why agents fail in production

Your agent is 45 minutes into a complex research task. It has searched dozens of sources, built up context, and is about to synthesize findings. Then a network blip drops the connection. Or the process runs out of memory. Or a downstream API times out.

With traditional agent architectures, that's 45 minutes of compute and API costs gone. The user has to start over. The agent has no memory of what it accomplished.

This happens constantly in production. Not because your code is bad, but because distributed systems are inherently unreliable. Networks fail. Processes get killed. External services have downtime. The question isn't whether failures will happen, it's what happens when they do.

what durable execution means

Durable execution treats agent state as a first-class concern. After every meaningful step; every tool call, every decision, every state change; the runtime persists a checkpoint. If anything fails, execution resumes from the last checkpoint, not from the beginning.

This is fundamentally different from retry logic. Retries help with transient failures in individual operations. Durable execution handles failures at any point in a multi-step process, preserving all the work that came before.

Think of it like a save system in a video game. You don't lose all your progress when something goes wrong. You pick up where you left off.

how it works

Traditional agent loops run as long-running processes. The agent starts, executes steps in sequence, and maintains state in memory. If the process dies, everything in memory is lost.

Durable execution inverts this model. The runtime is event-driven, not process-driven. Each step is:

• executed: the agent performs one action
• persisted: the result and updated state are saved
• yielded: control returns to the runtime

The next step only begins after the previous step is durably stored. If a failure occurs between steps, the runtime knows exactly where to resume. If a failure occurs during a step, that step is retried with the same inputs.

real-world impact

long-running tasks become reliable. An agent processing a 100-page document can take hours. Without durability, any failure means starting over. With durability, failures cost minutes, not hours.

cost becomes predictable. When failures mean retrying entire workflows, costs spike unpredictably. When failures mean retrying single steps, you pay for work once.

users trust the system. Nothing destroys user confidence faster than losing their work. Durable execution means users can close their browser, come back tomorrow, and find their agent exactly where they left it.

debugging becomes possible. When every step is persisted, you have a complete history of what the agent did. You can replay failures, understand decisions, and fix issues with real data.

built into inference.sh

Durable execution isn't a feature you enable; it's how the runtime works. Every agent on inference.sh automatically gets:

• automatic checkpointing after each step
• transparent resume on any failure
• state persistence across process boundaries
• full execution history for debugging

You write agent logic. The runtime handles durability.

start building durable agents →