSystem) {
    // Structured logging
    system.on('agent:start', (agent, task) => {
      console.log(`[${agent.id}] Starting: ${task.description}`);
    });
    
    system.on('agent:complete', (agent, result) => {
      console.log(`[${agent.id}] Completed in ${result.duration}ms`);
    });
    
    system.on('agent:error', (agent, error) => {
      console.error(`[${agent.id}] Error:`, error);
      
      // Send to monitoring service
      this.sendToSentry({
        agentId: agent.id,
        error,
        context: system.getContext()
      });
    });
    
    // Real-time metrics
    system.on('message:sent', (from, to, message) => {
      this.metrics.increment('messages.sent', {
        from,
        to,
        type: message.type
      });
    });
  }
}

Advanced use cases

Personalized recommendation system

const recommendationSystem = new MultiAgentCoordinator([
  new UserProfilingAgent(),    // Analyzes user behavior
  new ContentAnalysisAgent(),  // Analyzes available content
  new RAGAgent(),              // Searches similar content
  new RankingAgent(),          // Ranks recommendations
  new ExplanationAgent()       // Explains why recommended
]);

const recommendations = await recommendationSystem.orchestrate({
  description: 'Generate personalized recommendations for user',
  context: { userId: '123', currentPage: '/blog' }
});

Collaborative AI-powered code editor

const codeEditorSystem = new MultiAgentCoordinator([
  new CodeCompletionAgent(),   // Intelligent autocompletion
  new RefactoringAgent(),      // Refactoring suggestions
  new BugDetectionAgent(),     // Bug detection
  new DocumentationAgent(),    // Generates documentation
  new TestGenerationAgent()    // Generates tests
]);

// Agents collaborate as user types
editorSystem.on('code:change', async (change) => {
  const suggestions = await codeEditorSystem.orchestrate({
    description: 'Analyze change and generate suggestions',
    context: { change, fileContext: editor.getContext() }
  });
});

Conclusion

Multi-agent systems represent the future of intelligent frontend development. By specializing agents in specific tasks (RAG for knowledge, GenUI for interfaces, validators for quality), we achieve:

✅ Greater precision: Each agent is an expert in its domain
✅ Better scalability: Parallel and distributed processing
✅ Maintainability: Update one agent without affecting the system
✅ Robustness: Redundancy and failure recovery
✅ Extensibility: Add new agents without rewriting code

The most promising use cases include:

• Intelligent documentation assistants
• AI-powered collaborative code editors
• Personalized recommendation systems
• Dynamic interface generation (GenUI)
• Semantic search with context (RAG)

Want to implement multi-agent systems in your frontend application? Our AI-driven development service helps you design custom multi-agent architectures. We also offer AI integration and QA automation with specialized agents. Contact us to explore how multi-agents can transform your product.