Introducing Ciroos Signal Intelligence™

Alert fatigue is one of the most persistent challenges in enterprise operations. When a single infrastructure incident triggers a cascade of alerts across network, security, compute, and database monitoring systems simultaneously, operations teams are left drowning in noise rather than investigating root cause. This “alert storm” phenomenon doesn’t just slow response times — it erodes trust in monitoring systems and causes critical signals to get buried in the flood.

Well-designed AI SRE systems absorb this noise and add a layer of intelligence before investigating the contributing factors. Today we’re introducing Ciroos Signal Intelligence™: a set of advanced capabilities purpose-built to solve this problem by absorbing the noise, extracting relevant signals and providing optional controls to human operators to customize to their needs without creating cognitive overload.

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Problem Statement

Root cause analysis (RCA) of service events in large-scale environments presents significant challenges in computational efficiency and investigative accuracy. Many enterprise environments suffer from two challenges at opposite extremes — alert storms caused by multiple layers firing alerts for the same underlying condition and alert silence caused by the absence of effective alerting. While alert storms result in redundant investigations, leading to extended resolution times, alert silence leads to reactive troubleshooting and human intervention; collectively, both result in reliability targets being missed.

While Large Language Models (LLMs) offer promising capabilities for handling complex analytical tasks, directly applying them to raw multidimensional metrics yields suboptimal results. This is because despite their reasoning strengths, LLMs lack domain-specific knowledge of key production systems (e.g., database, infrastructure elements) and struggle with precise mathematical computations on raw numerical data.

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What is Ciroos Signal Intelligence™?

Ciroos Signal Intelligence™ ingests alerts from disparate data sources, deduplicates redundant events, and correlates related alerts into high-fidelity signals — all without requiring operators to author a single rule. It uses a Knowledge Graph enriched with contextual information about your environment to algorithmically group, correlate, and deduplicate incoming alerts. Ciroos Signal Intelligence™ uses a combination of LLM reasoning, the Knowledge Graph, and metric causal analysis that effectively addresses both alert storms and alert silence.

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How It Fits In: The Ciroos AI SRE Architecture

Ciroos Signal Intelligence™ is the first stage in the Ciroos pipeline. As shown in Figure 1 below, raw alerts enter this stage, which normalizes, deduplicates, and correlates them into high-fidelity signals. Those signals are then passed to the Reasoning Core — a multi-agent investigator that leverages the Knowledge Graph to perform root cause analysis. The Actuator produces a structured RCA report with supporting evidence, with the ability to execute remediation actions. The Knowledge Graph maintains the real-time state of the environment with persistent memory. It provides Ciroos Signal Intelligence™ with the contextual awareness needed to correlate alerts intelligently, and gives the Reasoning Core the system topology and dependency information needed to investigate with precision. The Knowledge Graph is not just a snapshot in time — it acts as a time machine, allowing the Reasoning Core to reconstruct the exact connectivity and service dependencies that existed at the precise moment alerts fired.

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No Rules Required — Intelligent from Day One

Bespoke tools rely on complex, manually authored rules and thresholds by default: configurations that are brittle, maintenance-heavy, frequently incorrect, and often obsolete before they’re fully deployed. Ciroos Signal Intelligence™ takes a fundamentally different approach.

The Knowledge Graph is not just a connectivity graph — it also includes a causal graph built on metric dependencies learned at runtime in an unsupervised manner for every entity in the tenant's namespace. It encodes temporal causal relationships, establishing clear pathways for investigation while accounting for confounding variables and lagged dependencies. By applying metric analysis in conjunction with the Knowledge Graph, Ciroos Signal Intelligence™ ensures that causally related entities are accurately grouped together, even without predefined rules. For teams requiring finer control, a policy engine allows operators to define investigation scope, apply cost controls, govern schedules, and configure downstream automation. This gives advanced teams the flexibility to tune behavior without forcing every team to start from a complex rule base.

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Key Use Cases

• Alert storm consolidation: Collapse thousands of redundant alerts from disparate sources into deduplicated, correlated signals so that the Reasoning Core (and by extension, operators) can immediately focus on what matters. Ciroos supports 50+ enterprise integrations, allowing it to be connected to tools and receive alerts from these systems even when they cross ownership boundaries within the enterprise.
• Cost-governed investigations: Apply cost controls to set boundaries on autonomous investigation scope, ensuring AI SRE operations run efficiently at enterprise scale, while keeping budget considerations in mind.
• Multi-source alert discrimination: Intelligently distinguish and correlate alerts arriving from different observability tools and environments, applying source-aware logic to provide differential treatment.
• Reduce deployment risk: Define schedules to run specific user-defined Ciroos Behavior Patterns™ before and after deployments, reducing reliability risks during change windows.
• Automated ticket lifecycle: Automatically create tickets in ticketing systems (e.g., ServiceNow, Jira Service Management) following alert analysis — and auto-close them once investigations resolve — eliminating the need for manual intervention.
• Downstream tagging: Set tags for downstream processing, routing, escalation, and analytics pipelines.

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Customer Benefits

The quality of an AI SRE investigation is only as good as the signal it receives. Ciroos Signal Intelligence™ ensures the Reasoning Core operates on the highest-fidelity input possible — with direct impact across investigation quality, efficiency, and outcomes:

• Higher-fidelity investigations: By deduplicating and correlating alerts before they reach the Reasoning Core, Signal Intelligence eliminates the noise that would otherwise dilute investigation focus. The Reasoning Core works from this enriched signal rather than redundant raw events, producing more accurate root cause analysis.
• Faster time to root cause: Correlated signals carry contextual relationships between affected entities. Using the Knowledge Graph and these signals, the Reasoning Core invokes multiple AI agents to investigate them, materially reducing time to identify and confirm root cause.
• Built-in AI FinOps: Ciroos Signal Intelligence™ includes a policy engine that lets operators define investigation boundaries before the Reasoning Core engages, ensuring computational resources are directed at the signals that matter most and keeping autonomous investigation costs predictable at scale.

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Results

In early customer deployments, Ciroos Signal Intelligence™ reduced alert noise by up to 70% on average. Teams that previously spent significant portions of their shift triaging alert storms are now equipped with rich, high-quality root cause analysis in place of manual alert triage. Figure 2 below shows one example of how 10,356 incoming alerts flow into 2,600 investigations, of which 1,040 are prioritized incidents in the selected time period.

Get Started

Ciroos Signal Intelligence™ is available now for enterprise operations teams. Whether you’re running a lean NOC or scaling an AI SRE practice, Ciroos Signal Intelligence™ is designed to deliver immediate value from day one — no configuration marathon required!

To learn more or request a demonstration, visit ciroos.ai.

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