Temporal just raised $300M at a $5B valuation on February 17, 2026, and the numbers explain why: 1.86 trillion executions from AI companies alone, with OpenAI, Replit, and Lovable betting their agent infrastructure on it. The pitch? Workflows that survive server crashes without losing state—no manual retry logic, no panic when processes fail. If your AI agent crashes halfway through two hours of expensive GPU compute, you’re not just losing time. You’re burning money and developer sanity.
What Durable Execution Actually Means
Durable execution is crash-proof execution. It doesn’t prevent crashes—it makes them irrelevant. When a process dies mid-workflow, Temporal reconstructs the complete application state in a new process and resumes from the exact failure point. All variables persist. All API calls are tracked. Your workflow continues like nothing happened.
Traditional distributed systems spend 66% of their code handling failures—retries, exponential backoff, state reconstruction, panic recovery. We’ve all built hacky retry mechanisms and prayed they work. Durable execution eliminates most of that defensive programming. You write business logic as normal code. The platform handles crashes.
Here’s the difference. Your payment workflow runs for 30 minutes across five microservices. Service three crashes at minute 18. Traditional approach: restart everything, hope idempotency saves you, debug state corruption at 3 AM. Durable execution: the workflow resumes at minute 18, service three’s activities retry automatically, payment completes.
Build Your First Crash-Proof Workflow
Let’s build a simple payment workflow in Python. Install Temporal CLI with temporal server start-dev for local development, then pip install temporalio for the SDK.
First, define an activity. Activities handle non-deterministic operations—external API calls, database writes, anything that interacts with the outside world. Temporal retries these automatically on failure.
from temporalio import activity
from dataclasses import dataclass
@dataclass
class PaymentInput:
transaction_id: str
amount: float
@activity.defn
async def process_payment(input: PaymentInput) -> str:
# External API call, database write, etc.
activity.logger.info(f"Processing payment for ${input.amount}")
# Temporal handles retries if this fails
return f"Payment {input.transaction_id} completed"Next, define the workflow. Workflows orchestrate activities and contain your business logic. This code survives crashes—if the process dies, Temporal restarts it from the last recorded step.
from temporalio import workflow
from datetime import timedelta
@workflow.defn
class PaymentWorkflow:
@workflow.run
async def run(self, payment: PaymentInput) -> str:
workflow.logger.info("Starting payment workflow")
result = await workflow.execute_activity(
process_payment,
payment,
start_to_close_timeout=timedelta(minutes=5),
)
return resultFinally, set up a worker to execute your workflows. Workers poll Temporal’s task queues and run your code.
from temporalio.client import Client
from temporalio.worker import Worker
async def main():
client = await Client.connect("localhost:7233")
async with Worker(
client,
task_queue="payment-queue",
workflows=[PaymentWorkflow],
activities=[process_payment],
):
await asyncio.Event().wait()That’s it. Crash your worker mid-workflow. Temporal will restart it and resume from the exact failure point. No state lost. No manual recovery. Check the official Python tutorial for complete setup details.
Why Companies Bet Production Infrastructure on This
Replit migrated Agent 3 to Temporal because, as their engineering team put it, “It’s a pretty bad user experience to have the agent get super far into something and then hit a catastrophic error.” Each Replit Agent runs as a unique Temporal workflow. If the agent crashes during a complex coding task, it resumes from the exact point of failure. No lost work. No frustrated users.
The result? Zero incidents traced to Temporal Cloud. Migration from prototype to production took two weeks. Replit now uses Temporal beyond agents—container builds, infrastructure provisioning, domain lifecycle management.
OpenAI’s Codex web agent runs on Temporal. The key benefit? When agents fail mid-workflow, they resume from the failure point instead of rerunning the entire LLM context. You preserve tokens. You save money. You don’t waste $50 in GPU costs because a network blip killed your agent at step 47 of 50.
Enterprise adoption tells the same story. ADP orchestrates HR processes. Block handles payment transactions. Washington Post runs video scene detection pipelines. Abridge manages medical workflows across 200+ health systems. These aren’t weekend hackathon projects. These are business-critical workflows where failures cost real money.
When You Actually Need Temporal
Use Temporal for long-running workflows (hours to years), critical business processes (payments, compliance, medical workflows), AI pipelines with expensive compute, and microservices orchestration. If your workflow completes in under 30 seconds and never talks to external services, you don’t need Temporal. You need a function.
But if you’re orchestrating AI agents that run for hours, coordinating microservices across payment flows, or managing infrastructure provisioning—this is the infrastructure that prevents your 3 AM pager duty. Why are you still writing retry logic manually in 2026?
Don’t use Temporal for simple CRUD operations, stateless request/response APIs, or basic scheduled batch jobs. For ETL pipelines and scheduled analytics, use Airflow. For simple AWS workflows under one year, use Step Functions. For scheduled tasks with no dependencies, use cron. Temporal is for workflows where state matters and failures are expensive.
The 2026 AI Infrastructure Signal
Temporal’s February 17 Series D funding ($300M at $5B valuation, led by Andreessen Horowitz) came with notable metrics: 380% year-over-year revenue growth, 20+ million installs per month, 9.1 trillion lifetime executions. But the most telling number? 1.86 trillion executions from AI-native companies.
When OpenAI integrated Temporal into their official Agents SDK in February 2026, the market voted. Durable execution isn’t a niche tool anymore. It’s becoming standard infrastructure for production AI agents. As Lightspeed VC put it: “Agents make mistakes. Your workflows can’t.”
Getting Started
Install Temporal locally with temporal server start-dev. The SDK supports Python, TypeScript, Go, and Java. Start with the official tutorials, deploy to Temporal Cloud for production, or self-host. The durable execution guide explains core concepts in depth.
If your workflows are mission-critical and crashes cost real money, this is your infrastructure. If they’re not, save yourself the learning curve.
