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.

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

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.

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.

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.

应用的学习项目

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. 

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