Observability
There are two kinds of production systems: the ones you can interrogate, and the ones you can only restart. Observability is the difference — and it's never the dashboards you bought, it's the instrumentation discipline underneath: traces that follow a request across every hop, metrics that map to what users feel, and logs that answer questions instead of filling disks.
We build that discipline with the open standard stack — OpenTelemetry, Prometheus, Grafana — and extend it to the new frontier: agent and LLM systems, where a single user request fans out into model calls, tool invocations, and retrievals that traditional APM renders as one opaque blob. Token costs, latency per step, retrieval quality, tool failure rates — traced, graphed, and alerted, so "the agent seems slow" becomes a specific span with a specific fix.
OpenTelemetry instrumentation
Distributed tracing across services, queues, and model calls — one request, one trace, end to end.
Metrics and dashboards
Prometheus and Grafana tuned to user-felt signals, not vanity graphs.
LLM and agent tracing
Spans for every model call, tool use, and retrieval — with token cost as a first-class metric.
SLOs and alerting
Error budgets and alerts that page on symptoms, sparing your on-call from noise.
Telemetry cost control
Sampling and retention strategies — visibility without a datadog-sized invoice.
We have dashboards nobody looks at and alerts everybody mutes. Now what?
Start over from questions, not tools: what breaks, what does it look like when it breaks, who needs to know. SLOs on user-facing behavior, a handful of symptom-based alerts, and dashboards built for incidents rather than decoration. Less telemetry, more answers.
How is observability different for AI systems?
The failure modes are quieter. A traditional service fails loudly with a 500; an agent fails politely with a wrong answer at normal latency. You need quality signals — eval scores on sampled traffic, retrieval relevance, tool success rates — alongside the golden signals, or production degrades silently.
Can you keep our observability bill from exceeding our infrastructure bill?
Yes — that inversion is common and self-inflicted. Head-and-tail sampling, log level discipline, metric cardinality budgets, and tiered retention typically cut telemetry spend by half or more while improving signal. Observability should cost noticeable money; it shouldn't cost Datadog-IPO money.
Put Observability to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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