Glossary
Observability
Being able to answer why a system behaved as it did, after the fact, from what it recorded.
For AI systems this means tracing prompts, retrieved context, tool calls and outputs, enough to replay any decision.
Without it, debugging a wrong answer is guesswork, and an auditor's question has no answer.
For AI systems there is a specific requirement beyond ordinary logging: the ability to reconstruct exactly what the model saw. Prompt, retrieved context, tool results and parameters together determine the output, and a trace missing any of them cannot explain a bad answer.
Sampling is the compromise most systems settle on. Tracing every request in full is expensive at volume, so the usual pattern is to capture complete traces for a small percentage plus every error and every low-confidence response, which is where the diagnostic value is concentrated anyway.
Make a trace reachable from the thing a user complains about. Being able to paste a conversation or order reference and see exactly what the system did is what turns a vague report into a fixed bug.
Related terms, in context
The concepts you almost always meet alongside observability.
- AI governance
- The controls, documentation and evidence that let an AI system pass an audit.
- MLOps
- The practices that keep models deployable, monitorable and reversible in production.
- Evaluation
- A labelled test set that turns 'it seems good' into a number you can regress against.
Where this shows up in our work
Observability is not an abstraction for us. It is a decision we make on live projects. It shows up most directly in ai infrastructure & mlops, ai governance & compliance, where getting it wrong has a cost someone can measure.
If you are evaluating a vendor on this, the useful question is not whether they can define the term. It is what they measure, what they would refuse to do, and what happens in their system when the assumption behind observability stops holding.
Questions
What is Observability?
Being able to answer why a system behaved as it did, after the fact, from what it recorded.
Does Orqent Labs build this?
Yes, AI Infrastructure & MLOps and AI Governance & Compliance. We work across India, covering all 19,238 PIN codes remotely.
Building something that involves observability?
We will tell you honestly whether it is the right approach for your problem.
Or email bd@dtrasglobal.com · call +91 74118 77878