What makes modern AI agents actually useful in the real world? In this video, Kwasi Ankomah of SambaNova breaks down one of the biggest shifts in AI right now: The move from simple, shallow agents to more capable deep agents that can plan, use tools, manage memory, and handle complex tasks over longer time horizons. He opens with an introduction to SambaNova as a full-stack AI company built around custom silicon, fast inference, and agentic AI workloads, then shares how the company’s new SN50 chip is designed to deliver the low-latency, high-throughput performance these systems need. For anyone trying to understand where AI agents are headed, this session offers a strong foundation.
A major focus of the video is the rise of coding agents and why they have become one of the first clear examples of AI agents reaching mass adoption. Kwasi explains why tools like Claude Code, Cursor, Windsurf, and Codex are gaining traction with both developers and non-technical users. He also explores why many custom agents still fail, pointing to common issues like context collapse, weak planning, broken tool use, and poor evaluation. From there, the session shows what coding agents get right, especially through the use of a strong harness that brings together memory, tools, evaluation, and reusable skills to make agents more reliable and more effective.
The video also introduces the 5 pillars of reliable agents: orchestration, memory, tools, evaluation, and agent skills. These ideas are explained in plain language, making the content approachable even if you are still early in your agent-building journey. Kwasi walks through concepts like the ReAct loop, state management, context offloading, file systems, tool ecosystems, and observability, helping viewers see how deep agents operate behind the scenes. He also highlights why skills matter, showing how plain-language instructions can become reusable capabilities that extend an agent’s value without adding unnecessary code complexity.
What makes this session especially useful is its practical, hands-on approach. Rather than staying at a theory level, the video moves into a live demo using LangGraph, SambaCloud, MiniMax, Tavily, and Langfuse to show how an agent can reason, act, observe, use tools, track to-dos, write files, and expose its state through traces. This gives viewers a clear look at how real agentic systems are built, tested, and monitored. The walkthrough makes it easier to connect the architectural ideas to working code, while also showing how observability and evaluation help teams improve agent performance before and after deployment.
For developers, architects, and business teams exploring agentic AI, this video is a valuable starting point. It combines strategic context with practical implementation and ties it back to SambaNova’s platform, including fast inference, accessible cloud tools, developer resources, and infrastructure built for the demands of deep agents. If you want to better understand coding agents, learn the building blocks of reliable agent systems, and see how SambaNova can support faster and more efficient AI development, this session is well worth watching.