Public lesson preview
Learn how AI agents observe, reason, and act autonomously using tool use, memory, planning strategies, and multi-agent architectures for complex real-world tasks.
By the end of this lesson, you will understand:
A language model answers questions. An agent accomplishes goals. The difference is the loop: agents observe their environment, reason about what to do, take actions using tools, and repeat until the task is complete. This lesson covers the architecture, patterns, and failure modes of modern agentic AI systems.
Agentic Workflows - Building Multi-Agent AI Systems is part of AI Canopy
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