AI Observability Maturity Assessment

Fifteen questions across tracing, evaluation, cost, alerting and governance. Find out which of the five maturity stages your AI systems are at, and what to do next.

Start the assessment The five stages

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5 stages, 5 areas

Model monitoring dashboard showing drift and performance metrics

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Assess your AI systems

0 of 15 answered

Tracing

How do you see what happens inside a single request?

Could you reconstruct a specific user session from last month?

How are your AI systems instrumented?

Evaluation

How do you know whether answers are good?

What happens to low-quality answers?

Do model and prompt changes pass an evaluation gate?

Cost

At what level can you see AI spend?

How are cost overruns caught?

Can you see the cost of one request or agent run?

Alerting & incidents

What do you alert on?

Does your incident process cover AI failures?

How long does it take to find why an answer was wrong?

Governance & evidence

Could you show an auditor how a specific AI decision was made?

Are retention and access for AI logs defined?

How are regulations mapped to your AI telemetry?

Answer all 15 questions to see your result.

What You Get

Five stages of AI observability maturity

Each stage adds a question your team can answer from production data.

1. Logging

What did the model say? Prompts and responses end up in application logs, but there is no structured view of behaviour, cost or quality.

2. Monitoring

Is it up, and what does it cost? Dashboards cover latency, errors and spend. You can see that something changed, but not why.

3. Tracing

Which step caused this failure? End-to-end traces let engineers follow a request through models, tools and retrieval to the failing step.

4. Evaluation

Are answers getting worse, and where? Quality is scored on live traffic, with alerts and a review loop that improves the test set.

5. Governance

Can we prove how the system behaved? Records are retained, access-controlled and mapped to your obligations, ready for an auditor.

The assessment

What the assessment covers

Your stage

One of five stages, from logging to governance, with the question each stage lets you answer.

Five area scores

Tracing, evaluation, cost, alerting and incidents, and governance, each scored separately.

Next steps

Concrete actions for your three weakest areas.

A shared language

The same stages used in our AI observability guide, so engineering and risk teams can plan together.

Five minutes

Fifteen multiple-choice questions, no sign-up.

Private

Scored in your browser. Your answers are not sent to Perimattic.

Background

Why maturity matters for production AI

AI systems fail quietly: an answer can be confident and wrong while every dashboard stays green. Each maturity stage closes part of that gap, from knowing a system is up to proving how it behaved. The AI observability guide explains the signals behind each stage.

Regulated teams usually need the governance stage, because the EU AI Act, HIPAA, SOC 2 and DORA all expect records that can be produced on request. See the regulated AI compliance hub for what each framework asks for.

Engineers reviewing an AI build plan on laptops

FAQ

Frequently asked questions

What is AI observability maturity?

It describes how well a team can see, explain and prove what its AI systems do in production. This assessment uses five stages: logging, monitoring, tracing, evaluation and governance. Each stage adds a question you can answer, from “what did the model say?” to “can we prove how the system behaved?”.

How is my stage calculated?

You answer three questions in each of five areas: tracing, evaluation, cost, alerting and incidents, and governance. Each answer scores 1 to 5. Your overall stage comes from the average, and each area gets its own score so you can see where you are ahead or behind.

What is the difference between monitoring and observability?

Monitoring watches known measures such as latency, errors and cost, and alerts when they change. Observability keeps enough detail, including traces, inputs and outputs, retrieved context and quality scores, to explain problems nobody predicted. Most teams reach monitoring first and then add tracing and evaluation.

Which stage do regulated teams need?

Usually governance. Frameworks such as the EU AI Act, HIPAA, SOC 2 and DORA expect records of how systems behaved, retained for a set period and available to auditors. That needs tracing and evaluation underneath, plus defined retention, access control and evidence exports.

Is my data sent anywhere?

No. Your answers are scored in your browser and are not submitted to Perimattic. Standard page analytics record that the assessment was used, without your answers.

Related Tools

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Assessment

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