# Explainable AI When an **AI Agent** returns a result, it is not always obvious how it got there. A score of 72, a "high" risk tier, a recommended action — these come from the model's reasoning over the input, but none of that reasoning is visible in the output by default. **Explainable AI** surfaces that reasoning as structured data alongside the main result, so you can see not just what the model concluded, but how confident it was and what evidence it used. ## How It Works When the **Explainable AI** toggle is enabled in the sidebar, the platform automatically adds an `explanation` object to every response. You do not define these fields — they are injected by the platform and populated by the model on every execution.
Enabling **Explainable AI** increases the number of tokens the model must generate, which adds latency and cost to each call. Enable it where the reasoning is genuinely useful — not by default on every rule. {% hint style="warning" %} When enabled, the `explanation` fields also appear in the **Output Model**. If you are using the rule output in downstream rules or mapping fields in a Flow, make sure not to remove them from the model — doing so will drop the explanation data from the response. {% endhint %} ### The explanation Object The `explanation` object contains four fields. Together they give you a full picture of how the model arrived at its output — what it was confident about, what it had to infer, and where in the input it found each value.
FieldTypeContent
probabilityNumberA score from 0 to 1 representing overall confidence in the complete output. Close to 1 means everything was clear and explicit. Lower means some fields had to be estimated or inferred.
reasonTextA plain language paragraph walking through how the model arrived at the output — field by field, noting what was stated directly, what was implied, and what had to be read between the lines.
source_fragmentsArrayThe exact phrases or sentences from the input that were used to determine the output values, copied verbatim.
Can be empty if no clear source can be identified.
warningsArrayPlain language warnings about fields the model extracted with less than full confidence — because the input was ambiguous, a figure was an estimate, or a value had to be inferred.
Empty array if everything was determined with full confidence.
#### The example output: ```json "explanation": { "probability": 0.98, "reason": "The NDA submission fails several company standards. Explicitly, the confidentiality period (2 years), notice period (14 days), non-compete duration (24 months), liability cap ($50,000), and return deadline (30 days) were all outside permitted ranges and required correction to the minimum or maximum allowed thresholds...", "source_fragments": [ // were extracted from NDA_Standart.pdf "3 years minimum", "litigation acceptable", "mutually agreed EU member state", "Minimum $100,000", "12 months maximum", "For one-way NDAs, non-solicitation is recommended but not mandatory.", "30 days minimum", "Not permitted — must be excluded", "deadline 14 days maximum" ], "warnings": [ "The acceptance of 'Germany' as governing law assumes it is a mutually agreed EU member state as per section 2.", "Non-solicitation is compliant only because this is a one-way NDA." ] } ``` ## Accessing the Explanation The `explanation` object is part of the standard rule output — nothing special is needed to retrieve it. It sits alongside your defined output fields in the response. When called via the [Rule Solver API](https://docs.decisionrules.io/doc/api/rule-solver-api): ```json { "decision": { ... }, "risk_profile": { ... }, "explanation": { "probability": 0.87, "reason": "...", "source_fragments": [ ... ], "warnings": [ ... ] } } ``` In a Flow, after an AI Agent node, the fields are available as: ``` [node name].output.explanation.probability [node name].output.explanation.reason [node name].output.explanation.source_fragments [node name].output.explanation.warnings ``` ## Using Explainability in Practice The most practical use of the `probability` score is as a routing signal. After an AI Agent node, add a Switch node that checks the score — if it falls below a defined threshold, route the record to a manual review branch. The reviewer then has `reason` and `source_fragments` available to quickly understand what the model found and where, without having to read the original document in full. ``` AI Agent → Switch on explanation.probability < 0.75 → flag for manual review >= 0.75 → continue automated processing ``` {% hint style="info" %} The threshold you choose depends on your use case and acceptable risk — there is no universal value. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.decisionrules.io/doc/rules/ai-agent/explainable-ai.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.