Most frontier LLMs degrade badly by ~1,000 tokens of input, not the millions in their spec sheets. For on-call agents, that means accuracy drops exactly as an incident gets complex. The fix isn't a bigger model. It's not handing the LLM the raw data at all.
Getting OpenTelemetry into Java enterprise applications without touching the JVM has been a persistent gap. OBI changes that, and for Causely customers, it unlocks the topology data needed to pinpoint root causes across complex Java microservice architectures.
Causely MCP Skills are live. One master router + six specialist workflows: alert triage, change impact, K8s investigation, postmortems & more. Describe your situation, Skills pick the right tools. No prompt engineering. No orchestration.
Observability semantics fall into six layers, from entity inventory to constraints. Most tooling reaches layer two. This post defines all six precisely.
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.