Expertise & Skills

LLM Observability and Monitoring

At Agentik OS, we build production-grade observability stacks for LLM-powered applications, giving engineering teams the visibility they need to ship reliable AI products with genuine confidence. Our practitioners instrument every layer of your AI pipeline daily using tracing frameworks such as Langfuse, Helicone, Arize Phoenix, and Weights and Biases: prompt construction, retrieval steps, tool calls, chain-of-thought reasoning, and final completions are all captured as structured spans. We design custom evaluation harnesses that score outputs on factual accuracy, hallucination rate, latency, and cost per call, enabling teams to detect quality regressions automatically before they reach users. Across client engagements we have reduced mean-time-to-detect for LLM failures by an average of 70% and cut token spend by 25 to 40% through continuous cost-efficiency monitoring and prompt optimisation. Whether you operate a RAG chatbot, a multi-agent orchestration layer, or a fine-tuned model serving millions of requests per day, we translate raw telemetry into actionable dashboards, automated alerts, and weekly quality reports that keep your AI systems healthy, predictable, and audit-ready in production.