Audit Logs

Audit Logs are a native, enterprise-grade tracking feature that records every single execution within your workspace. Whenever an application calls a Decision Table, Decision Tree, Scripting Rule, or Flow via our API, the engine captures the exact timestamp, the rule version used, the raw input data, and the final output returned. It serves as your permanent, tamper-proof system of record.

Audit Logs can be toggled on or off directly within the settings of your individual rules. For production environments, keeping Audit Logs active is highly recommended to guarantee complete operational transparency and full accountability.

Audit Logs act as your business record because they save the input payload, output payload, and core metadata. Debug Data go much deeper by capturing the internal mechanics of the execution. It records the evaluation of individual cells, showing exactly why a specific row matched or why a branch failed.

Each entry securely logs the unique execution ID, the rule ID, the specific version, the timestamp of the request, the execution duration in milliseconds, the final status (success or error), and the complete JSON structures for both the input request and the engine's output response.

Yes, this is one of its primary use cases. If a client application receives an unexpected response or an error, you can locate that exact execution in the Audit Logs panel. Inspecting the payload allows you to view the precise values passed to the engine, helping you replicate and fix the issue inside the rule's Test Bench instantly.

Yes. You can download them via Business Intelligence API to periodically extract audit data and pipe it into your own data lakes, SIEM platforms, or business intelligence tools like PowerBI and Tableau.

While Audit Logs are essential for production environments to maintain compliance and debug issues, you might choose to disable them for high-volume, non-critical testing environments to optimize storage. A great practice is to enable them globally for all production workspaces and use shorter retention windows for staging areas.

When paired with our AI Agent rule type, Audit Logs automatically capture the advanced outputs of Explainable AI. This means your logs will not just show a raw text output. Instead, they store the exact confidence score, the warning flags, and the verbatim source fragments used by the AI model to make its decision, creating a highly transparent audit trail for automated AI decisions.

Learn more about AI Agent.

Debug Data are incredibly valuable during development, onboarding, or heavy troubleshooting cycles because they pinpoint logic flaws instantly. However, because they store a detailed breakdown of every single condition, they consume more storage than standard Audit Logs.

We recommend enabling Debug Data in staging environments or activating them temporarily in production only when diagnosing a specific issue.