Reference Architecture for AI Developer Productivity

Reference Architecture for AI Developer Productivity

In this article we’ll lay out a reference architecture for an in-house AI assistant that can help development teams work with AI agents connected to their data directly from their integrated development environment (IDE) as well as providing a web portal for other team members to be able to interact with the same capabilities through their browser for non-programming tasks.

Developer Productivity Opportunities with AI

Since large language models (LLMs) gained mass popularity with the advent of GPT Turbo 3.5 (Chat GPT), many teams have been looking at LLMs as an opportunity to improve team productivity when writing and maintaining code. Teams have looked to integrate AI agents into their development workflows to help with the following tasks:

• Scaffolding new code by writing standardized but tedious pieces of code
• Generating unit tests around specific existing code
• Drafting documentation of public methods and classes
• Guided refactoring and code review of existing code that is in an unmaintainable state
• Helping analyze error messages to expedite troubleshooting of known issues

Of course, anything involving AI must be done in a secure manner that protects the organization’s intellectual property.