Building AI Agents and Agentic Workflows | Coursera

Build Autonomous Applications with AI Agents.
Master Agentic AI with multi-agent frameworks such as LangGraph, CrewAI, BeeAI, and AG2 (AutoGen)
位教师：Wojciech 'Victor' Fulmyk另外 6 个
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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
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.
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
Develop AI agents that can reason and perform tasks independently
Implement tool calling and chaining to create structured AI workflows
Utilize built-in LangChain agents to analyze data, generate visualizations, and execute database queries
Apply best practices in prompt engineering and tool calling to enhance AI agent performance
Build agentic AI systems using LangChain and LangGraph to support memory, iteration, and conditional logic
Design and implement self-improving agents using Reflection, Reflexion, and ReAct architectures
Apply agent orchestration techniques to build collaborative multi-agent systems
Implement agentic RAG systems that route queries and support retrieval-enhanced reasoning
Design optimized AI systems by selecting and combining appropriate agentic frameworks and architectural patterns
Implement AI workflow patterns using agentic design principles and LangGraph
Build structured multi-agent workflows using CrewAI, including agents, tasks, and custom tools
Develop AI applications with BeeAI and design conversation-driven interactions using AG2 (AutoGen)
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