Ciroos is reimagining how enterprises can modernize their operations with AI — not as another black-box tool, but as a true teammate. Founded by a team of executives and innovators from Cisco, AWS, Gigamon and VMware, the company is tackling a problem most Ops teams know too well: rising complexity, unrelenting on-call duty and insufficient time to make sense of it all. Their solution? An AI SRE Teammate that works just like a new team member — starting with automation and augmentation via collaboration, and eventually earning the stripes to operate autonomously.

In this Q&A, co-founder and CEO Ronak Desai shares how the company came to be, why the rise of AI-native apps is reshaping the rules of incident response and what it really takes to trust AI inside high-stakes workflows. He also breaks down the signature Ciroos framework — Automate, Augment, Autopilot — and explains why the future of operations is about helping SREs reclaim their time and flow state.

AI won’t replace SREs, but it will make them exponentially more effective — and maybe even let them sleep through the night.

What problem does Ciroos solve, and what led you to start the company?

We started Ciroos because we saw a pattern emerge across industries. Operations teams everywhere are drowning in dashboards, chasing alerts and stuck in manual workflows. Despite substantial investments in their operations, most teams still rely on institutional knowledge and gut instinct to resolve incidents. This is stressful, inefficient and unsustainable.

My co-founders and I have built and scaled distributed systems in infrastructure and observability for decades. We knew the existing tools weren’t enough; teams needed help in a way that would let them make a quantum leap in the state of their operations. Not just better visibility, but real-time reasoning and decision support. That’s why we built the AI SRE Teammate: a system that thinks like a seasoned operator and works alongside operations teams from day one.

What does the rise of AI-native applications mean for operations teams, and how does that shift impact your product road map?

AI-native apps introduce a new level of operational complexity. These applications use large language models that are probabilistic in nature, span multiple domains and generate different outcomes depending on the context. You can’t debug them with the same dashboard-based “click ops” approach we’ve used for the last decade. And as AI speeds the velocity of development, Ops teams must work harder to keep them operational and secure.

This shift is forcing Ops teams to rethink everything — from detection to diagnosis to remediation. It’s no longer enough to rely on static runbooks or wait for a human to connect the dots. The systems are too fast and too dynamic. That’s why our product road map is centered around agentic AI — models that can reason across domains, adapt to changing environments and interoperate with other AI agents already in the enterprise with the right guardrails. We’re designing for a world where the AI SRE Teammate isn’t a stand-alone system — it’s part of a team of humans and other AI systems in an enterprise.

You describe Ciroos as a teammate, not a tool. What does that mean in practice?

The IT industry to date has deployed plenty of innovative tools. We’re building a teammate. That means the system behaves like a new hire: It listens first, takes on well-defined tasks, learns your environment and gradually earns your trust. You wouldn’t give a new engineer root access on day one — the same logic applies here.

In practice, our AI SRE Teammate integrates with your existing enterprise tool sets such as observability, ticketing and incident management tools. It starts by answering routine questions, deploying automations and correlating data across silos to accelerate root cause identification. Over time, it can propose fixes or even execute actions autonomously — but always under human governance. This approach mirrors how Ops teams already work: collaborative, incremental and trust-driven with policies such as two-person approval (2PR approval) in place.

What’s the idea behind Automate, Augment and Autopilot — and why does that progression matter for operations teams?

It’s the foundation of how we think about AI in operations. Here’s the breakdown:

• Automate: The AI takes on repeatable, well-understood tasks like identifying relevant log data, running standard queries, enriching alerts with context and correlating signals across systems. The human validates the results, but the process is low-touch, unlocking efficiency at scale.
• Augment: The AI expands the operator’s decision-making power by analyzing large volumes of telemetry, surfacing anomalies and proposing likely root causes. It helps answer questions like “What changed?” or “Is this incident related to that deployment?” The human collaborates with the AI — asking follow-up questions, steering the investigation and confirming conclusions, resulting in faster, higher-quality decisions, even under pressure.
• Autopilot: The AI autonomously handles the incident end to end — from detection to diagnosis to remediation — within clearly defined guardrails. It only escalates to a human when governance or uncertainty requires it. This mode delivers scale, speed and resilience while keeping human SREs in control of when and where to delegate.

This progression maps to how Ops teams actually onboard people, and different parts of this trifecta of Automate-Augment-Autopilot could be used for different use cases or scenarios in the same organization. You don’t delegate the big stuff on day one — you build up to it. By aligning AI adoption with that natural workflow, we reduce friction and accelerate impact. Teams stay in control but have a 10x engineer on their team.

What’s your long-term vision for how AI will work alongside SREs — and what should teams be doing today to get ready?

Our long-term vision is simple: Every Ops team should have an AI teammate who never sleeps, scales with demand and gets smarter over time. But that future only works if we earn trust at every step.

Right now, teams should think about where AI can reduce toil — things like incident triage, root cause analysis and noisy alert de-duplication. Start small, measure results and treat the AI like a new team member. Give it access gradually, validate its insights and expand its scope as confidence builds.

AI won’t replace SREs, but it will make them exponentially more effective — and maybe even let them sleep through the night.

If you’re interested in learning more about adding AI to your Ops team, I’d love to talk.

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