Standard observability tools were built for deterministic systems. GenAI applications break that contract — token counts shift, tool call patterns change, completion rates drop — and none of it fires an alert. Here is what OTel GenAI instrumentation gives you today, and where the gaps remain.
DNS lookup latency is invisible to standard OpenTelemetry instrumentation. eBPF-based tracing closes the gap, and it matters more as agents fan out calls across MCP servers.
Causely is now a Cursor plugin. Your coding agent gets causal context from your live environment and can move from emerging causes of reliability risk to code-level fixes in the IDE.
Named root causes are what turn a guessing agent into one you can trust to act without manual review.
AI agents reconstruct environment state from raw telemetry on every reliability query. Causal context eliminates the reconstruction and cuts token use by 60%.
Most AI SRE agents are stuck on Read-Only — not because teams lack trust, but because raw telemetry offers no causal context to act on with confidence.
Launching a new fintech product required certainty across a complex microservices platform. With Causely modeling cause-and-effect relationships across services, Humm Group gained system-level understanding and confidence that critical dependencies behaved correctly during launch.
Reliability is managed in services, but users experience outcomes. In complex, multi-service and AI-driven architectures, systems can look healthy in isolation while end-to-end workflows still fail. Product reliability needs visibility at the level of transactions and flows.
Alerts are signals, not explanations. By explicitly mapping alerts to symptoms and inferred root causes, Causely turns alert noise into a coherent explanation of what is actually happening in the system.
Slow SQL queries degrade UX and reliability. This guide shows how to distill OpenTelemetry DB spans into actionable metrics: build span-derived slow-query dashboards, rank queries by traffic impact, and detect regressions with anomaly baselines, so you fix what matters first. Hands-on lab included.
Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.
Alerts are supposed to start an investigation. Too often, they start translation: what is the system doing right now? That translation slows containment, splinters context, and stretches customer impact.