Durable execution for agents that can't afford to forget
Temporal is the orchestration backbone we reach for when an agent has to survive crashes, wait days for a human, and pick up exactly where it left off. We use it to make long-running agentic workflows boringly reliable.
- Crash-proof, resumable workflows
- Built-in retries & timeouts
- Day-long human approval waits
- Replayable, auditable history
Temporal in one paragraph
A durable execution engine that turns ordinary code into fault-tolerant workflows.
Temporal lets you write a long-running process as plain application code — loops, conditionals, waits, retries — and then guarantees that process runs to completion even if the machine running it dies. It does this by persisting every step to an event history and replaying that history to reconstruct state, so a workflow is never "in flight and unrecoverable."
For agentic AI that matters enormously. A real agent doesn't return in 200 milliseconds; it calls a model, waits on a tool, pauses for a human, retries a flaky API, and resumes hours later. Temporal makes that entire arc a single, observable, recoverable unit of work instead of a fragile chain of queue messages and cron jobs nobody can debug.
What Temporal gives an agent architecture
The capabilities that move an agent from a working demo to something we'll put in front of production traffic.
Resumable workflows
A crashed or redeployed worker rehydrates the exact agent state from history — no re-prompting, no duplicated actions, no lost context.
Retries & timeouts
Flaky model and API calls get declarative retry policies and timeouts at the Activity boundary, so transient failures self-heal.
Durable human-in-the-loop
A workflow can pause for a signal — an approval, a correction, an email reply — for days, holding state the whole time at near-zero cost.
Replayable history
Every decision, tool call, and input is recorded. We replay it to debug, to prove what happened, and to feed governance reviews.
Multi-agent coordination
Parent workflows fan out to child workflows, so a planner agent can spawn and supervise worker agents with clean ownership.
Horizontal workers
Stateless workers scale out independently of the workflow state, so throughput grows without rearchitecting the orchestration.
Where Temporal sits in the stack
The reasoning framework decides; Temporal makes sure the decision actually executes.
Workflow
Deterministic orchestration code defines the agent's plan — the steps, branches, waits, and child workflows.
Activities
Every LLM call, tool invocation, and side effect runs here — the retryable, non-deterministic boundary.
Signals & queries
Humans and external systems push approvals or corrections in, and read live workflow state out.
Workers
Stateless processes poll the cluster, execute tasks, and can crash or redeploy without losing a thing.
A backbone, not a lock-in
Temporal is open source under a permissive license, and the SDK runs in Go, Java, TypeScript, Python, and .NET. We write your orchestration as ordinary code in your language of choice — not in a proprietary DSL you can only run on someone else's platform.
Self-hosted in your VPC or running on Temporal Cloud, the workflow definitions are byte-for-byte identical. That means you can start managed and migrate in-house later, or the reverse, without rewriting the agent. We pick Temporal when durability earns its keep — and we'll tell you plainly when a simpler queue or a tool like n8n is the right call instead.
- Open-source server, multi-language SDKs
- Self-hosted or Temporal Cloud, same code
- No proprietary workflow DSL to escape
Temporal vs. a queue + cron stack
Why durable execution is a different category from message delivery.
| Queue + cron | Temporal workflow | |
|---|---|---|
| Worker crash | Task lost or re-run from scratch | Resumes from exact last step |
| Long waits | Polling, dead-letter queues, glue code | Native durable timers, days or months |
| Retries | Hand-rolled per consumer | Declarative policy per Activity |
| Human approval | External state you must track | Built-in signals, state held in workflow |
| Debugging | Scattered logs across services | One replayable event history |
Frequently asked questions
Why not just run agents on a queue and a cron job?
You can — until a worker dies mid-task, a tool call times out, or a workflow needs to wait three days for an approval. Temporal persists the full execution state, so a crashed agent resumes exactly where it left off instead of replaying side effects or losing context. Queues give you delivery; Temporal gives you durability.
Does Temporal replace LangChain, CrewAI, or AutoGen?
No — it sits underneath them. The agent framework decides what to think and which tools to call; Temporal guarantees that the multi-step plan actually runs to completion despite failures. We routinely wrap a CrewAI or LangChain agent loop inside a Temporal workflow so the reasoning is portable and the execution is reliable.
Self-hosted Temporal or Temporal Cloud?
Both are first-class. We deploy the open-source server inside your VPC or air-gapped network when data residency demands it, and use Temporal Cloud when you'd rather not operate the cluster. Your workflow code is identical either way, so the choice stays reversible.
How do you handle non-deterministic LLM calls inside a deterministic workflow?
LLM calls, API requests, and database writes all run as Activities — the non-deterministic, retryable boundary. Workflow code stays deterministic and only orchestrates. That separation is what lets Temporal replay history safely without re-prompting a model or double-charging an action.
Bring a workflow that keeps falling over.
If your agents lose state on deploys, choke on long waits, or can't prove what they did, we'll architect a durable execution layer that fixes it.