Building AI Agents and Agentic Workflows

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 LLM s 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

• Develop agentic AI applications integrating tools, reasoning, and self-improvement
• Design AI agents with LangGraph using memory, iteration, RAG, and workflow patterns
• Orchestrate multi-agent systems for collaboration and task coordination with CrewAI
• Build conversation-driven agentic AI assistants and compare AI frameworks for use cases

• Basic familiarity with the topic and common terminology
• Readiness to practice through applied exercises or case-based work