What is AI observability?

AI observability is the practice of collecting, analyzing, and correlating telemetry across your tech stack to understand how AI systems behave in every environment, including production. It enables real-time visibility into LLMs, AI agents, orchestration layers, and their downstream impact on your application and infrastructure.

AI observability delivers actionable insights that enable developers, SREs, and platform teams to debug, optimize, and improve AI-powered services, ensuring they stay reliable, performant, cost-efficient, and meet quality standards.

Full-stack observability for AI systems is especially critical when working with managed AI platforms like OpenAI, Anthropic, Gemini (Google Cloud), Amazon Bedrock, Azure AI Foundry, and Vertex AI, where model execution happens externally and opaquely, yet directly affects business-critical workflows.

AI observability spans multiple layers of your technology stack – from user-facing applications to infrastructure services powering AI workloads. Each layer introduces unique telemetry, challenges and insights. Here is a breakdown of the essential components.

Application Layer
Where users interact with the AI system.

• Includes chat UIs, feedback widgets, dashboards, and other frontend and backend services.
• Observability priorities: Track user interaction patterns, latency, feedback loops, and UI-triggered anomalies.
• Track user interaction patterns like Thumbs up/down ratio.

Orchestration Layer
Manages LLM calls, tools, and decision logic.

• Includes tools and frameworks like LangChain, Semantic Kernel, and custom pipelines.
• Observability priorities: Trace prompt/response pairs, retries, tool execution timing, and decision branches.
  

Agentic Layer
Handles multi-step reasoning agents and autonomous workflows.

• Manages agents’ thought processes, goals, memory context, intermediate reasoning, and tool usage.
• Observability priorities: Instrumentation for trace reasoning chains, memory tool usage history and calls, memory references, and outcomes.

Model and LLM Layer
Where AI models are executed.

• Includes foundation models (hosted or self-hosted), fine-tuned LLMs, embedding models, and classifiers.
• Observability priorities: Monitor key telemetry including model and token usage, latency, prompt/response pairs, failure modes (timeouts, errors, exception), guardrail and quality metrics (hallucination, accuracy, relevance, grounding, PII leaks etc.).

Semantic Search & Vector Database Layer
Powers Retrieval-Augmented Generation (RAG) and vector-based search.

• Includes RAG components and vector databases (DBs) like Pinecone, Weaviate, FAISS, and embedding models.
• Observability priorities: Track embedding quality, retrieval relevance scores, result set sizes, latency, and tokenization drift.

Infrastructure Layer
Underlying systems powering AI workloads.

• Covers cloud-native infrastructure supporting AI development including compute, network and storage
• Observability priorities: Monitor GPU utilization, memory pressure, network bottlenecks, inference cost breakdowns, and availability of AI infrastructure like serverless LLM endpoints or managed vector stores.

Every step must be traceable and measurable — from a user’s thumbs-down click in the UI, through the agent’s decision graph and prompt processing, to the underlying workloads. This makes end-to-end observability across all layers essential for ensuring reliable AI services.

Improve cost and model control by attributing token usage and consumption to requests, model versions, components, or individual users.

Trace end-to-end workflows from user input to model output to infrastructure execution.

Identify failure modes such as latency spikes, tool failures, hallucinations, and degraded retrieval performance.

Ensure model quality using metrics from user feedback, correctness, or grounding scores.

Meet SLAs and compliance by tracking the performance and reliability of AI workflows.

Modern AI stacks are dynamic, opaque, and costly. AI observability transforms these complex systems into measurable, traceable, and optimizable systems.

Learn more about how Dynatrace combines predictive, causal, and generative AI for observability, security, and business use cases.

Gain actionable insights and full-stack visibility into quality, performance, and cost—from user input through model inference—by correlating logs, traces, metrics, and feedback across all layers of AI observability.

Application Layer
Capture user interactions, feedback events, and request metadata. Instrument UI components and backend services to correlate user sessions with AI behavior.

Orchestration Layer
Trace the full LLM execution path including prompt construction, retries, tool calls, and branching logic. Enrich spans with prompt inputs, outputs, and timing data.

Agentic Layer
Log agent goals, reasoning steps, memory state, tool invocations, and intermediate outputs. Maintain structured traces for multistep autonomous flows.

Model & LLM Layer
Capture raw prompts and completions, model latency, token usage, cost, and failure patterns. Tag traces with model versions and quality metrics.

Semantic Search & Vector Database (DB) Layer
Log embedding generation, query latency, vector match quality, and source document metadata. Monitor semantic drift and retrieval anomalies.

Infrastructure Layer
Collect metrics on resource usage like GPU, memory, storage, and network. Link infrastructure health to AI performance and cost impact.