Tabnine today made available a preview of an artificial intelligence (AI) agent that ensures code adheres to an organization’s policies and software development standards.
Company president Peter Guagenti said the Tabnine Code Review Agent provides DevOps teams with an AI agent that can be trained to ensure that application developers adhere to best software development practices the organization has defined for code found in both a software repository as a pull request is made and within an integrated development environment (IDE).
Previously, Tabnine enabled DevOps teams to leverage multiple large language models (LLMs) to both write and test code. The company is now extending that capability by previewing the first in what will become a series of AI agents that will improve the overall quality of the software DevOps teams deploy by automating specific tasks, said Guagenti.
The overall goal is to make it simpler for DevOps teams to capture institutional knowledge and then apply it by surfacing guidance in natural language to ensure rules are being appropriately followed as code is written, he added. If any aspect of the code doesn’t conform with those rules, the Tabnine Code Review Agent will flag it and surface suggested edits to resolve it. DevOps teams in addition to defining rules, can also disable specific rules as they see fit.
While generative AI tools are clearly making individual developers more productive in terms of the speed at which code can be written, the impact this capability will ultimately have on improving quality, security and compliance is likely to have greater significance to the business, said Guagenti.
Generative AI will also eliminate confirmation bias because the machines conducting the reviews will not have any prejudices about who created a specific piece of code using what methodology, he added.
However, in the absence of an AI agent that reviews code as it is created, more code than ever is being rejected because the quality of the code created by machines is not sufficient, noted Guagenti.
Nevertheless, the pace at which software engineering teams are embracing AI continues to increase. A Techstrong Research study finds a third (33%) of respondents are working for organizations that already make use of AI, while another 42% are considering it. Only 6% said they have no plans to use AI.
However, 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.
AI agents should, theoretically, make it easier to achieve that goal because they have already been trained to automate specific tasks versus requiring software engineers to string together a series of prompts that, hopefully, will generate the desired output from a large language model (LLM).
Each organization will naturally adopt AI as they best see fit, but it’s no longer a question of whether to use AI as much as to what degree to rely on it.
