Guides·Guide
Durable execution, queues, autoscaling, state and recovery: the parts you need to run long-running agents for real, and how to get them without building a platform.
TL;DR
Running long-running AI agents in production needs more than a model and a loop: durable execution so work survives crashes, state that persists across sessions, recovery, safe density, and integrations. You either build that architecture or run on a runtime that ships it.
A short-running agent is a request: it runs and finishes, so the architecture is simple. A long-running agent stays alive for hours or days, which means it has to survive crashes, hold state, act on its own, and share hardware with many others. That is a different, harder architecture.
Short-running: sandboxes and workflows (E2B, BrowserUse, Modal)
Stateless. Re-hydrates state, re-auths and reconnects every time. Great for code execution, scraping and batch tasks.
Long-running: persistent agents (OpenClaw, Hermes)
Persistent. An agent that lives, remembers and takes initiative. The only catch is idle cost, which over-provisioning or your own always-on infrastructure removes.
If an agent crashes mid-task, the work should resume, not restart from zero. That is durable execution.
Long-running agents accumulate state, and that state has to be durable and recoverable. In practice that means a versioned filesystem with point-in-time restore, plus a recovery loop: detect the crash, restart the process, recreate the pod if needed, restore a known-good state, and alert only when automation cannot fix it.
01
In-pod restart
A daemon restarts OpenClaw the instant it dies.
02
Pod recreation
If the pod fails, it is recreated with state intact.
03
Known-good restore
Config auto-repair and a versioned restore.
04
Critical alert
Only if all else fails, with a full post-mortem.
Agents mostly wait, so packing many on shared capacity is how the economics work, but it is also how a node runs out of memory and takes everything down. Safe density needs a throttle on startups, real-time memory monitoring, and selective shutdown by priority before the node is overwhelmed. Autoscaling alone does not give you that, the protection has to be agent-aware.
One agent
Easy to babysit.
A fleet, by hand
You can assemble this yourself: a durable execution engine, queues, an autoscaler, a recovery system, a versioned store, an integration layer. That is a platform, and it is months of work plus permanent on-call. The alternative is a runtime that ships the whole architecture. Molted is that runtime for long-running agents (OpenClaw today, Hermes on request): 4-tier self-healing, a RAM semaphore for safe density, a versioned S3-backed filesystem with point-in-time restore, and 1,000+ integrations, managed.
Q.01
It is the set of components that keep an always-on agent alive and correct: durable execution so work resumes after a crash, queues and scheduling for event and heartbeat triggers, persistent versioned state, a recovery loop, and autoscaling with agent-aware safe density. You build it, or run on a runtime that includes it.
Q.02
Durable execution means each step of an agent's work is persisted so that if the process crashes, it resumes from where it stopped instead of restarting. It is what lets a long-running agent run a task over hours or days reliably.
Q.03
Plain autoscaling is not enough because agents over-provision memory. You need agent-aware safe density: throttle startups, monitor real memory use, and selectively stop low-priority agents before a shared node runs out of memory. Molted does this with a RAM semaphore.
Q.04
If running agent infrastructure is your product, build it. If your product is the agents, building durable execution, queues, autoscaling, recovery and integrations is months of undifferentiated work plus on-call. A managed runtime like Molted ships the architecture so you ship agents instead.
Q.05
Yes. Molted is a managed runtime for long-running AI agents with the full architecture built in: 4-tier self-healing, a RAM semaphore for safe density, a versioned S3-backed filesystem with point-in-time restore, queues and scheduling, and 1,000+ integrations, running OpenClaw today and Hermes on request.
Keep reading