Building replayable, resumable workflows for long-running AI agents: lessons from Pyash
aizvo · reddit · 2026-11-15
The author, developer of Pyash, lays out a sequence for making long-running AI work recoverable: record what each run did, replay it using stable request/turn identities, reconstruct agent sessions, resume queued work, and keep evidence with a bounded retention lifecycle.
Key points:
- Run records make completed work inspectable; replay revisits work via stable identities.
- Session reconstruction restores the relevant agent conversation.
- Resumable queues and timeout recovery handle interrupted workflows.
- Recovery checks verify recorded evidence before work continues.
The core lesson: replaying a run, reconstructing a session, and resuming queued work are related but distinct jobs, and they become easier to reason about when they share stable identities and recorded evidence.
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