Modern systems emit telemetry across many platforms logs, metrics, traces, and LLM observability. Correlating a CPU spike in Prometheus with an error burst in Splunk and a latency anomaly in a third platform usually depends on hardcoded threshold rules and a human stitching signals together. That approach is brittle, slow to adapt, and likely to miss novel failure patterns.
The Observability Agent continuously pulls telemetry from Splunk and system sources on a schedule, then uses Claude to analyze it scoring system health, classifying anomalies by severity, and generating on-call summaries. It combines deterministic health rules with AI-driven interpretation to surface issues and grade their urgency.
Autonomous, multi-source incident detection and structured reporting without threshold rules or manual stitching.
Claude autonomously decides which of the six connected collectors to query on each cycle, what queries to run, and how to interpret the results adapting the investigation to what the signals actually show.
Collects data from Splunk index health, local system metrics, and AI-agent telemetry to provide a unified view of overall system health.
Generates structured incident reports at four severity levels. Reports are produced in both JSON (machine-readable for ticketing systems) and Markdown (human-readable for on-call teams).
Streams events to a real-time FastAPI dashboard with an incident browser and source health view, so on-call teams have a live picture of what the agent is seeing.
Skips unconfigured data sources and reasons around partial failures rather than crashing. The agent keeps working even when not every collector is available.
Uses prompt caching across high-frequency polling cycles to keep inference costs controlled as the polling interval decreases.
The Observability Agent is designed for the class of incidents that fixed thresholds consistently miss.
Catches cross-system incidents automatically
A Prometheus CPU spike plus a Splunk error burst plus a Langfuse latency anomaly adds up to an incident that no single-platform rule would catch. Claude correlates them.
Threshold rules need updating every time the system changes. Claude-based reasoning adapts to new patterns without requiring a rules update.
Adding a new data source means implementing the common collector interface. The core reasoning and reporting pipeline does not change.
JSON reports slot into ticketing and alerting systems. Markdown reports go straight to your on-call channel. Both formats are produced on every incident.
The Observability Agent brings AI reasoning to cross-platform incident detection, surfacing the anomalies that threshold rules miss.
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The Observability Agent is an AI-powered monitoring application built by bitsIO on the Claude Agent Platform. It uses Claude to autonomously query multiple data sources, correlate signals across platforms, and generate structured P1 to P4 incident reports.
The Observability Agent currently supports six collectors: Splunk, Splunk Observability (Olly), AWS CloudWatch, Prometheus, Elasticsearch, and Langfuse. All six sit behind a common interface, making the addition of future sources straightforward.
Traditional alerting fires when a specific metric crosses a specific threshold. The Observability Agent uses Claude to reason about signals from multiple platforms simultaneously, correlating anomalies that no individual rule would connect. It is better suited to novel failure patterns and cross-system incidents.
Incidents are classified at four severity levels. P1 is the most critical, indicating a severe cross-platform issue requiring immediate attention. P4 is an informational anomaly that warrants monitoring but not immediate action. The agent determines the severity based on the breadth and depth of the anomalies it correlates.
The Observability Agent degrades gracefully. It skips unconfigured or unavailable sources and continues reasoning with the sources that are available. It does not crash or produce false positives based on missing data.
Yes. Langfuse is one of the six supported collectors. This means the agent can include LLM call latency, error rates, and usage anomalies in its cross-platform correlation useful for teams running AI-powered applications in production.
The polling interval is configurable. Higher-frequency cycles increase cost but reduce time to detection. The agent uses prompt caching to keep inference costs controlled as the polling interval shortens.
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