The AI landscape is abuzz with talk of "agentic intelligence" and "autonomous reasoning." But beneath the hype, a quieter revolution is underway: Small Language Models (SLMs) are starting to perform the core reasoning and orchestration tasks once thought to require massive LLMs. In this talk, we’ll demystify the current state of “AI agents,” show how compact models like Phi-2, xLAM 8B, and Nemotron-H 9B can plan, reason, and call tools effectively, and demonstrate how you can deploy them on consumer-grade hardware. Using Python and lightweight frameworks such as LangChain, we’ll show how anyone can quickly build and experiment with their own local agentic systems. Attendees will leave with a grounded understanding of agent architectures, SLM capabilities, and a roadmap for running useful agents without the GPU farm.