At a Glance

Name: Build a Self-Reflective Deep Research Agent using LangGraph
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: hailey_quach, boyun_leung, kunal_makwana, wilbur_elbouni, faranak_heidari, joseph_santarcangelo

Build an OpenAI's DeepResearch-like self-improving AI agent that critiques its responses using structured feedback and external search. This hands-on project covers research-backed validation, graph-based workflows, and intelligent decision-making to refine accuracy and efficiency. You'll explore self-critique, adaptive learning, and automated performance optimization to create an AI agent that handles complex, multi-step tasks with precision. Perfect for developers looking to develop reliable, high-quality AI responses with external tool integration and continuous refinement.

Implement AI Self-Improvement with Reflexion Agents

Learn to build an AI system that doesn't just generate responses but actively critiques and refines its own output—just like an actor perfecting a performance based on critical feedback. The ability to self-improve is a game-changer in AI, enabling systems to produce higher-quality, research-backed, and dynamically evolving responses. In this guided project, you'll explore the Reflexion architecture, a powerful framework that enables AI agents to continuously enhance their performance through structured feedback loops.

This project is perfect for AI developers, researchers, and anyone interested in building self-improving AI systems. You'll learn how to develop a Reflexion agent that evaluates its own responses, integrates external tools for fact-checking, and iterates until it achieves optimal accuracy. Whether you're working on customer service chatbots, research assistants, or decision-support systems, mastering this technique will help you create AI solutions that evolve and improve over time.

What You'll Learn

By the end of this project, you will be able to:

Understand Reflexion agents and how they improve AI-generated responses.
Build a production-ready Reflexion agent that refines its outputs based on structured feedback.
Develop multi-step AI workflows with LangGraph that integrate self-critique mechanisms for continuous learning.
Incorporate external validation tools to enhance factual accuracy and research-backed responses.
Optimize AI decision-making with graph-based workflows and structured improvement loops.
Ensure robustness with error handling, schema validation, and iteration limits.

Whether you're aiming to enhance AI reliability, create intelligent chatbots, or optimize decision-support systems, this project will equip you with state-of-the-art AI self-improvement techniques.

What You'll Need

To get started, make sure you have:

Basic knowledge of AI and machine learning concepts.
Familiarity with programming, especially Python.
A web browser (Chrome, Edge, Firefox, Safari, or Internet Explorer).

Ready to Build Smarter AI?

Start now and create a self-improving AI agent that delivers more accurate, research-backed, and dynamically refined responses. In under one hour, you’ll unlock a powerful approach to AI-driven problem-solving and automation—a must-have skill for the future of AI development! 🚀

Offered By: IBMSkillsNetwork

Build a Self-Reflective Deep Research Agent using LangGraph

At a Glance

Implement AI Self-Improvement with Reflexion Agents

What You'll Learn

What You'll Need

Ready to Build Smarter AI?