AI Agents & Systems
AI Agents Fundamentals
Learn the foundations of AI agent systems through a cinematic story about a house that keeps its memories. Build a practical mental model for system architecture, embeddings, model-provider connections, prompt engineering, prompt review, agentic design patterns, memory data shapes, and a production-ready Tiger Cloud data layer.
Course Snapshot
Instructor
Vivid Labs
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First lesson free · Join waitlist for full access
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AI Agents & Systems
Chapter List
Map the major parts of an AI system—from model providers and prompts to embeddings, memory, agents, and data stores—and see how they work together as one architecture.
Convert textual meaning into numerical vectors, compare those vectors for semantic similarity, and understand why embeddings are the foundation of retrieval and memory.
Set up the Python environment and connect an application to an AI provider with a clean boundary for credentials, requests, and model responses.
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