This article explores how to build a minimal AI coding agent using Gemma 4 on llama.cpp, focusing on the role of tool harnesses and the security implications of running such agents unsandboxed. It also introduces NVIDIA OpenShell as a containment solution. By Murat Sari.
A language model, on its own, reads text and writes text. It cannot open a file, run a command, or save your landing page to disk. It can only describe what it would do. A harness turns that description into action. It takes the model’s requested tool call, runs the tool, returns the result, and lets the model decide what to do next. Strip away the personality and an “AI agent” is a model and a harness. This article will give you a better understanding of how tools like Claude Code or Cursor work by building your own minimalistic AI agent.
Some important pointes author makes:
- A minimal AI agent consists of a language model and a tool harness that translates model output into real-world actions.
- Running AI agents locally with direct access to system tools can pose significant security risks if not properly contained.
- NVIDIA OpenShell provides a secure sandboxing environment to safely run AI agents with access to system commands.
- Building a local AI agent from scratch helps demystify how advanced coding assistants like Cursor operate under the hood.
- The article emphasizes the importance of understanding both the capabilities and limitations of AI agents in development workflows.
This is a valuable resource for developers and DevOps engineers interested in understanding the inner workings of AI coding assistants. It provides practical insights into building and securing local AI agents, making it particularly relevant for those experimenting with or deploying AI tools in development environments. While the concepts are technical, the hands-on approach makes it accessible for intermediate developers looking to deepen their understanding of AI-integrated workflows. Nice one!
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