Agentic workflows¶

Agentic workflows let you create persistent, intelligent pipelines that securely combine AI models, retrieval systems, and structured logic. Unlike jobs, which run once and complete, agentic workflows are long-lived services: they stay live after launch and respond to requests programmatically until you stop them. This makes them ideal for production use cases that require reusable, governed, and auditable AI automation.

Think of agentic workflows as secure, composable AI services. You define how data flows, which models are used, and how agents behave—all without writing orchestration code. Once live, these workflows can be triggered via the OPAQUE SDK or API, returning structured results from a trusted execution environment.

Why use agentic workflows?¶

Many teams want to use AI to interact with sensitive data—summarizing reports, generating decisions, or answering questions from private knowledge bases. But they need more than just a model call. They need:

• Persistent, callable services
• Auditability and policy enforcement
• Safe access to secure data

Agentic workflows are built for these needs.

Use them to:

• Run retrieval-augmented generation (RAG): Combine document search with AI summarization.
• Build governed agents: Encode behavior with prompts and constraints.
• Deploy decision flows: Route and transform inputs to produce structured outcomes.

All of this happens inside attested, policy-controlled environments that log access, enforce guardrails, and preserve privacy—even during inference.

Getting started¶

Before creating your first agentic workflow, finish your local setup by installing Python 3.10 or alter and the OPAQUE Python SDK. Next, ensure you have access to a workspace that supports agentic workflows, then learn how to create, review, and launch your first workflow programmatically with the OPAQUE SDK.

To learn more about how agentic workflows operate within OPAQUE’s confidential computing environment, see the next page on key capabilities.