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.

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.

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.

Field	Type	Content
probability	Number	A 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.
reason	Text	A 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_fragments	Array	The 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.
warnings	Array	Plain 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:

"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:

{
  "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

The threshold you choose depends on your use case and acceptable risk — there is no universal value.