Reading these tea leaves, it is reasonable to believe that these new team structures will consist of multiple AI agents, each with a specific role in the software development process. For example, one agent might define the project scope and objectives, while another focuses on project planning and quality analysis. Human engineers will oversee this process, providing input and verifying the AI-generated results.
Intelligence: Knowledge capture and access become automated
Jira, Slack, Confluence, Workday, Dynamics, Teams. Docs… This is knowledge management, also known as the bane of any developer’s existence. Capturing, storing, and making available the wealth of content created during the software development process is daunting, time-consuming, budget-consuming, and often done very poorly. Because most of this information is captured and stored as text, it’s a ripe area for LLMs to step in and help automate and clean up the process.
Knowledge management basically consists of two functions: knowledge capture, i.e. determining how you effectively and efficiently capture knowledge, and knowledge access, i.e. determining not only how you offer access to knowledge but also how to make sure people access it. While both capture and access are interesting and important, I find the possibilities for access most promising because, with generative AI, you can make all of this data and context proactively applied rather than relegated to access only. As you capture information in an LLM, you can extend that context model to other applications.
