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Make a tool call a durable, identifiable operation before you execute it: write the planner’s relevant state and a pending outbox record in one database transaction, then have a worker deliver that record using the same stable operation ID on every retry. This prevents a lost response or a worker restart from creating a second logical intent. It does not, by itself, guarantee that an external tool runs only once; the receiving service must also support idempotency, deduplication, or a way to reconcile uncertain outcomes.
Why a planner retry can duplicate a tool call
A timeout tells the planner that it did not receive a timely response; it does not establish whether the tool completed its work. The request may have committed and its success response may have been lost. If the planner then submits the action as a new operation, it can repeat business logic: charge twice, create a second record, or send another notification.
Google Cloud’s guidance on transaction retries describes this unknown-commit problem: retrying the same transaction after a network timeout without deduplication can execute its business logic twice. The right response is not to assume every retry is unsafe, but to make a retry refer to the same logical operation and resolve its existing status.
What the transactional outbox does—and does not do
The outbox solves a local dual-write problem. Without it, an application can commit its database update and fail before publishing the corresponding event, or publish an event for a database update that later rolls back. Instead, store the relevant local state and a record of the action to perform in the same transaction. A relay processes only committed records, so a rollback leaves no executable intent and a process crash after commit does not erase the pending work.
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The relay commonly provides at-least-once delivery: it may deliver a record again after a crash or an ambiguous acknowledgment. The outbox therefore preserves intent, but cannot make an arbitrary external service participate in the database transaction. If the worker calls a tool and crashes before saving the success status, it may call the tool again after restart. Downstream idempotency or reconciliation is what makes that repeat safe.
Build a stable operation identity into the write path
- Choose the identity for the logical action. Use an idempotency key supplied with the original request when available, or assign an operation ID once and persist it before retries can generate another one. Keep it stable across transport retries of the same requested action. Do not use a fresh ID for every planner attempt.
- Write state and intent atomically. In one local database transaction, save the planner state needed to recover and an outbox row with the operation ID, tool or action name, validated arguments or a safe reference to them, creation time, and pending status. Put a uniqueness constraint or equivalent conditional write on the identity so concurrent or repeated submissions resolve to the existing operation rather than creating duplicate intents.
- Relay committed rows. A worker claims pending records, records enough attempt and outcome information to recover after restart, and delivers them. Preserve ordering by aggregate or workflow sequence only where the domain requires it; AWS notes ordering can matter in event-sourcing use cases.
- Carry the same identity to the receiving boundary. If the tool or API accepts an idempotency key, pass the outbox operation ID. Otherwise, deduplicate at the receiving application where possible, or define how to reconcile an uncertain result before another attempt.
- Return the operation, not a new action, on planner retry. Look up the durable record by the stable identity and return its current status or stored result. Represent pending, completed, and outcome unknown as distinct states rather than mapping a timeout directly to failure.
The identity format and row schema are implementation choices, not a planner-specific standard established by the cited guidance. The important properties are stable identity, atomic persistence with relevant local state, and enforcement of uniqueness at the point where duplicate intent would otherwise be created.
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What to do at each failure point
- The database transaction fails: neither the planner state nor the outbox intent commits, so no relay should act on that intent.
- The commit succeeds but its acknowledgment is lost: the planner retries with the same identity. The uniqueness rule finds the existing operation instead of inserting another logical action.
- The worker crashes before calling the tool: the committed pending record remains available for another attempt.
- The tool may have succeeded, but the worker crashes before recording completion: the outcome is ambiguous. Retry with downstream idempotency if supported; otherwise query or reconcile the external state before deciding whether to call again. Blindly replaying can duplicate the effect.
- The external service is unavailable: keep the intent durable and use bounded retries with backoff and monitoring appropriate to the workflow. The cited sources do not prescribe a universal interval or retention period.
Choose a relay and deduplication boundary that fit the system
| Approach | How it fits the outbox | Important boundary |
|---|---|---|
| Relational outbox table with a polling or processing worker | A database transaction writes the business row and outbox record; a processor relays the record. AWS describes this pattern with a relational outbox and SQS. | Standard SQS can deliver more than once, so AWS calls for idempotent processing. Broker delivery does not make an external side effect transactional. |
| Change data capture or change feed | A stream of committed database changes can drive publication. AWS describes DynamoDB Streams; Microsoft Learn demonstrates a Cosmos DB change feed. | For Cosmos DB transactional batches, Microsoft specifies that the transaction is confined to one logical partition. Its sample has multithreading limitations and is not production-ready. |
| Kafka relay or connector | A separate process, connector, or CDC pipeline can publish outbox records to Kafka, as described in Confluent’s guidance. | Confluent describes at-least-once delivery, so consumers still need duplicate handling. Frequent outbox inserts and deletes can also put pressure on resources. |
| Broker-side duplicate detection | Where supported, a stable message ID can reduce repeated broker messages. Microsoft’s Cosmos DB and Service Bus example uses the event document ID as MessageId for duplicate detection. | This helps at the broker boundary; it cannot guarantee that a downstream arbitrary tool’s side effect occurs only once. |
These patterns differ in relay mechanics, not in the core requirement: persist the intent with local state and make repeat delivery safe at the consumer boundary. If actions must remain ordered, include an ordering key or sequence and use consistent partitioning; do not impose ordering where the workflow does not need it.
Account for operating costs and tool limitations
An outbox adds durable storage, worker or connector operations, delivery latency, and retention and cleanup work. Backlog monitoring and recovery procedures matter because a durable record can remain pending rather than disappearing when a downstream service is down. Confluent also notes resource pressure from frequent outbox inserts and deletes; Microsoft illustrates TTL cleanup in its Cosmos DB example, while warning that the sample is not production-ready.
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Check the external tool’s actual contract before relying on its idempotency behavior: whether it accepts a key, how long it remembers keys, whether it exposes operation status, and whether the same key with different arguments is rejected. The sources cited here do not establish behavior for any particular planner tool provider. If the API offers no idempotency or status lookup, make the ambiguous-outcome path explicit and route consequential cases to reconciliation or human review.
Keep external calls out of transactions that may be retried. A local database transaction cannot roll back a remote action already performed, and rerunning the transaction can repeat that action. AWS Durable Execution documentation makes a related, product-specific distinction between at-least-once and at-most-once retry semantics; neither setting alone promises exactly-once execution across a workflow that can retry.
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