Mastering Durable Workflows with the Microsoft Agent Framework: A Q&A Guide

The Microsoft Agent Framework (MAF) is an open-source, multi-language toolkit for creating and orchestrating AI agents. It recently introduced a workflow programming model that allows you to chain agents and tasks into multi-step pipelines. This Q&A guide explores its core concepts—from executors and pipeline construction to local execution and cloud scalability—helping you build durable, production-ready workflows.

1. What is the Microsoft Agent Framework (MAF) and what does its workflow model offer?

MAF is an open-source framework that supports building, orchestrating, and deploying AI agents in multiple languages. Its workflow model extends the basic agent concept by enabling you to define a series of steps—called executors—that form a directed graph. The framework handles execution order, data transfer between steps, and error propagation automatically. This means you can create complex pipelines without managing low-level state or concurrency. The model supports patterns like sequential chains, parallel fan-out/fan-in, conditional branching, and human-in-the-loop approvals. A lightweight in-process runner lets you test workflows in memory, perfect for development. Later, you can add durability (persistence) to handle long-running processes and failures, or scale out using Azure Functions.

2. What are executors and how do you create one in MAF?

An executor is the fundamental unit of work in a MAF workflow. It receives typed input, processes it, and produces typed output. To create one, you subclass Executor<TInput, TOutput> and override the HandleAsync method. For example, an OrderLookup executor takes an OrderCancelRequest and returns an Order after a simulated database lookup. Similarly, an OrderCancel executor receives an Order and returns it with an updated cancellation flag. Each executor has a name (e.g., "OrderLookup") used to identify it in the workflow graph. The IWorkflowContext parameter provides access to shared state and pipeline services. Executors can be simple, like sending an email, or complex, integrating with external APIs or AI models. This design promotes reusability and testability because each step is a self-contained component.

3. How can you compose multiple executors into a workflow pipeline?

Composition is done using a workflow builder that lets you wire executors into a directed graph. You start by creating a WorkflowBuilder instance, then add steps by calling methods like AddStep or defining transitions. For instance, you might chain an OrderLookup step to an OrderCancel step, then to a SendEmail step. The builder also supports conditional edges using gates or filters. Data flows automatically between steps: the output of one executor becomes the input of the next, as long as types match. You can also fork the flow for parallel execution using Parallel constructs. Once the graph is defined, you call Build() to get a runnable Workflow instance. The framework then orchestrates execution, handling errors and state transitions. This approach makes complex agent interactions visible and maintainable.

4. Which workflow patterns are supported by MAF?

MAF’s workflow model supports several standard patterns out of the box. Sequential chains are the simplest: one executor runs after another. Parallel fan-out/fan-in allows multiple executors to run concurrently, then merge results. Conditional branching enables different paths based on data (e.g., if cancellation succeeds, send email; otherwise, log an error). Human-in-the-loop approvals can be modeled by pausing execution until an external signal resumes it—often used for manual review steps. Additionally, you can implement retry and timeout policies per step. The framework’s durability layer also supports long-running workflows that can survive process restarts. These patterns are combined to create real-world scenarios like order processing, multi-agent research, or customer support automation. By using a declarative graph, you avoid writing complex orchestration logic yourself.

5. How can you run a MAF workflow locally for testing?

MAF provides a lightweight in-process runner that executes workflows entirely in memory. This is ideal for local development and unit testing. You simply create a Workflow instance using the builder, then call RunAsync with an input message. The runner will execute each executor, handle data flow, and return the final output. For example, you can set up a .NET console app, add the Microsoft.Agents.AI and Microsoft.Agents.AI.Workflows NuGet packages, then wire together executors like OrderLookup, OrderCancel, and SendEmail. With the in-process runner, you can debug each step, inspect intermediate results, and verify error handling. This rapid feedback loop speeds up development. Later, when you’re ready for production, you can swap the runner for a durable one that persists state to a store like Cosmos DB, ensuring workflows survive crashes.

6. How can you add durability and scalability to a MAF workflow, such as by hosting in Azure Functions?

Durability is achieved by using a persistent storage backend for workflow state. MAF’s core package includes an in-process runner for testing, but you can integrate with the Durable Workflows extension (not to be confused with Azure Durable Functions) or host the workflow inside Azure Functions. With Azure Functions, each executor step can be a separate function, or the entire workflow can run as an orchestration using the Durable Functions pattern. The framework supports an IWorkflowStore interface for checkpointing progress. When a step completes, its output is saved; if the process crashes, the workflow resumes from the last checkpoint. This enables long-running processes that may take hours or days. For scalability, you can run multiple workflow instances concurrently, each isolated. Hosting in Azure Functions also gives you automatic scaling, monitoring, and integration with other Azure services. This approach transforms a simple prototype into a robust, production-grade agent system.