Observability
Observability for AI is the ability to understand what an AI system did, why it did it, and at what cost, by inspecting its inputs, outputs, intermediate steps, and metrics.
What is Observability?
AI observability tools capture every prompt, response, tool call, retrieval result, latency, cost, and user feedback signal, and let engineers trace a single user interaction end-to-end. Aggregate dashboards track quality, drift, cost per request, and incident counts. Observability is the precondition for evaluation, debugging, capacity planning, and regulatory evidence.
How does Observability apply to enterprise AI?
Without AI observability, an enterprise cannot answer 'why did the system give this customer that answer?'. That single question is at the heart of EU AI Act technical documentation, GDPR right-of-access, and most internal audit requests.
Related terms
- LLMOps - LLMOps is the subset of MLOps focused on the specific operational concerns of large language models: prompt versioning, evaluation, cost control, and output observability.
- AI Audit Trail - An AI audit trail is the persistent, tamper-evident record of every input, output, tool call, model version, and decision an AI system has made, sufficient to reconstruct any past interaction.
- Model Drift - Model drift is the gradual or sudden degradation of a model's performance in production caused by changes in input data, target distribution, or operating context.
- Evaluation Harness - An evaluation harness is the test framework used to measure an AI system against a fixed set of inputs, expected outputs, and metrics, run on every change.
External references
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