Kubernetes AI SRE & Observability — Reliability for Production AI and Agents

Because Pod and node health say nothing about the AI behaviour: a Ready Pod can still return wrong RAG citations, time out on long generations, exhaust its KV cache, loop an agent, or burn a GPU at 8% utilisation. Reliability for AI means measuring the application-layer and inference-layer signals — latency distribution (TTFT/TPOT), token throughput, retrieval quality, tool-call success, and GPU/KV efficiency — and alerting on those, not just CPU/memory and restarts.

OpenTelemetry tracing across the full agent/LLM/MCP/tool/retrieval path using GenAI semantic conventions; Prometheus metrics for inference servers (vLLM, KServe) and the NVIDIA GPU Operator/DCGM; Grafana dashboards for TTFT, TPOT, tokens/sec, queue depth, KV-cache utilisation, GPU memory and SM utilisation; and integrations with Datadog, CloudWatch, Azure Monitor, and Google Cloud Monitoring where you already run them.

Yes. We help set SLIs/SLOs that reflect user experience (e.g., p95 TTFT, successful-response rate, retrieval freshness), error budgets, alerting that fires on the right leading indicators, PodDisruptionBudgets and rollout safety for model updates, and blameless-postmortem-ready runbooks for the common AI failure modes.

Yes. Multi-agent systems need action-level auditing — which agent, which tool, which MCP server, which parameters, what cost, and what outcome. We help you capture that as spans and structured events so you can debug loops, runaway cost, and bad tool calls after the fact, and prove what an agent did.

Message us on WhatsApp with your current stack (inference servers, tracing, metrics backend) and what is hurting — slow answers, blind spots, noisy alerts, or an upcoming reliability review. We will join and work it with you, same-day.