ReadyTensor Agentic AI Certification Chatbot: A Multi-Agent System Built with LangGraph and LangChain

Artificial Intelligence continues to evolve beyond single-model responses into coordinated systems capable of reasoning, delegation, and action. At ReadyTensor, this transformation is embodied in the Agentic AI Certification Chatbot, a multi-agent project powered by Groq and designed to demonstrate how agentic architectures can create intelligent, dynamic learning experiences.

Developed using LangGraph with integrations from LangChain, this project showcases how modular AI agents can work collaboratively to guide users through an entire learning and certification journey on the ReadyTensor platform. The system is composed of four autonomous agents—each with distinct roles—and supported by three specialized tools that extend their reasoning, search, and execution capabilities.

System Overview

At its core, the Agentic AI Certification Chatbot serves as a personalized guide for learners enrolled in ReadyTensor’s Agentic AI Certification program. Instead of interacting with a single monolithic chatbot, users engage with a dynamic network of agents that communicate with one another to deliver precise, contextual responses.

Each agent has a specific responsibility, enabling the system to perform complex, multi-step tasks with remarkable efficiency and accuracy:

Router Agent – This agent acts as the orchestrator, intelligently routing user queries to the appropriate specialized agent. It analyzes the user’s intent, determines the nature of the request—be it technical, enrollment-related, or course-focused—and delegates accordingly.

Course Content Agent – This agent handles all interactions related to course materials. It retrieves and explains modules from the Agentic AI curriculum, assists with lesson summaries, and provides structured learning recommendations using the document retrieval tool.

Enrollment Agent – Responsible for managing the onboarding and enrollment process, this agent helps learners understand course offerings, guides them through sign-up steps, and provides support for any enrollment issues.

Technical Agent – A crucial component for hands-on learners, the Technical Agent leverages the Python Code Executor tool to run, debug, and explain code snippets directly within the conversation. This provides real-time coding assistance and helps learners practice without leaving the chatbot interface.

Integrated Tools

To enhance these agents’ reasoning and execution abilities, the system incorporates three specialized tools:

DuckDuckSearchGO – A web search tool enabling real-time access to updated information from the web. This ensures the chatbot provides accurate and current answers beyond static datasets.

Python Code Executor – Allows the Technical Agent to interpret and execute Python scripts securely, turning the chatbot into a live coding assistant.

Document Retrieval Tool – Connects to ReadyTensor’s knowledge base, enabling context-aware retrieval of training materials, FAQs, and reference guides for learners.

Together, these tools create a seamless environment where users can learn, experiment, and get certified—all within one agentic system.

Technology and Architecture

The system was entirely built using LangGraph, a framework designed for creating multi-agent reasoning workflows. LangGraph’s node-based structure allowed each agent to be modeled as an independent node connected through a directed graph of communication pathways. This design ensures modularity, scalability, and transparency in the reasoning process.

A limited integration with LangChain was also implemented to handle certain document retrieval and tool invocation tasks, demonstrating how both frameworks can coexist within a unified agentic pipeline.

Project Impact

The ReadyTensor Agentic AI Certification Chatbot represents a practical demonstration of applied agentic AI—bridging education, automation, and interactive intelligence. By combining autonomous agents with Groq’s high-speed inference capabilities, the system delivers real-time, contextually aware responses that make the learning experience immersive and efficient.

From an engineering perspective, the project illustrates how modern AI systems can move beyond static prompts into orchestrated, tool-augmented reasoning frameworks. It also sets a foundation for future ReadyTensor initiatives that will expand agent collaboration, memory, and real-time analytics.

Conclusion

This project reflects the future of AI-driven education—where agents reason, coordinate, and act to empower users through adaptive and intelligent interaction.
Built entirely from the ground up, the ReadyTensor Agentic AI Certification Chatbot embodies the vision of ReadyTensor: to make Agentic AI accessible, practical, and transformative.

Here is the link to my chatbot in use:
https://youtu.be/SySth09Ptcg