1 Introduction

AI tools are increasingly handling tasks that once required the direct attention of a developer, cutting down the time between initial ideation and functional code. Gartner predicts that 90% of enterprise software engineers will use AI code assistants by 2028. Figure 1-1 shows how AI tool usage can be implemented throughout the development life cycle.

However, standard AI models struggle with the complexities of embedded systems. For example, when pin assignments, peripheral configurations, or low-level timing are mismanaged, the resulting errors can be difficult for models to identify without access to hardware-specific data.

For AI-enabled workflows to succeed in this context, the agent requires authoritative access to the specific hardware and toolchain data an embedded developer relies on. Otherwise, an AI can potentially generate code that compiles correctly but fails upon execution due to incorrect hardware assumptions.

The purpose-built environment in the CCStudio IDE, part of the CCStudio development ecosystem, gives AI agents direct access to TI's editing, building, and debugging tools. CCStudio IDE supports complete, autonomous workflows for embedded system development (Figure 1-2).