Anatomy of a Coding Agent: A step-by-step illustration (marginlab.ai)

The latest installment in a multi-part series on AI-based coding assistants delves into the inner workings of coding agents, emphasizing their core components—the model and the scaffold. A coding agent, exemplified by tools like Opencode, integrates a Large Language Model (LLM) with tool calling capabilities to read and write code efficiently. This piece specifically breaks down the "tool calling loop," a fundamental mechanism that involves the LLM making function calls to execute tasks within a local environment. The example provided illustrates how a user's query about TypeScript files is processed through HTTP requests, showcasing the communication between the LLM and local tools. This exploration is significant for the AI/ML community as it lays a technical foundation for understanding how coding agents can facilitate software development by streamlining code management processes. The utilization of tools within the Opencode scaffold enables coding agents to perform complex tasks like searching for bugs and editing code without requiring extensive predefined configurations. As these systems become more sophisticated, they promise to enhance productivity and improve coding accuracy, illustrating the potential of combining AI with development workflows. The series promises further insights into building a fully-featured coding agent in future installments.