Build Agentic AI with LangChain, LangGraph & CrewAI β create AI Agents, use tools, and manage memory.
β±οΈ Length: 9.7 total hours
β 5.00/5 rating
π₯ 523 students
π November 2025 update
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- Course Overview
- Embark on a transformative journey into the realm of Agentic AI, a paradigm shift in how we build intelligent systems.
- This course demystifies the intricate workings of AI agents, moving beyond simple prompt engineering to cultivate truly autonomous and goal-oriented applications.
- You will explore the foundational principles that underpin intelligent agents, including their perception, cognition, action, and learning loops.
- Dive deep into the practical implementation of these concepts using leading-edge frameworks, empowering you to construct sophisticated AI agents from the ground up.
- Gain a comprehensive understanding of how to integrate diverse tools and functionalities, enabling your agents to interact with the real world and perform complex tasks.
- Uncover the secrets to managing an agent’s historical context and decision-making processes, crucial for building robust and scalable enterprise solutions.
- Master the art of orchestrating multiple agents into collaborative networks, tackling multifaceted challenges through distributed intelligence.
- Learn to navigate the complexities of deploying and maintaining Agentic AI systems in production environments, ensuring reliability and performance.
- The course emphasizes a hands-on, project-driven approach, culminating in the development of real-world AI agent solutions.
- Prepare to witness the evolution of AI from static tools to dynamic, problem-solving entities.
- Requirements / Prerequisites
- A solid grasp of Python programming is essential, including familiarity with data structures, control flow, and object-oriented concepts.
- Basic understanding of Artificial Intelligence and Machine Learning fundamentals will be beneficial, though core concepts will be reinforced.
- Comfort with command-line interfaces and basic software development practices is expected.
- Access to a computer with a stable internet connection capable of running development environments and executing code.
- An eagerness to explore novel AI architectures and contribute to the future of intelligent systems.
- Familiarity with version control systems like Git is recommended for collaborative project work.
- Skills Covered / Tools Used
- Agent Architecture Design: Crafting the internal logic and decision-making frameworks for autonomous AI agents.
- Tool Integration and Orchestration: Seamlessly connecting AI agents with external APIs, databases, and other software functionalities.
- Memory Management Techniques: Implementing effective strategies for agents to retain and leverage past experiences for improved performance.
- Reasoning and Planning Algorithms: Developing agents capable of complex problem-solving and strategic task execution.
- LangChain Framework: Leveraging this powerful library for building LLM-powered applications and agents.
- LangGraph for Graph-Based Agent Flows: Designing and implementing dynamic, multi-step agent workflows using graph structures.
- CrewAI for Multi-Agent Systems: Orchestrating sophisticated collaborative efforts between multiple AI agents.
- Evaluation and Monitoring Strategies: Establishing metrics and methodologies for assessing agent performance and identifying areas for improvement.
- Production Deployment Best Practices: Understanding the nuances of deploying and scaling Agentic AI solutions in real-world scenarios.
- API Interaction and Data Processing: Programmatically interacting with various services and handling data for agent operations.
- Benefits / Outcomes
- Become a proficient developer of sophisticated, autonomous AI agents capable of complex task execution.
- Acquire the skills to build enterprise-grade AI solutions that can automate workflows and drive business innovation.
- Develop a deep understanding of the underlying mechanisms that power intelligent agent behavior.
- Gain practical experience with cutting-edge frameworks that are shaping the future of AI development.
- Be equipped to design, implement, and deploy AI agents that exhibit reasoning, planning, and adaptive capabilities.
- Contribute to the development of more intelligent and autonomous systems across various industries.
- Enhance your career prospects in the rapidly growing field of AI engineering and development.
- Build a portfolio of practical projects demonstrating your mastery of Agentic AI concepts and tools.
- PROS
- Highly practical and hands-on approach with a focus on real-world applications.
- Utilizes industry-leading frameworks (LangChain, LangGraph, CrewAI) for building cutting-edge AI agents.
- Covers advanced topics like memory management, tool orchestration, and multi-agent systems.
- Strong emphasis on enterprise-grade development and productionization.
- Excellent rating and student reviews indicate high-quality content and effective teaching.
- Regular updates ensure the course stays current with the fast-paced AI landscape.
- CONS
- Requires a solid foundation in Python programming, which might be a barrier for absolute beginners.
Learning Tracks: English,IT & Software,Other IT & Software
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