---
title: "Dapr Workflows"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# [Dapr Workflows](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-overview/)

In the DACA architecture, Dapr Workflow is a crucial building block. It empowers developers to orchestrate complex, AI-driven processes and coordinate multiple DACA agents effectively. Dapr Workflow is designed for building and managing stateful, long-running, and fault-tolerant applications, which are essential for sophisticated, production-grade AI systems. It is particularly well-suited for orchestrating complex interactions between ai-apps and other Dapr building blocks like service invocation, pub/sub, state management, and bindings.

![Dapr Workflow](./public/hello-workflow.png)

With Dapr Workflow, you can define business logic and integration processes that are resilient to failures and can run for extended periods.

![Dapr Engine](./public/Workflow%20Engine%20.png)

By default, Dapr Workflow supports the Actors backend, which is stable and scalable. However, you can choose a different backend supported in Dapr Workflow. The backend implementation is largely decoupled from the workflow core engine or the programming model that you see. It primarily impacts:

- How workflow state is stored
- How workflow execution is coordinated across replicas

We will study details in workflows architecture.

![Dapr Engine Actors](./public/Workflow%20Engine%20Actors.png)

## Module Structure & Learning Path

This module is designed to guide you through Dapr Workflows, from foundational concepts to advanced DACA-specific implementations. Each subdirectory focuses on a specific aspect:

*   **`00_lab_starter_code/`**: Contains starter code, templates, and common utilities for the hands-on labs in this module.
*   **`01_hello_workflow/`**: Your first step into Dapr Workflows. Learn to set up, define, register, and run a basic workflow.
*   **`02_architecture_theory/`**: Delve into the underlying architecture of Dapr Workflows, understanding how the Dapr sidecar, workflow engine, and SDKs interact.
*   **`03_author_workflows/`**: Focus on the practical aspects of writing workflow and activity logic using the Dapr Workflow SDK, including data passing, timers, and child workflows.
*   **`04_manage_workflows/`**: Learn how to interact with and manage workflow instances using the Dapr Workflow HTTP API (start, query, pause, resume, terminate, raise events, purge).
*   **`05_patterns/`**: Explore common and effective workflow design patterns (e.g., chaining, fan-out/fan-in, sagas, human interaction) and how to apply them.
*   **`06_ai_pizza_shop/`**: A challenge to apply Dapr Workflows to build an AI-driven application (the "AI Pizza Shop"), integrating various concepts learned.

Follow these sections sequentially to build a comprehensive understanding of Dapr Workflows and their application in the Dapr Agentic Cloud Ascent (DACA) architecture.

## Extra Reading
- [Workflow Overview](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-overview/)
- [Workflow Patterns](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-patterns/)
- [Workflow Architecture](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-architecture/)
- [Agentic Patterns Implementations Code Inspiration](https://github.com/dapr/python-sdk/tree/main/examples/workflow)
- https://github.com/dapr/python-sdk/tree/main/examples/demo_workflow
- https://github.com/dapr/python-sdk/tree/main/ext/dapr-ext-workflow
- https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-overview/
-https://docs.dapr.io/reference/api/workflow_api/
