Do more with AI. Keep your institution in control.

AI works within the user’s access

The identity and context of the person using AI remain part of the workflow. As AI interacts with protected Civitas Learning services, authorization can be evaluated against the access available to that user.

This helps keep AI access aligned with the people behind the work.

Separate access to AI from access to student data

Being able to use an AI capability does not automatically grant access to every student or every piece of institutional information.

Civitas Learning applies controls across both AI capabilities and application data, including student-visibility rules that determine which student records a user can access.

Fail safely when access cannot be established

Authorization is designed so unavailable permission information does not automatically become permission to proceed.

This creates a stronger foundation for expanding AI access without treating uncertainty as authorization.

Keep each institution’s environment separate

Civitas Learning maintains institution-specific analytical and application environments, with institutional context carried through protected services.

Your institution’s data stays distinct from other Civitas Learning customers as it moves through the platform.

Give AI defined paths to data, not unrestricted access

AI experiences interact with institutional information through defined application services and tools rather than receiving unrestricted access to underlying systems.

The model can determine which available tool is appropriate for a task, but the tools and data paths available to it are defined by the platform.

Reduce unnecessary information in the analytical layer

Civitas Learning removes selected categories of unstructured free text as data moves into the analytical Lakehouse, reducing the narrative information available to downstream analytical experiences while preserving structured information needed for analysis.

Define what an agent is built to do

Agents begin with a Skill: a reusable set of instructions for a defined job. That definition establishes the work the agent is designed to perform and the tools available to support it.

Agents can then run manually, on a schedule, or through defined triggers.

Keep actions inside protected application paths

Agents don’t receive unrestricted access to the Impact Platform.

They interact through defined tools and protected application services, keeping authentication, authorization, and application controls between an AI decision and the action it requests.

Match automation to the workflow

Different work calls for different levels of human involvement.

Civitas Learning supports agent patterns ranging from automated execution to act-and-notify and human-review workflows, giving teams a framework for designing automation around the needs and consequences of the task.

Create a durable record of every agent run

Agent execution records capture the context and activity associated with a run rather than treating every AI interaction as an isolated response.

Record actions as they happen

Tool activity is persisted as an agent executes, including the arguments provided to a tool and its result.

Recording activity as it happens helps preserve the history of actions already completed even if a later step in the workflow fails.

Connect model activity to real work

Run records connect model interaction with the tools used during execution, creating a more complete technical record of how an AI-powered workflow progressed.

Instead of knowing only what an agent produced, teams can trace the sequence behind the work.

Build predictive intelligence around your institution

Civitas Learning predictive models are built from each institution’s own data rather than training one shared predictive model across customer student populations.

That allows predictions to reflect the patterns, students, and context of the institution using them.

Understand what influences a prediction

A prediction is more useful when teams can understand the factors contributing to it.

Civitas Learning uses model explainability techniques to surface the variables influencing predictive results, helping teams move from a score toward the context behind it.

Keep predictive and generative AI distinct

Civitas Learning maintains distinct architectures for these different forms of intelligence rather than treating every AI workload as the same problem.