At a Glance

Name: Reflexion Agent 101
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: joseph_santarcangelo, faranak_heidari, abdul_fatir

LangGraph ReAct agents: end the era of unreliable AI responses. Build nutritional advisors that actively research, critique, and improve their answers through systematic self-reflection using the powerful Reflexion framework. Learn to create AI systems that don't just respond but iterate, validate, and refine their expertise like human professionals who double-check their work before giving advice.

With the explosive growth of AI applications in healthcare, finance, and critical decision-making, the need for reliable, self-improving AI systems has never been greater. This guided project teaches you to build a Reflexion Agent—a breakthrough approach that transforms simple language models into sophisticated, self-aware systems capable of continuous improvement. Unlike traditional chatbots that provide single responses, Reflexion Agents embody the scientific method: they generate hypotheses, critique their own reasoning, identify knowledge gaps, and actively seek evidence to refine their answers.

You'll build a specialized nutritional advisor that demonstrates this powerful approach. The agent takes on the persona of a controversial nutrition expert, providing initial advice, then stepping back to critically evaluate its own response. It identifies what information might be missing or unnecessary, generates targeted research queries, and uses web search tools to gather current evidence. Finally, it synthesizes this new information into a revised, more comprehensive answer complete with citations and references.

What You'll Learn

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

Implement the Reflexion framework: Master the core technique that enables AI agents to self-critique and iteratively improve their responses, a crucial skill for building reliable AI systems.
Design sophisticated agent workflows: Use LangGraph to create complex, cyclical processes where agents can loop through reflection, research, and revision until reaching satisfactory conclusions.
Structure AI outputs with Pydantic: Learn to enforce specific response formats that ensure agents provide structured self-critiques, search queries, and evidence-based revisions.
Integrate external knowledge sources: Connect agents to real-time information through web search APIs, enabling them to access current research and evidence beyond their training data.
Build production-ready agent architectures: Create robust systems with proper error handling, iteration limits, and structured data flows that can scale to enterprise applications.

Who Should Enroll

AI/ML Engineers building production systems who need to ensure their agents provide reliable, evidence-based responses rather than hallucinated or outdated information. This project teaches essential patterns for creating trustworthy AI systems.
Data Scientists working in healthcare, finance, or research domains where AI recommendations must be backed by evidence and subject to rigorous validation processes.
Product Managers and Technical Leaders who need to understand the architecture behind next-generation AI systems and how to build agents that can be trusted with critical decision-making.
Developers with LLM experience who want to move beyond simple prompt engineering to sophisticated agent architectures that can handle complex, multi-step reasoning tasks.

Why Enroll

This project teaches you to build AI systems that embody intellectual rigor—a critical skill as AI moves into high-stakes applications. You'll learn the Reflexion framework, which is rapidly becoming the gold standard for reliable AI agents in production environments. The techniques you'll master—structured self-critique, evidence gathering, and iterative improvement—are foundational to creating AI systems that can be trusted with important decisions. By the end, you'll have both a working agent and the architectural patterns needed to build sophisticated AI systems that continuously improve their performance through self-reflection.

What You'll Need

To follow along with this guided project, you should have intermediate Python experience and familiarity with LangChain or similar LLM frameworks. Basic understanding of prompt engineering and API integrations will be helpful. You'll need a Tavily API key for web search functionality (free tier available). All code runs in Jupyter notebooks with pre-configured environments. The platform works best with current versions of Chrome, Edge, Firefox, or Safari.

Offered By: IBMSkillsNetwork

Reflexion Agent 101

At a Glance

What You'll Learn

Who Should Enroll

Why Enroll

What You'll Need

Translation missing: en.courses.show.this_is_part_of_learning_path