Amazon Web Services (AWS) has extended the reach of its generative artificial intelligence (AI) platform for application development to include a set of plug-in extensions, that make it possible to launch natural language queries against data residing in platforms from Datadog and Wiz.
Neha Goswami, technical director for foundational services for Amazon Business, said the overall goal is to extend the reach of the Amazon Q Developer platform into the realm of IT operations.
Those plug-ins will also reduce the number of generative AI platforms that DevOps teams might otherwise need to deploy across an AWS environment, she added. In fact, DevOps teams should expect the scope of the reach of Amazon Q Developer platform to be extended across the entire software development lifecycle (SDLC), said Goswami.
That approach will make it simpler for DevOps teams to be able to via a single pane of glass manage increasingly complex IT environments, she noted.
Amazon Q Developer was created to make it possible to leverage large language models (LLMs) to provide a natural language chat that can invoke multiple application programming interfaces (APIs) to automate a workflow. That capability is now being extended to third-party APIs to provide DevOps teams with access to additional data sources.
The overall goal is to provide a generative AI tool that understands the intent of an application development team, generates requests for data and then surfaces relevant insights.
It’s not clear how many plug-ins AWS might add to its generative AI platform, however, as DevOps teams enter the age of AI it’s already becoming clear there will be many chat interfaces and AI agents through which they can automate the management of workflows. Each organization will need to determine to what degree they will decide to standardize on multiple interfaces and agents. However, most, in the interest of limiting potential confusion, will try to limit the number of options they make available to software engineering teams.
The one certain thing is AI technologies are already rapidly being incorporated into DevOps workflows. A Techstrong Research survey finds a third (33%) of respondents are already working for organizations that make use of artificial intelligence (AI) to build software, while another 42% are considering it. Only 6% said they have no plans to use AI.
However, the survey also finds only 9% have fully integrated AI into their DevOps pipelines. Another 22% have partially achieved that goal, while 14% are doing so only for new projects. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
It’s not likely that AI will eliminate the need for application developers and software engineers any time soon, however, the overall amount of toil experienced should continue to dramatically decline as more tasks are automated. The challenge now is determining not only which tasks to automate, but also the level of supervision that might be required to ensure the overall integrity of software engineering workflows is maintained, as the overall amount of code being created exponentially increases.
