Agentic AI represents a new way of building software that leverages LLMs to complete some or all of the steps in complex tasks. Instead of generating single responses to prompts, agentic workflows enable AI to plan multi-step processes, execute them iteratively, and improve outputs through reflection and tool use. This course teaches you to build these sophisticated AI systems from the ground up.
You’ll master four design patterns that power agentic AI systems:
- Reflection: AI critiques its own work and iterates to improve quality—like code review, but automated. - Tool Use: Connect AI to databases, APIs, and external services so it can actually perform actions, not just generate text. - Planning: Break complex tasks into executable steps that AI can follow and adapt when things don’t go as expected. - Multi-Agent: Coordinate multiple specialized AI systems to handle different parts of a complex workflow.
The course emphasizes practical implementation using Python, building each pattern from first principles before exploring frameworks, giving you the flexibility to customize AI workflows for your specific needs. You’ll learn to deconstruct business processes into agentic workflows, identifying where human-like iteration and tool interaction can automate complex tasks. Critical evaluation skills are woven throughout—you’ll build robust testing frameworks, conduct systematic error analysis, and optimize systems for production deployment.
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