Join us for an exciting, code-first exploration where we transform a humble chat completion script into a sophisticated, fully agentic AI system, all through a series of live, iterative Python demonstrations. We'll kick off with a memory-less agent that does well with single questions but stumbles on conversational follow-ups. Watch as we add persistent message history, enabling coherent multi-turn dialogues. Next, we'll hit the wall of context limits when tackling file operations and large data, revealing why raw tool access isn't enough. Through hands-on additions of tool-calling capabilities, we'll differentiate between built-in tools and configurable MCP services. Our agent will gain "hands" to act on the world, "eyes" to inspect complex data structures (databases, logs, packets, registries), and advanced memory architectures to manage its cognitive load. Culminating in a blueprint for agentic memory management, including adaptive system prompts, modular skill loading, planning modes, and compressible context, we'll demonstrate how these elements combine to create truly autonomous, efficient AI agents.