Astra

What is Astra?

Astra helps you build AI agentic workflows with a data-isolation approach—agents are designed to never see your sensitive data.

AI agent creation with a data-isolation approach, designed so the agent “never see[s] your data.”
Workflow-focused agent building, intended for users who want automated reasoning or actions without exposing sensitive inputs.
Clear privacy framing at the product level, signaling that data access is a primary constraint in how the agents operate.

Define the task your AI agent should handle (e.g., an information or action workflow) while identifying what data the agent should not receive.
Create/configure the agent in Astra according to your desired workflow.
Run the agent for the defined task, keeping your sensitive data outside the agent’s view as intended by the product design.

Building internal automation where the agent needs to perform steps in a process, but should not be given access to confidential datasets.
Prototyping or deploying agent-driven assistants for operational tasks while ensuring private records remain off-limits to the agent.
Running agent workflows in environments where data segregation is required (for example, when inputs are sensitive and should not be exposed to model-driven components).
Creating reusable agent configurations for teams that need consistent behavior without repeatedly re-sharing restricted data.

What does “never see your data” mean? It indicates that Astra is designed so the AI agents built with it do not have access to your data.
Who is Astra for? People building AI agent workflows who want to avoid exposing sensitive information to the agent.
Can Astra protect data once the agent is running? The source states the privacy goal at the product level (“never see your data”), but it does not describe specific enforcement mechanisms or runtime guarantees.
What kinds of tasks can Astra agents handle? The source is not specific about task categories. In general, it’s for workflows where you want agent behavior without granting access to your data.

General-purpose agent frameworks with custom data handling: Tooling like orchestration frameworks where you manage what the model can access yourself; the difference is that you may need to implement or verify isolation behavior.
AI agent platforms focused on privacy controls: Platforms that position privacy and data governance as a selling point; the difference is how explicitly they document and implement access controls.
Traditional automation (workflows/RPA): If you only need deterministic steps rather than LLM-driven reasoning, workflow automation can avoid data exposure by design; the difference is reduced natural-language or adaptive behavior.