AI ObservabilityEnd-to-end observability for your Agentic AI and LLM workloads.
AppBusinesses are adopting Agentic AI, GenAI services and apps at scale — using LLMs for autonomous agents, RAG pipelines, semantic search, and complex multi-step orchestration across dozens of providers and frameworks. Managing the performance, cost, quality, and reliability of these workloads requires purpose-built observability that understands AI-specific signals.
Dynatrace AI Observability provides a unified view across every layer of your whole AI stack: model and provider calls, orchestration frameworks, vector databases, agent topology, and infrastructure to coding agents. It ingests gen_ai.* telemetry from any source — Dynatrace OneAgent (zero-code, automatic), OpenTelemetry (native OTLP), OpenInference or OpenLLMetry and surfaces it in a single, consistent experience without requiring you to change your instrumentation approach
Beyond traces and metrics, Dynatrace AI Observability includes agent topology mapping via Smartscape, showing how agents call each other and which models and tools they depend on in real time. It also supports production AI quality evaluation through dt-evals, enabling LLM-as-judge scoring of live responses so quality regressions are caught alongside performance and cost issues. Set SLOs and get alerts the moment latency, error rate, or cost budgets are breached
Setting up full stack observability for your GenAI applications is possible with OpenTelemetry, or auto-instrumentation libraries like Traceloop's OpenLLMetry, with OpenTelemetry under the hood, which can seamlessly provide comprehensive end-to-end insights into your production environments.
To set up OpenLLMetry with Dynatrace, see Dynatrace Documentation.
You can access all these capabilities through customizable, ready-made dashboards tailored to your needs.
