Ciroos.AI this week emerged from stealth to provide early access to a set of artificial intelligence (AI) agents that have been trained to augment site reliability engineers (SREs).
Fresh off raising $21 million in additional funding, Ciroos.AI CEO Ronak Desai said the SRE Teammate provides access to multiple extensible AI agents that have been trained to proactively investigate anomalies without necessarily having to be directed.
There is already a chronic shortage of SREs, so any tasks that can be assigned to AI agents is only going to reduce the stress level of existing teams, noted Desai. AI agents, for example, are already capable of reducing incident response times by up to 90% to enable SRE, he added.
That capability will prove critical as the overall volume of applications deployed in production environments continues to steadily increase in the age of AI, noted Desai.
The SRE Teammate platform makes use of the Model Context Protocol (MCP) developed by Anthropic and Agent2Agent (A2A) architecture defined by Google to enable AI agents to interact with each other.
Ultimately, DevOps teams will soon consist of multiple autonomous agents that are supervised by software engineers to ensure tasks are completed as expected, said Desai.
It’s not clear how quickly DevOps teams are embracing AI to manage workflows, but Futurum Group research finds 41% of survey respondents expect generative AI tools and platforms will be used to generate, review and test code.
Even less apparent is the extent to which organizations will adopt AI copilots and agents that have been added to their existing platforms versus opting to acquire new platforms developed from the ground up for AI agents that are supervised by software engineers.
Regardless of approach, many of the manual tasks that today conspire to make DevOps workflows more tedious will be eliminated. There is no doubt that will also change the role of software engineers, but the primary goal remains finding ways to leverage automation to enable organizations to deploy more applications in production environments that can run at higher levels of scale.
Additionally, AI technologies should make DevOps tools and platforms more accessible to a wide range of organizations that previously could not afford the level of investment required.
Each organization will, of course, need to determine the level of faith they are willing to place in AI. The challenge is that, given the current pace of innovation, the capabilities of an AI tool that might be made available in a few short months will be substantially higher than the current iteration. As such, DevOps teams need to base their overall strategy not just on what AI tools can do today but, more importantly, what kinds of capabilities will be available in the near future.
In the meantime, DevOps teams would be well-advised to start making a list of tasks that are likely to be assigned to an AI agent with an eye toward determining how the role of software engineers will evolve from there.
