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To make an AI workflow recover after a worker restart, use Temporal to coordinate durable steps and run model requests, tool calls, and other external operations in Activities. Keep Workflow code deterministic so Temporal can replay it from recorded results. In .NET, Microsoft.Extensions.AI can provide a common chat-client abstraction; it does not provide durable execution by itself.
What each part does
These technologies address different responsibilities. Microsoft.Extensions.AI provides a common .NET layer for chat and embedding clients, so application code can use supported provider integrations through familiar abstractions. Microsoft recommends starting with it for most app-level AI features. It is not a workflow engine and does not preserve progress through a worker outage. See Microsoft’s .NET AI ecosystem guide and its Microsoft.Extensions.AI overview.
Temporal supplies the durable orchestration layer: Workflows coordinate the process, Activities perform work outside the Workflow, and Workers execute both. Temporal records workflow progress and Activity results so a Workflow can resume after interruptions. That recovery applies to orchestration; it does not make an external provider’s operation exactly-once or make an AI answer correct. The Temporal .NET SDK guide covers these primitives and related topics such as timers, cancellation, timeouts, testing, and error handling.
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Microsoft’s AI ecosystem guidance describes the Microsoft Agent Framework as a place for multi-step agent behavior, routing, and handoffs. That is a different concern from a provider-neutral chat abstraction or a durable execution engine. These pieces are not necessarily mutually exclusive: Temporal can coordinate direct LLM calls or work done through an agent framework, but compatibility depends on the specific SDKs and versions you choose.
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Should an LLM call run in a Workflow or an Activity?
Run it in an Activity. A Temporal Workflow must be deterministic: during replay, it needs to make the same orchestration decisions from the same inputs and recorded results. A fresh model request is external, nondeterministic work; making it from Workflow code could produce a different result on replay. Instead, have the Workflow schedule a model-call Activity and make decisions using the result Temporal records for that Activity. Tool execution, HTTP requests, database operations, and other external I/O belong in Activities for the same reason. See Temporal’s .NET Workflow versioning guidance.
- Workflow: coordinate the sequence, wait for timers or messages, and branch on durable inputs and recorded Activity results.
- Activity: call the configured chat client, execute a tool, access a database, or perform another external operation.
Keep Workflow code away from external I/O, mutable external state, direct system-clock access, and random or otherwise nondeterministic calls. Temporal’s .NET SDK repository also documents .NET-specific cautions around constructs such as Task.Run, Task.Delay, and ConfigureAwait(false) in Workflow code. Check the current SDK guide and package version before relying on low-level details.
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How to structure the .NET implementation
- Define the durable contract. Give each step stable, typed inputs and outputs. Carry the state needed to continue the business process, but keep large documents or binary data in external storage and pass references when appropriate. This is an application-design choice, not a prescribed SDK API.
- Register the AI client outside Workflow logic. Configure an
IChatClientor another supported provider integration through application dependency injection. Keep credentials and provider-specific settings in configuration and service boundaries, not in Workflow code. See Microsoft’s library guidance. - Write deterministic orchestration. Have the Workflow schedule Activities, wait for timers or messages, and choose the next step from inputs and recorded results. Do not call the model or make HTTP or database requests directly from it.
- Put the model request in an Activity. Give the Activity a bounded request and return a structured result the Workflow can use. Put tool effects in their own Activities when separate timeout, retry, audit, or side-effect handling is useful. Exact package calls vary by provider and version; there is no single code sample established here that can be assumed to compile unchanged across integrations.
- Set operation-appropriate timeouts and retry behavior. Reads are often easier to retry safely than writes. For a write, use an idempotency key or downstream deduplication when available. A provider may have completed an operation even if the worker fails before Temporal records the Activity result, so a later attempt can repeat the request. The application must decide how to make that repeat safe.
- Use heartbeats for suitable long-running Activities. When an operation supports progress reporting or resumption, use the SDK’s heartbeat mechanisms. Temporal’s .NET guide documents Activity execution, timeouts, asynchronous completion, and error handling.
- Test interruption and change paths. Exercise worker termination, Activity retries, duplicate downstream requests, cancellation, late user input, and replay of histories created by earlier Workflow code. Temporal’s guide includes testing and debugging resources.
How to handle status, input, and approvals
Temporal has three message types for interacting with a running Workflow. Choose based on whether the caller is reading state, sending information without waiting, or needs an acknowledged write:
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|---|---|---|
| Query | Read current Workflow state, such as progress | Reads state without changing it or blocking for a result |
| Signal | Send asynchronous input, such as a user response or cancellation request | Changes Workflow state without waiting for a result |
| Update | Submit a decision or configuration change that needs acknowledgment | Can wait for completion or receive an error |
Validate incoming values before applying them to Workflow state. Temporal’s message-passing guide explains the distinction between Queries, Signals, and Updates; it describes a Query as a read request that cannot block.
How to evolve Workflow code safely
A Workflow may still be running when you deploy new code. If an orchestration change would cause an existing execution’s history to replay differently, use Temporal’s versioning approach rather than assuming every open execution starts on the new path. Keep nondeterministic external calls in Activities as you make those changes. Follow the current Temporal .NET versioning guide for the SDK’s supported pattern.
For very long-running executions, the .NET guide also lists Continue-As-New as a Workflow capability. Use it when appropriate to start a fresh history while carrying forward the business state needed to continue. The right history and payload strategy depends on the application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate reliability and AI quality
Durable orchestration and answer quality are separate concerns. Test failure recovery and replay behavior for the Workflow; evaluate representative prompts and outputs to catch regressions as models, prompts, and tools change. Microsoft’s .NET AI overview describes evaluations as a way to assess quality over time.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Monitor the signals that help explain a failed or slow run: Activity latency and errors, model-call outcomes, tool results, and retry behavior. Track token or cost data where the chosen provider exposes it. Temporal can restore orchestration progress, but it cannot guarantee that an AI response is accurate, that a provider will be available, or that a repeated side effect is harmless.
How this differs from other .NET options
| Option | Best fit | Key distinction |
|---|---|---|
| Microsoft.Extensions.AI | Common .NET chat and embedding client abstractions | Model and provider integration; not durable execution |
| Microsoft Agent Framework | Multi-step agent behavior, routing, and handoffs | Agent orchestration rather than a provider-neutral chat abstraction |
| Temporal | Durable, long-running application orchestration | Workflow replay is deterministic; external nondeterministic work belongs in Activities |
| Azure Durable Task | Durable workflow and agent programming in Azure’s ecosystem | Another durable execution option; compare language integration, hosting, state management, operations, and portability for the actual deployment |
Microsoft describes Durable Task for AI agents as durable execution infrastructure and distinguishes it from an agent framework. The right choice depends on the deployment’s requirements, not on assuming these options are interchangeable or universally exclusive. Verify current stable package versions, .NET target-framework support, preview labels, and provider compatibility for the exact versions you plan to deploy; the architecture described here is not a tested end-to-end sample.
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