AI observability
Definition
AI observability is the practice of recording what an AI system does on every run, including each model call, tool call, cost and time taken, so you can see why it behaved the way it did. It is how teams debug and monitor AI once it runs on real work.
By Ronan Liedmeier, fractional head of growth and course author · Published 17 October 2026 · Last updated 17 October 2026
Also called: LLM observability, AI monitoring
An AI workflow can hand back a plausible answer while doing something wasteful or wrong along the way, and the final output won't show it. Observability makes those steps visible.
How it works
Each run is saved as a trace: a timeline of steps, each holding its input, output, errors, duration and cost. Dashboards built on top show totals and trends, such as average cost per run or how often a tool fails. The chart can show that cost per run jumped on Tuesday, and one trace from Tuesday shows which step caused it.
Look at cost per run as well as the monthly total, because a handful of runaway runs can cost many times more than a typical one. Traces also contain whatever went into the prompts, often customer data, so they need the same access rules and retention period as any other stored data.
Example
A marketing agency runs an AI agent that writes a weekly ad performance summary for each account. The summaries look fine, but the monthly model bill doubled. Reading three traces shows the agent retrying a failing reporting API call up to ten times per run, and none of the retries were logged as errors.
Common mistake
Logging only summaries or final answers. When a run goes wrong, you can't tell whether the tool returned bad data or the model misread good data. Store the raw tool results, and give every step of a run the same ID so the steps can be stitched back into one timeline.
Learn it in AI Academy
- Observability & TracingExpert · 8 min
Related terms
- Trace
- A trace is the step-by-step record of a single run of an AI workflow or agent, showing every model call and tool call in order with its inputs, outputs, errors and timing. Reading one shows exactly where a run went wrong.
- AI eval (evaluation)
- An AI eval (evaluation) is a repeatable test that checks whether an AI prompt or workflow gives good answers, by running it on a fixed set of inputs and scoring the outputs against agreed criteria. It turns "seems fine" into a score you can compare.
- Latency (AI response time)
- Latency is the time an AI system takes to respond, from the moment a request is sent until the answer, or its first words, arrive. In agent workflows it adds up across every model call and tool call in a run.
- AI agent
- An AI agent is an AI system that works toward a goal by choosing its own next steps: it picks a tool, uses it, looks at the result and repeats until the task is done. You set the goal and the limits, and the agent works out the path.