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Build a Self-Reflective Deep Research Agent using LangGraph

AdvancedGuided Project

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.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Python, LangGraph, Generative AI, AI Agents

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 min

Platform

  • SkillsNetwork

Last Update

  • July 8, 2025
About this Guided Project

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! 🚀

Instructors

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Contributors

Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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Boyun Leung

UX Designer

Creating and designing delightful experiences.

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Wilbur Elbouni

Software Developer

I think computers are pretty cool!

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