An agent checkpoint is a durable snapshot of an AI agent's verified progress and execution state. It records what completed, what remains, relevant tool outputs, and the next safe step, allowing a stopped or failed workflow to resume without repeating every action or trusting a reconstructed conversation.
OpenAI Agents SDK provides the primary reference used for this definition and its production boundaries.
How does agent checkpoint work in production?
A runtime writes checkpoints at known safe boundaries, such as after a database transaction or human approval. A long-running agent resumes from that state, while deterministic replay reconstructs how it arrived there. A checkpoint records confirmed progress, not an optimistic plan.
When does agent checkpoint matter?
Checkpoint before expensive, irreversible, or slow steps. Agent recovery should validate external state before continuing, because another system may have changed. Link the snapshot to its agent session. Resume from evidence, not from a model’s summary of events.
Frequently asked questions
What is agent checkpoint used for?
Checkpoint before expensive, irreversible, or slow steps. Agent recovery should validate external state before continuing, because another system may have changed.
Is an agent checkpoint the same as agent memory?
No. A checkpoint stores execution state; memory stores information intended for later reasoning.
No advertising or tracking cookies, and our visitor counts are anonymous. The Cal.com booking widget loads only if you allow it. Privacy Policy.
The page itself, anything our host sets to serve and secure it, and the anonymous visitor count. Always on, and none of it stores anything on your device.
The Cal.com booking widget. Left off, a booking link opens the booking page instead of a popup, so you can still book a call.