How Syncause Makes RCA AI Agents Precise, Not Guesswork

The value of AI extends beyond merely "helping analyze trace data." Instead, it fundamentally redefines the relationship between humans and observability tools. AI can actively reason, connect disparate clues from multiple sources, and liberate engineers from fragmented data, providing conversational analysis and actionable insights. This conviction drives our belief that AI Agents will reshape the future of observability.

From Naive Approaches to Engineering Excellence

Achieving these capabilities required significant technical evolution. Initially, we took what seemed like the straightforward approach: feeding all metrics, logs, and traces directly into a large language model, hoping it would magically determine root causes. For straightforward problems like applications generating HTTP 500 errors, the LLM performed surprisingly well. It could analyze traces and logs, identify stack traces, and suggest reasonable solutions.

However, complex issues exposed significant limitations. Consider a misconfigured application causing latency spikes in upstream or downstream services. The model struggled with massive data volumes, leading to unacceptable inference delays, context window exhaustion, and hallucinations that generated numerous "possible" causes that proved largely irrelevant. Raw observability data, we learned, contains too much noise and ambiguity for effective standalone analysis.

Building a Unified Data Foundation

Our current approach begins with integrating existing observability data sources, capturing comprehensive monitoring across metrics, logs, traces, and events through a unified data plane. This foundation provides the breadth of information necessary for thorough incident analysis.

However, simply querying and correlating metrics, logs, and traces creates a critical problem: large language models tend to identify any anomaly during incident timeframes as potential root causes. For instance, if CPU metrics spike on a particular host during an incident, the model might classify this as a contributing factor when it's actually a symptom rather than a cause. This occurs because AI systems, lacking robust causal indicators, rely primarily on pattern matching and correlation guessing rather than establishing standardized, reliable diagnostic pathways.

Introducing Causal Precision with eBPF

To address this fundamental challenge, Syncause leverages eBPF technology to capture system call data during program execution. These statistical indicators decompose each program's problems into specific directions: excessive CPU consumption, high network latency, or elevated disk I/O latency. This approach not only guides the large language model away from hallucinations and speculation but also accelerates root cause analysis.

We've abandoned the concept of treating LLMs as magic answer boxes. Instead, we integrate them into structured troubleshooting workflows, combining standardized debugging expertise with enriched, low-level signals. Here's our systematic approach to performance problems like slow service latency:

1. First, we analyze request latency metrics using anomaly detection algorithms to identify services exhibiting unusual behavior.
2. Next, we apply causal relationship algorithms to understand latency spike origins, utilizing topology data to eliminate unrelated applications.
3. We then incorporate eBPF data to determine whether latency issues originate from the service itself through execution time problems or downstream dependencies.
4. Typically, applications with degraded execution time represent the primary culprits. eBPF data reveals whether problems stem from intensive CPU usage, insufficient CPU resources, I/O bottlenecks, or other factors.
5. For identified problematic applications, we examine their logs, traces, and metrics to pinpoint root causes.

This methodology ensures the LLM follows clear, repeatable procedures using precise signals to identify root causes efficiently. Rather than operating as a mysterious black box, the system functions as a logical, step-by-step diagnostic process that maintains transparency while delivering rapid results.

Transforming Incident Response

Through this engineering approach, Syncause transforms incident response from reactive fire-fighting into proactive, structured problem-solving. By combining AI reasoning with systematic observability data and causal precision, we're building the foundation for next-generation incident management that scales with modern system complexity while maintaining the speed and accuracy that critical operations demand.

The future of observability lies not in replacing human expertise but in augmenting it with intelligent systems that can process vast amounts of data, identify patterns, and provide actionable insights during the most critical moments.

Syncause represents our vision of this future: where AI Agents serve as intelligent partners in maintaining system reliability, enabling teams to focus on strategic decisions rather than getting lost in data correlation and manual troubleshooting processes.