About this Learning Path
Turn your weekend into an AI development sprint that leaves you with agents smart enough to replace your most tedious tasks. This hands-on path shows you how to build intelligent systems using LangGraph and LangChain. Your agents will learn to use tools, reason through problems, and get better with each interaction—just like training a smart assistant.
Many AI systems today are glorified search engines—they answer questions but can't actually solve problems. Real AI agents are different. They can think through complex challenges, use external tools, research information, and even critique their own work to get better results.
This learning path takes you from basic concepts to building sophisticated AI agents that actually get things done. You'll work with LangGraph and LangChain—the same frameworks used by leading AI companies—to create agents that can research topics, solve mathematical problems, analyze content, and even improve their own responses through self-reflection.
Many AI systems today are glorified search engines—they answer questions but can't actually solve problems. Real AI agents are different. They can think through complex challenges, use external tools, research information, and even critique their own work to get better results.
This learning path takes you from basic concepts to building sophisticated AI agents that actually get things done. You'll work with LangGraph and LangChain—the same frameworks used by leading AI companies—to create agents that can research topics, solve mathematical problems, analyze content, and even improve their own responses through self-reflection.
Project: Build a Simple ReAct Agent from Scratch
Objective: Establish foundational understanding of ReAct framework principles and basic agent architecture.
Skills Covered: Agent design patterns, structured reasoning implementation, basic tool integration, and context management techniques.
Learning Outcomes: understand core ReAct concepts and build their first reasoning agent capable of systematic problem-solving.
Skills Covered: Agent design patterns, structured reasoning implementation, basic tool integration, and context management techniques.
Learning Outcomes: understand core ReAct concepts and build their first reasoning agent capable of systematic problem-solving.
Project: How to Build - AI Math Assistant with LangChain Tool Calling
Objective: Develop practical tool-calling capabilities with focus on accuracy and reliability.
Skills Covered: Custom tool creation, input validation, error handling, and precision-focused AI responses.
Learning Outcomes: create specialized mathematical tools and understand how to eliminate hallucinations in computational tasks.
Skills Covered: Custom tool creation, input validation, error handling, and precision-focused AI responses.
Learning Outcomes: create specialized mathematical tools and understand how to eliminate hallucinations in computational tasks.
Project: Build and Execute Your Own Tools for LLMs
Objective: Expand tool-calling capabilities to interact with external services and digital content.
Skills Covered: API integration, video content analysis, external service connections, and manual tool execution control.
Learning Outcomes: design tools that enable LLMs to interact with YouTube and other external platforms effectively.
Skills Covered: API integration, video content analysis, external service connections, and manual tool execution control.
Learning Outcomes: design tools that enable LLMs to interact with YouTube and other external platforms effectively.
Project: Build Reasoning and Acting AI Agents with ReAct
Objective: Implement complete ReAct cycles with observation and adaptation mechanisms.
Skills Covered: Multi-step reasoning, information gathering strategies, result analysis, and approach refinement.
Learning Outcomes: create agents capable of complex query processing and systematic problem resolution.
Skills Covered: Multi-step reasoning, information gathering strategies, result analysis, and approach refinement.
Learning Outcomes: create agents capable of complex query processing and systematic problem resolution.
Project: Make Your AI Agents Smarter with Reflection in LangGraph
Objective: Introduce self-improvement capabilities through structured reflection mechanisms.
Skills Covered: Iterative response improvement, self-critique implementation, and content optimization workflows.
Learning Outcomes: build agents that can evaluate and enhance their own outputs through systematic reflection.
Skills Covered: Iterative response improvement, self-critique implementation, and content optimization workflows.
Learning Outcomes: build agents that can evaluate and enhance their own outputs through systematic reflection.
Project: Reflexion Agent 101: LangGraph ReAct agents
Objective: Develop specialized domain agents with self-validation capabilities.
Skills Covered: Domain-specific reasoning, response validation, systematic self-reflection, and professional-grade advice generation.
Learning Outcomes: create nutritional advisory agents that research, critique, and improve their recommendations.
Skills Covered: Domain-specific reasoning, response validation, systematic self-reflection, and professional-grade advice generation.
Learning Outcomes: create nutritional advisory agents that research, critique, and improve their recommendations.
Project: Build a Self-Reflective Deep Research Agent using LangGraph
Objective: Construct advanced research agents with external validation and continuous improvement.
Skills Covered: Research-backed validation, graph-based workflows, intelligent decision-making, and performance optimization.
Learning Outcomes: develop sophisticated agents capable of complex, multi-step research tasks with built-in quality assurance.
Skills Covered: Research-backed validation, graph-based workflows, intelligent decision-making, and performance optimization.
Learning Outcomes: develop sophisticated agents capable of complex, multi-step research tasks with built-in quality assurance.
Upon completing this learning path, learners will possess practical knowledge of agent architecture, tool integration, and self-improvement mechanisms.
