Waydev today revealed it has revamped its engineering intelligence platform to provide insights into how the adoption of artificial intelligence (AI) coding tools is impacting DevOps workflows.
Company CEO Alex Circei said the overall goal is to make it easier for the leaders of software engineering teams to determine the return on investment (ROI) their AI coding tools are actually providing.
While there is little doubt that AI tools are capable of generating code faster than humans, the percentage of that code making it into production environments is often unknown. DevOps engineers need to understand where AI code is being accepted, rejected or rewritten, and whether AI-assisted pull requests pass CI at the same rate as those authored by a human.
The Waydev platform, now at every checkpoint in a DevOps workflow, captures which AI agent wrote the code across all commits, repositories, teams, and tools, along with insights into usage costs. A Waydev AI agent then provides a natural language interface to enable DevOps teams to interrogate that data. Longer term, the goal is to provide an additional speech interface to query that data, said Circei.
The goal is to create a continuous feedback loop through which the data collected can be fed back to AI coding tools to improve their output, said Circei. That’s critical because it provides a means to validate the quality of the output being generated versus relying solely on an AI agent that might only tell a developer what they want to hear, he added. Ultimately, it’s the data that will tell the actual tale, said Circei.
The Stanford AI Index estimates the median company now spends $86 per developer per month on AI coding tools, a number that is likely to climb in the months and years ahead as AI coding tools become more sophisticated. A recent Futurum Group survey found a full 60% of respondents said their organization is now actively using AI to build and deploy software, with the top areas of investment over the same period are AI Copilot/AI code tools (38%), AI agent development (37%), AI-assisted testing (37%) followed closely by DevOps (37%), automated deployment (34%), software security testing (31%). When those two statistics are correlated, it’s clear organizations are already making major investments in AI coding based as much on faith as actual tangible results.
It’s not clear to what degree the rise of AI might, as a consequence, induce more software development teams to embrace engineering intelligence platforms, but there is little doubt that managing application development and deployment is becoming more challenging as the volume of code being generated continues to exponentially increase. Regardless of approach, the one certain thing is that software development, as it was once known, will never be the same again. The challenge now is determining not so much whether or not to use AI coding tools, so much as it might be the instances where developers are generating more code than it might actually be worth creating in the first place.