Beyond Chatbots: Building Self-Improving AI Agents with Agentic RAG

🚀 From Chatbots to Autonomous Agents

Most AI applications today are glorified chatbots—they respond to queries but don't remember, don't learn, and don't improve. They're stateless, forgetful, and limited to single-turn interactions.

But what if your AI could:

• Break down complex questions into sub-queries automatically?
• Reflect on its own answers and improve them iteratively?
• Adapt its search strategy based on context?
• Remember conversations and learn from every interaction?
• Execute tools safely with human oversight?

That's exactly what Agentic RAG and Production Agent Runtime bring to HazelJS. We're not just building chatbots—we're building autonomous, self-improving AI agents.

🎯 What Makes an Agent "Agentic"?

Traditional RAG systems are passive—they retrieve documents when asked. Agentic RAG is active—it plans, reflects, adapts, and learns:

⚡ Production Agent Runtime: The Execution Engine

While Agentic RAG handles intelligent retrieval, the Agent Runtime manages the entire agent lifecycle:

• Stateful Execution: Agents maintain context across multiple steps—unlike stateless request handlers
• Tool Orchestration: Safe, auditable tool execution with approval workflows
• Memory Integration: Automatic conversation tracking and entity memory
• Pause/Resume: Support for long-running workflows with state persistence
• Observability: Comprehensive event system for monitoring and debugging

💡 Real-World Example: Research Assistant Agent

Let's build a research assistant that demonstrates both Agentic RAG and Agent Runtime:

import { Agent, Tool } from '@hazeljs/agent';
import { AgenticRAGService } from '@hazeljs/rag/agentic';
import {
  QueryPlanner,
  SelfReflective,
  AdaptiveRetrieval,
  MultiHop,
  ContextAware,
} from '@hazeljs/rag/agentic';

@Agent({
  name: 'research-assistant',
  description: 'Intelligent research assistant with agentic capabilities',
  enableMemory: true,
  enableRAG: true,
})
export class ResearchAssistant {
  constructor(private agenticRAG: AgenticRAGService) {}

  /**
   * Agentic retrieval with all the intelligence built-in
   */
  @QueryPlanner({ decompose: true, maxSubQueries: 5, parallel: true })
  @SelfReflective({ maxIterations: 3, qualityThreshold: 0.8 })
  @AdaptiveRetrieval({ autoSelect: true, contextAware: true })
  @MultiHop({ maxHops: 3, strategy: 'breadth-first' })
  @ContextAware({ windowSize: 5, entityTracking: true })
  async research(query: string, sessionId: string): Promise<SearchResult[]> {
    return this.agenticRAG.retrieve(query, { sessionId });
  }

  @Tool({
    description: 'Save research findings to knowledge base',
    requiresApproval: true,
  })
  async saveFindings(findings: { title: string; content: string }) {
    // Save to database - requires approval
    return { success: true, id: 'finding-123' };
  }
}

With just a few decorators, this agent:

• ✅ Automatically breaks complex research questions into sub-queries
• ✅ Reflects on result quality and improves answers
• ✅ Adapts retrieval strategy based on query type
• ✅ Performs multi-hop reasoning across documents
• ✅ Maintains conversation context and entity tracking
• ✅ Requires approval before saving findings (human-in-the-loop)

🔥 Key Differentiators

📊 Performance That Matters

Agentic RAG isn't just smarter—it's measurably better:

• Query Planning: 2-3x better coverage on complex queries
• Self-Reflection: 15-20% improvement in result quality
• HyDE Technique: 10-15% better retrieval for abstract queries
• Caching: 10x faster for repeated queries
• Active Learning: Continuous improvement over time

🎨 Decorator-First: Write Less, Do More

The beauty of HazelJS is its declarative API. Instead of writing hundreds of lines of orchestration code, you use decorators:

// Traditional approach: 200+ lines of orchestration code
// HazelJS approach: 5 decorators

@QueryPlanner({ decompose: true, maxSubQueries: 5 })
@SelfReflective({ maxIterations: 3 })
@AdaptiveRetrieval({ autoSelect: true })
@ContextAware({ windowSize: 5 })
@Cached({ ttl: 3600 })
async retrieve(query: string): Promise<SearchResult[]> {
  // All intelligence handled by decorators
  return this.vectorStore.search(query);
}

🚀 Getting Started

Ready to build your first autonomous agent? Here's how:

# Install packages
npm install @hazeljs/agent @hazeljs/rag @hazeljs/ai

# Create your first agent
npx @hazeljs/cli generate agent research-assistant

💭 The Future is Agentic

We're moving beyond the era of simple chatbots. The future belongs to autonomous AI agents that:

• Think before they act
• Learn from every interaction
• Improve their own performance
• Work safely with human oversight
• Remember context across sessions

With HazelJS Agent Runtime and Agentic RAG, you're not just building AI applications—you're building intelligent, autonomous systems that get smarter over time.

🎯 Conclusion

Agentic RAG and Production Agent Runtime represent a fundamental shift in how we build AI applications. No longer are we limited to stateless chatbots—we can now build autonomous, self-improving agents that plan, reflect, adapt, and learn.

The future of AI is agentic. And with HazelJS, that future is available today.