Explore the latest technology trends, best practices, and industry insights to fuel your technical growth journey.
Syncause’s LLM-powered RCA agent achieved 96.67% accuracy on industry-standard benchmarks — surpassing academic models by over 20%. It finds real faults in minutes, no training required.
Root cause analysis has always been the hardest part of incident response. Traditional observability tools often drown engineers in data without clear direction. Syncause combines AI reasoning with eBPF-powered causal signals to cut through the noise, helping teams restore services faster and with greater confidence.
Syncause’s LLM-powered RCA agent achieved 96.67% accuracy on industry-standard benchmarks — surpassing academic models by over 20%. It finds real faults in minutes, no training required.
Modern IT operations have never been more complex. Microservices, cloud-native architectures, and distributed teams have pushed systems to a level of scale and interdependence that even the most experienced SREs struggle to manage. In this landscape, the idea of letting machines help us run machines has always been irresistible.
In the world of AI-driven software engineering, product differentiation often emerges from the problems being solved rather than the technologies being used. This divergence is evident in both the AI coding and SRE domains.
Over the past two years, large language models (LLMs) have begun to land in the observability space. Projects like ITBench SRE Agent and OpenDerisk are exploring automated Root Cause Analysis (RCA): feeding metrics, traces, and logs from distributed systems into a model that infers “which host, which service, which call chain” is most likely the root cause.
Root cause analysis has always been the hardest part of incident response. Traditional observability tools often drown engineers in data without clear direction. Syncause combines AI reasoning with eBPF-powered causal signals to cut through the noise, helping teams restore services faster and with greater confidence.
The AI Agent Market will expand to USD 42.7 billion by 2030 according to the latest research by MarkNtel Advisors. AI Agents are widely predicted to be the next big wave, and we’re already seeing them applied in Observability and DevOps—especially for incident management and root cause analysis.
Observability was meant to empower engineering teams with clarity and speed. Yet in 2025, many organizations find themselves drowning in tools, overwhelmed by noise, and facing rising costs.
Built on cutting-edge eBPF technology, our AI agent dives straight into the kernel to capture thread-level interactions with system resources. No more guessing—we reconstruct the crime scene with precision, using expert rules and proven algorithms to bridge the "last mile" of root cause analysis.
