Support for DMN standards including import/export. - Allows for skills interchange as well as maintaining central decision assets in a standard format.

Non-programmer decision authoring – allows for faster change and upskilling

Decision table support – This is the most central artifact in business rules authoring, robust support for tables is foundational

Decision Lifecycle management – Ability to version, branch and manage decisions for releases

Deployment pipeline management – Functionality that facilitates and automates deployments of decisions to be consumed as a service or api

Scenario-based testing against defined expected results – this is similar to unit testing for code but tests logic in decisions against a set of expected results.

Tracing of execution – giving the ability to inspect after execution what decisions were made

Visual version comparison tools – Tools that allow for rapid change management of decision artifacts from the requirement to implementation components

Low Code object models – Easily update data models used by decisions without major code updates & deployments

Advanced/Large Decision table support – Ability to edit large and complex decision tables

Decision Catalog – Ability to manage shared decision assets across the enterprise

Full DMN model – Able to represent decisions in a model with shared decisions across multiple decision services

Allow traceability to original source – support for hierarchical knowledge sources that map decisions to original sources of logic e.g. regulations, machine learning models, etc. This enables fast impact analysis.

Requirements lifecycle management – ability to manage requirements lifecycle separately to the implementation components lifecycle, this facilitates collaboration across stakeholders.

Containerized API-based deployment model – allow for the deployment of individual decision APIs

Hybrid cloud-ready – Support for deployment on all major cloud and virtualization platforms and ease of relocation from one platform to another

Simulation – Able to run champion challenger simulations by varying the deployed decisions and running representative data sets against the decisions at scale.

Analyst-driven testing – Ease of use for business-driven testing rather than programmatic test cases

Explainer XAI Support – ability to receive explainable artificial intelligence data and incorporate that into the decision-making explanations

Connect to models – ability to easily connect to versions of model APIs that have been deployed to a serving environment

Access feature farms – ability to access streaming grid data that allows for up to data variables to be used with decisions

Grid ready - Ability to deploy natively to grid compute like spark and have a licensing model that supports bringing the execution to the data.