Logz.io today launched an edition of its observability platform that is intended to be primarily used by artificial intelligence (AI) agents rather than DevOps engineers.
Company CEO Tomer Levy said that as DevOps continues to evolve it’s become apparent that most tasks pertaining to observability will soon be assigned to AI agents. This edition of the Open 360 observability platform exposes machine-readable schemas alongside every panel, allowing AI agents to autonomously create charts, interpret anomalies and modify entire dashboards.
Rather than simply adding a chat bot interface to an existing observability platform, Logz.io is moving to make it possible for DevOps engineers and AI agents to collaboratively observe complex IT environments using a common set of dashboards, said Levy.
That approach still produces insights via a dashboard that can still be read by humans, but the assumption is that AI agents will be the primary end user of those dashboards, he added.
Tasks assigned to those AI agents include executing custom playbooks created by DevOps teams, reviews of new application deployments, cost analysis and security assessments. AI agents can also automatically generate visualizations based on natural language requests, interpret and analyze anomalies in real time and interact with other agents to automate more complex tasks.
Logz.io has already tested this AI agent-first approach with more than 200 IT organizations that have deployed more than 2,000 Logz.io AI Agents, resulting in saving a total of approximately 25,000 engineering hours this month alone—equivalent to 12 full-time SRE positions. At the current adoption rate, Logz.io projects customers will recover roughly 300,000 hours of engineering time over the next year.
It’s not clear how extensively AI agents will change the way DevOps workflows are managed, but it is apparent that with the rise of AI coding tools, the number of applications being deployed by any organization is about to significantly increase. Most existing DevOps teams are not going to be able to keep pace with that rate of application development and deployment without being augmented in some way by AI agents.
The challenge then becomes determining which tasks are best assigned to an AI agent versus those that still require the expertise of a DevOps engineer. Over time, however, DevOps engineers will find themselves supervising a small army of AI agents that have been trained to perform a range of specific tasks.
Ultimately, AI agents should make applications both more resilient and secure as anomalous behavior is identified sooner. Armed with those insights, it then becomes feasible for application developers that are similarly augmented by AI agents to troubleshoot applications in a way that results in more issues being resolved long before they have any meaningful impact on the organization.
In fact, AI agents should make it possible for more organizations to benefit from observability by proactively addressing application issues rather than simply continuing to monitor a set of pre-defined metrics that provide limited actionable intelligence. That, of course, might take some time for IT professionals to accept and fully trust, but in time, many of the tasks that conspire to make managing DevOps workflows more tedious will, like it or not, be increasingly performed by AI agents.
