sagas-garcia-molina-salem-1987

What problem does it solve?

Prevents multi-step agent and distributed workflows from leaving systems in inconsistent partial states by providing a compensation-based pattern and recovery taxonomy. It addresses the core risk of long-lived operations: resource blocking, irrecoverable side effects, and the exponential increase in deadlock and failure exposure as task scope grows.

Core Features & Use Cases

• Compensation design: How to specify compensating actions for each step so partial executions can be semantically undone.
• Recovery policies: Decision frameworks for forward (checkpoint-and-retry), backward (compensate), and mixed recovery, plus save-point placement guidelines.
• State and schema guidance: "Funds in transit" principle — externalize intermediate state so workflows are observable, recoverable, and auditable.
• Orchestration pattern: Saga daemon pattern and instrumentation rules for wrapping skills so orchestration can drive recovery without native support from underlying systems.
• Use cases: Agent orchestration, multi-step payments and reservations, staged code integration, large-data transformations, and scheduled batch jobs that must be resilient to crashes.

Quick Start

Ask the assistant to decompose your multi-step task into discrete saga steps, list a compensating action for each step, and recommend save-points and in-transit state representations.