The article provides a hands-on guide to building a Large Language Model (LLM) agent capable of writing and executing code, significantly expanding its potential applications. By integrating code execution, agents can inspect files, process data, and generate outputs.

Three core components are detailed: the LLM model itself, the workspace where files are staged and results are stored, and the execution environment. The execution environment, often managed via Docker, provides a sandboxed space for the generated code to run safely. Control mechanisms are also discussed, addressing how to define the agent's system instructions, manage staged files, and configure the execution environment's dependencies and access.

The OpenAI Agents SDK offers tools like the Sandbox Agent, which utilizes a Manifest to specify staged files. A case study demonstrates an agent analyzing a CSV file for time series anomaly detection, generating reports and charts. This involves preparing a Docker runtime with necessary libraries like pandas and matplotlib, then configuring the agent to use this sandbox for analysis. Other options include using the Code Interpreter Tool for OpenAI-managed environments or the Shell Tool for command-line tasks.