Ciroos announced a suite of capabilities designed to manage the skyrocketing complexity of AI-powered infrastructure.
Ciroos’s AI-native SRE platform delivers proactive reliability intelligence specifically designed for modern enterprise environments.
Ciroos Signal Intelligence acts as an alert normalizer, ingesting, deduplicating and correlating them into a high-fidelity signal for investigation. By algorithmically performing these actions without the need to author rules, operators can automatically cut alert noise on day one, compressing thousands of noisy events into rich signals. For advanced use cases, operators can use a policy engine to govern investigation scope, cost, and other actions required to enhance autonomous reliability. In early customer deployments, Ciroos Signal Intelligence reduced noise by up to 60%.
Ciroos is reducing operational toil through a dynamic knowledge graph that persistently compounds over time. Unlike static topological maps, the knowledge graph serves as the platform’s persistent memory architecture. To build this living, persistent model, Ciroos has expanded the graph's ingestion capabilities to seamlessly fuse historical observability data, service mappings from systems of record, and eBPF (Extended Berkeley Packet Filter) with human feedback. By autonomously learning from every incident, the graph retains critical system state and tribal knowledge provided directly by human engineers. This ensures that once a problem is solved, that understanding persists permanently and never requires human input twice.
Ciroos has significantly expanded its cross-domain investigation capabilities to navigate these sprawling architectures, now boasting more than 50 active integrations (including deep new support for AWS ElastiCache, Amazon RDS, PostgreSQL, and MariaDB).
Rather than replacing existing observability tools, Ciroos acts as a connective intelligence layer. Its multi-agent architecture automatically correlates machine data from these systems with runtime telemetry and tribal knowledge captured through Ciroos User Behavior Patterns™. By bringing together this full context, Ciroos delivers accurate, automated root cause diagnosis, compounding intelligence over time and enabling customers to extract significantly more value from their existing observability investments without the need for manual troubleshooting war rooms.
“Modern site reliability in the enterprise isn’t simply a matter of using AI to crunch observability data in the hopes it will determine a root cause,” said Ronak Desai, Co-Founder & CEO of Ciroos. “It requires human context that truly understands the environment. It must look at signals from multiple data sources. It must have predictable, repeatable incident resolutions, rich auditability, and be constantly refined as infrastructure and AI models evolve. Enterprise site reliability is hard. With Signal Intelligence, persistent context, and cross-domain investigations, Ciroos aims to make it easier for enterprises on the path towards autonomous reliability.”
The Latest
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...