Series: AI Systems at Scale · Part 3 of 5
Why Temporal is the Best AI Workflow Orchestrator (and How to Use It)
Temporal gives AI applications durable execution, automatic retries, and observable state. Learn how I use Temporal for multi-step LLM workflows, email automation, and BYOK AI SaaS.
The Problem with Naive AI Pipelines
Most AI applications start as a simple API call: user sends message → call Claude API → return response. This works fine until:
- The LLM API times out mid-workflow
- A downstream service (email, database) fails
- You need to pause and wait for human approval
- You want to retry with exponential backoff
- You need to track what happened to every request
Temporal solves all of these with durable execution — your workflow continues from where it left off, even after server restarts or partial failures.
A Real Example: Email Support Workflow
import { proxyActivities, sleep } from '@temporalio/workflow'const { classifyEmail, generateDraft, sendForApproval, sendEmail } = proxyActivities({ startToCloseTimeout: '5 minutes' })
export async function emailSupportWorkflow(emailId: string): Promise
if (classification.requiresHuman) { const draft = await generateDraft(emailId, classification) const approved = await sendForApproval(emailId, draft)
if (!approved.approved) return await sendEmail(emailId, approved.finalContent) } else { const draft = await generateDraft(emailId, classification) await sendEmail(emailId, draft.content) } } ```
Why This is Better Than Celery/Bull
| Feature | Celery/Bull | Temporal |
|---|---|---|
| Durable execution | No | Yes |
| Resume after restart | No | Yes |
| Built-in retry logic | Basic | Advanced |
| Human-in-the-loop | Complex | Built-in |
| Observability | External tools | Built-in UI |
| Multi-step state | Redis hacks | Native |
Integration with LangFuse
Every activity in our Temporal workflows traces to LangFuse for cost and quality tracking:
export async function generateDraft(emailId: string, classification: Classification) {
const trace = langfuse.trace({ name: 'generate-draft', userId: emailId })const response = await anthropic.messages.create({ model: 'claude-sonnet-4-6', messages: [{ role: 'user', content: buildPrompt(classification) }], })
trace.generation({ output: response.content[0].text, usage: response.usage }) return { content: response.content[0].text } } ```