AI observability

AI observability is tracking what an AI system does in production: inputs, outputs, tool calls, cost, latency and quality.

Written by Reflective Data experts · updated Oct 5, 2026

Logs and traces let you debug bad answers, spot rising costs and measure quality over time. Linking them to evaluation results shows whether changes help or hurt.

It is the AI counterpart of monitoring for traditional software.

Agent harness AI evaluations (evals)Model drift

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Retrieval-augmented generation (RAG)

RAG is a technique that retrieves relevant information from your own sources and gives it to…

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Human in the loop

Human in the loop means a person reviews or approves an AI output or action before…

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ChatGPT

ChatGPT is OpenAI conversational AI assistant, built on its GPT family of models.

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AI agent

An AI agent is a system that uses a language model to pursue a goal by…

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Hallucination

A hallucination is when an AI model produces confident but incorrect or invented information.

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AI governance

AI governance is the set of policies, roles and controls that make sure AI is used…

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