Observability
Logging, metrics, tracing, monitoring strategy, and the operating discipline that makes enterprise platforms supportable.
AI Observability — What to Log and Why
Conventional application observability misses what matters in LLM systems. A practitioner view of the trace shape that actually lets you debug, audit, and improve a production AI system.
Enterprise Monitoring Platforms
Datadog, Splunk, Dynatrace, New Relic, the Grafana stack, cloud-native observability — the market has split along distinct operating models with materially different cost curves. A practitioner view of how to choose, when to switch, and where the migrations actually break.
Enterprise Logging & Observability
Most enterprise logging architectures were designed for a different era of cost economics. A practitioner view of structured logging, retention tiers, the cost discipline modern observability requires — and why the cheapest log line is often the one not emitted.
API Observability Best Practices
Most API observability tells you that the gateway is up. Useful API observability tells you what consumers actually experience. A practitioner view of the three pillars, the consumer-centric metrics that matter, and the OpenTelemetry adoption that has finally simplified the picture.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.