Saga Orchestration
SkillCommerce & financeA skill for implementing saga patterns, which coordinate multi-step transactions across microservices without two-phase commit. It helps you design ordered steps, compensation logic for failures, per-step timeouts, and monitoring for stuck sagas.
Then ask your AI: use the Saga Orchestration skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Identify the service boundaries and which service owns each step.
What your AI can do with it
- Produce a saga definition with ordered steps and compensation commands
- Generate orchestrator or choreography implementations for a chosen pattern
- Write idempotent, always-succeeds compensation logic per participant service
- Configure per-step timeouts with deadlines based on SLA requirements
- Set up monitoring for state machine metrics, stuck sagas, and DLQ recovery
- Troubleshoot stuck compensation states, duplicate executions, and lost events
Getting started
- Identify the service boundaries and which service owns each step.
- List the transaction requirements, marking which steps must be atomic and which can be eventual.
- Describe the failure modes for each step, including whether they are transient or permanent and the retry policy.
- Provide the SLA requirements per step so timeouts can be configured.
- State the existing event or messaging infrastructure, such as Kafka, RabbitMQ, or SQS.
What this skill tells your AI
The instructions your AI receives, as published by wshobson/agents in plugins/backend-development/skills/saga-orchestration/SKILL.md and read by Ahel’s review.
Patterns for managing distributed transactions and long-running business processes without two-phase commit.
Inputs and Outputs
What you provide:
- Service boundaries and ownership (which service owns which step)
- Transaction requirements (which steps must be atomic, which can be eventual)
- Failure modes for each step (transient vs. permanent, retry policy)
- SLA requirements per step (informs timeout configuration)
- Existing event/messaging infrastructure (Kafka, RabbitMQ, SQS, etc.)
What this skill produces:
- Saga definition with ordered steps, action commands, and compensation commands
- Orchestrator or choreography implementation for your chosen pattern
- Compensation logic for each participant service (idempotent, always-succeeds)
- Step timeout configuration with per-step deadlines
- Monitoring setup: state machine metrics, stuck saga detection, DLQ recovery
When to Use This Skill
- Coordinating multi-service transactions without distributed locks
- Implementing compensating transactions for partial failures
- Managing long-running business workflows (minutes to hours)
- Handling failures in distributed systems where atomicity is required
- Building order fulfillment, approval, or booking processes
- Replacing fragile two-phase commit with async compensation
Detailed section: Core Concepts
Moved to references/details.md.
Detailed section: Templates
Moved to references/details.md.
Best Practices
Do's
- Make every step idempotent — Commands may be replayed on broker reconnect
- Design compensations carefully — They are the most critical code path
- Use correlation IDs — The
saga_idmust flow through every event and log - Implement per-step timeouts — Never wait indefinitely for a participant reply
- Log state transitions —
saga_id,step_name,old_state → new_stateon every change - Test compensation paths explicitly — Inject failures at each step index in integration tests
Don'ts
- Don't assume instant completion — Sagas are async and may take minutes
- Don't skip compensation testing — The rollback path is the hardest to get right
- Don't couple services directly — Use async messaging, never synchronous calls inside a saga step
- Don't ignore partial failures — A step that partially executed still needs compensation
- Don't use a global timeout — Each step has different latency characteristics
Troubleshooting
Saga stuck in COMPENSATING state
A saga enters compensation but never reaches FAILED. This means a compensation handler is throwing an unhandled exception and never publishing SagaCompensationCompleted. Add dead-letter queue (DLQ) handling to compensation consumers and ensure every compensation action publishes a result event even when the underlying operation was already rolled back.
async def handle_release_reservation(self, command: Dict):
try:
await self.release_reservation(command["original_result"]["reservation_id"])
except ReservationNotFoundError:
pass # Already released — treat as success
# Always publish completion, regardless of outcome
await self.event_publisher.publish("SagaCompensationCompleted", {
"saga_id": command["saga_id"],
"step_name": "reserve_inventory"
})
Duplicate saga executions on restart
If your orchestrator service restarts mid-saga, it may replay events and re-execute already-completed steps. Guard every step action with an idempotency key — see Template 3 above.
Choreography saga losing events
In a choreography-based saga, a downstream service may miss an event if it was offline when published. Use a durable message broker (Kafka with replication, RabbitMQ with persistence) and store the current saga state in a dedicated saga_log table so you can replay from the last known good step.
Timeout firing before a slow-but-valid step completes
A step like create_shipment might take up to 15 minutes during peak load but your global timeout is 5 minutes, causing spurious compensation. Make step timeouts configurable per step type — see references/advanced-patterns.md for the TimeoutSagaOrchestrator implementation and the STEP_TIMEOUTS dict pattern.
Compensation order not matching execution order
When two steps both complete before a failure is detected, compensation must run in strict reverse order or you leave data in an inconsistent state. Verify that _compensate() iterates from current_step - 1 down to 0, and add an integration test that deliberately fails at each step index to confirm correct rollback order.
Advanced Patterns
The references/ directory contains production-grade implementations not needed for most sagas:
references/advanced-patterns.md— FullSagaOrchestratorabstract base class,TimeoutSagaOrchestratorwith per-step deadlines, detailed bank transfer compensating transaction chain, Prometheus instrumentation, stuck saga PromQL alerts, and DLQ recovery worker.
Related Skills
cqrs-implementation— Pair sagas with CQRS for read-model updates after each step completesevent-store-design— Store saga events in an event store for full audit trail and replay capabilityworkflow-orchestration-patterns— Higher-level workflow engines (Temporal, Conductor) that build on saga concepts
Signals
- GitHub stars
- 40k
- Forks
- 4k
- Last commit
- Oct 2026
Questions
- When should I use this skill?
- Use it when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows, building event-driven saga coordinators for travel booking, or debugging stuck saga states in production.
- What does the skill produce?
- It produces a saga definition with ordered steps, action and compensation commands, an orchestrator or choreography implementation, idempotent compensation logic, per-step timeout configuration, and monitoring for stuck sagas and DLQ recovery.
Advanced
- Item type
- skill
- Key
saga-orchestration-wshobson- Source
- github.com/wshobson/agents
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