Ready to build the next generation of AI applications? This specialization from IBM experts equips you with the skills to develop agentic AI systems using modern frameworks and workflow patterns.

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You’ll start with LangGraph, creating agents that support memory, iteration, conditional logic, and retrieval-augmented generation (Agentic RAG).

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Next, you’ll explore self-improving agents that use reflection and reasoning, and design multi-agent systems that collaborate through orchestration. With CrewAI, you’ll learn to structure agents, tasks, and tools into modular workflows that solve real-world problems.

 

Finally, you’ll expand your toolkit with frameworks like AG2 (AutoGen) and BeeAI, applying them to cases such as question answering, summarization, and conversation-driven applications. You’ll also study design patterns like sequential and routing to make systems scalable and reliable.

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You will apply the concepts you’ve learned using hands-on labs to build Agentic systems powered by LLMs such as  OpenAI GPT, Meta Llama, and IBM Granite.

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By the end of this program, you’ll be able to compare frameworks, apply AI design patterns, implement orchestration, and build AI systems that support multi-agent collaboration and advanced workflows. These are the sought-after skills that employers look for in Software Developers, Machine Learning Engineers, Data Scientists, and GenAI Engineers.

 

Applied Learning Project

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In this specialization, you’ll complete hands-on labs that guide you in designing agent workflows, configuring multi-agent systems, & integrating tools into structured applications. Access to cloud-based lab environments are provided to you at no extra cost. By the end, you’ll have built working prototypes with LangGraph, CrewAI, BeeAI, & AG2 (AutoGen) to tackle real-world challenges in collaboration & conversation-driven tasks. 

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Some examples of the labs & projects are:​

• Build an AI Math Assistant with LangChain 

• AI Powered Data Analysis with LCEL 

• Build Interactive LLM Agents with Tools 

• Building a Reflection Agent with LangGraph 

• Building a Reflexion Agent with External Knowledge Integration

• ReAct: Build Agents that Reason Before Acting

• DocChat: Build a Multi-Agent RAG System

• Implement CrewBase and Structuring a Crew

• Create a Structured Meal & Grocery Planner with CrewAI

• Building Agentic AI systems with BeeAI

• Build a Multi-Agent Chatbot with AG2 (AutoGen) for Healthcare​​​​​​​

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What you will Learn​

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• Develop agentic AI applications that integrate tools, support reasoning, and improve performance through reflection

• Design AI agents with LangGraph using agentic AI design principles such as memory, iteration, RAG, and workflow patterns

• Orchestrate agentic multi-agent systems with CrewAI for collaboration, coordination, and workflows

• Build conversation-driven agentic AI assistants with BeeAI and AG2; compare AI frameworks for real-world use case applicability