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Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Learn how to build intelligent agentic workflows from the ground up using LlamaIndex, a powerful framework for orchestrating AI behavior. Starting with a single-agent setup and moving toward multi-agent collaboration, you'll create a system that can perform research, generate reports, and reflect on its own outputs.
You’ll begin by setting up a simple AI agent using the AgentWorkflow class and connecting it to functionalities such as searching the web. You’ll learn how to give your agents “memory” using context and make their responses feel more dynamic with streaming outputs. As the course progresses, you’ll explore advanced capabilities like event handling, concurrent execution, and creating custom workflows using step-based logic. You'll also implement self-reflection loops to allow agents to improve their own outputs.
In the final step, you’ll move beyond single-agent systems by designing teams of AI agents that work together. You’ll build workflows where each agent takes on a specialized role—like researching, writing, and reviewing—and shares information through a common context. You’ll experiment with sequential, looping, and branching workflows, gaining insights into how to manage complex tasks with multiple agents.
Build Smart, Capable AI Agents from Scratch
Learn how to build intelligent agentic workflows from the ground up using LlamaIndex, a powerful framework for orchestrating AI behavior. Starting with a single-agent setup and moving toward multi-agent collaboration, you'll create a system that can perform research, generate reports, and reflect on its own outputs.
Create and Orchestrate Agentic Workflows
You’ll begin by setting up a simple AI agent using the AgentWorkflow class and connecting it to functionalities such as searching the web. You’ll learn how to give your agents “memory” using context and make their responses feel more dynamic with streaming outputs. As the course progresses, you’ll explore advanced capabilities like event handling, concurrent execution, and creating custom workflows using step-based logic. You'll also implement self-reflection loops to allow agents to improve their own outputs.
Design Multi-Agent Systems with LlamaIndex
In the final step, you’ll move beyond single-agent systems by designing teams of AI agents that work together. You’ll build workflows where each agent takes on a specialized role—like researching, writing, and reviewing—and shares information through a common context. You’ll experiment with sequential, looping, and branching workflows, gaining insights into how to manage complex tasks with multiple agents.
