If you want to understand how modern AI agents actually work, this video offers a clear and practical walkthrough from the ground up. Kwasi Ankomah of SambaNova opens with a look at SambaNova’s full-stack AI platform, including its custom-built chips, high-speed inference, and cloud environment for running open-source models at impressive speed. He also shows how developers can get started in SambaCloud, explore hosted models, and use fast inference to power agentic applications.
The heart of the session is a hands-on explanation of the ReAct loop: reason, act, observe. Rather than treating agents as a black box, the video breaks down the core pattern that drives many agentic systems today. You’ll see how a model reasons through a task, decides when to call a tool, observes the result, and repeats the loop until it can produce a final answer. This makes the session especially useful for builders who want to debug, improve, or customize their own agent workflows with confidence.
From there, the video moves into live coding and shows how to build a ReAct agent first in raw Python, then with higher-level abstractions. Viewers get a side-by-side look at what changes when you move from a simple loop to LangGraph-powered orchestration. The session explains key concepts like runtimes, frameworks, and harnesses, then shows why they matter in real work. It also covers tool binding, state management, planning, middleware, tracing, and virtual file systems, giving viewers a grounded understanding of what goes into production-ready agents.
A major strength of the video is its focus on practical engineering tradeoffs. Instead of only talking about models, it demonstrates how prompts, tools, and middleware can dramatically improve agent behavior without changing the underlying model. Using MiniMax-M2.5, a reasoning model available through SambaNova, the session shows how structured planning, to-do tracking, and file-based context management can produce more capable and reliable outcomes. This gives developers a concrete view of how better orchestration and harness design can extend an agent’s usefulness on complex tasks.
Whether you are new to AI agents or already building with them, this video gives you a strong foundation and a useful set of next steps. You’ll come away with a better understanding of how to build agents from scratch, when to use LangGraph, and how SambaNova’s fast inference and developer resources can accelerate your work. Watch the full session to follow the notebook-based demo, learn the patterns behind effective agent systems, and explore how SambaNova Cloud, documentation, and model access can help you start building right away.