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A client timeout does not prove that a shared tool call failed, stopped running, or lost its lease. The request may never have arrived, may still be executing, or may have committed while its response was lost. Treat the result as in doubt: preserve the operation’s identity, check authoritative state, and replay only when the tool’s idempotency and lease rules make it safe.
Does a timeout mean the operation failed?
No. A timeout tells you that the caller stopped waiting; it does not reveal what happened at the receiver. Apache Kudu’s RPC retry design describes the same uncertainty: after a timeout or network failure, a sender may not know whether the original request succeeded. Apache Kudu’s retry and failover design explains why blindly sending the request again can duplicate work.
- The request may not have reached the tool.
- The tool may still be running it.
- The tool may have completed the operation, but the response may have been lost.
For a shared tool slot, there is another risk: work can still be valid under its lease after the client timer expires. Conversely, a late result may no longer be allowed to update shared state if a newer lease or conversation epoch has taken ownership. A timeout and a lease are separate state; neither tells you the other has expired.
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Mark the logical operation as unresolved, keep its identity, and find out what the component that owns the operation recorded. The exact status lookup and cancellation steps depend on the runtime; there is no universal API for them.
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- Record the result as in doubt. Do not convert expiration of the client’s timer into a failure status.
- Keep the operation identity. Retain the same operation or idempotency key and normalized request fingerprint. A lost response is not a reason to create a new logical operation.
- Check authoritative state. Query the durable operation journal or owning coordinator, if the implementation provides one. Determine whether the operation is pending, committed, rejected, or expired.
- Validate ownership before accepting a late result. The commit path should check that the lease and generation or epoch still authorize the write. A worker’s local belief that it owns the slot is not enough to protect shared state.
- Choose a bounded recovery. Reuse a recorded result if the operation committed. If it is still running, wait or use the documented cancellation protocol. If it is known not to have been dispatched, retry with the same identity. If a non-idempotent operation’s outcome remains unknown, stop automatic replay and surface the unresolved state for inspection or compensation.
Can I safely retry a timed-out request?
Only if the operation is safe to repeat or the receiver enforces effective idempotency. AWS’s Durable Execution SDK Developer Guide states, “Replay and retry can each run the same operation more than once.” Its guidance distinguishes at-least-once behavior, which suits idempotent operations, from at-most-once behavior for side effects that should not be automatically re-executed. That is SDK guidance, not a universal guarantee. AWS Durable Execution SDK replay semantics
Also qualify “at most once.” A system may make at most one execution attempt within a particular retry mechanism, yet a higher-level workflow policy can start another attempt. That does not amount to exactly-once execution across the whole workflow or exactly-once external effects.
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| Operation or guarantee | What a retry means | Safer response to an unknown outcome |
|---|---|---|
| Read or naturally repeatable operation | Repeating may be harmless, although the returned state can differ over time. | Retry according to the tool’s documented policy; keep identity if the runtime supports it. |
| Idempotent write with receiver-side deduplication | The receiver recognizes the same logical operation and avoids applying its effect twice. | Retry with the same key and matching payload, within the documented key scope and retention period. |
| Non-idempotent side effect | A second execution may create a second effect. | Reconcile with the authoritative system; do not replay automatically while the result is unknown. |
How do I prevent a duplicate tool call after a timeout?
Use a stable operation key and receiver-side durable deduplication. The receiver should associate that key with the operation’s outcome and reject reuse of the key with a conflicting request. A caller-side key alone is not protection: the component that performs the effect must recognize and enforce it.
TDE Security’s documentation describes one product-specific approach: a durable journal scoped by tool name and idempotency key, with a fingerprint of the normalized command to detect conflicting reuse. It also describes reporting an unknown outcome after dispatch when the result cannot be persisted, and directing the caller to inspect state rather than retry blindly. These are implementation details of that documented system, not guarantees shared by all tool runtimes. TDE Security documentation
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Idempotency records also have a lifetime. Apache Kudu’s design document describes how a retry can execute twice if the stored response record has already been garbage-collected. Its retention example is specific to Kudu’s design; it is not a general timeout window. Make sure the deduplication horizon in your implementation exceeds the longest delay during which retries or recovery can occur, or explicitly define what callers should do after that horizon.
How should leases and epochs protect shared state?
A lease governs ownership; a client timer governs how long the caller waits. A worker can still hold a valid lease after a client timeout, so the caller must not infer that the slot is free and start a competing operation. At the same time, an old worker can finish after ownership has moved on. The receiver or coordinator should validate the current lease and generation or epoch at commit time, rejecting a stale write.
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This distinction matters in conversational systems as well as other shared-tool runtimes: a completion from an earlier turn should not be appended to state owned by a newer turn. This is a design principle, not a claim that any particular runtime uses a specific lease, epoch, or status API. Follow the runtime’s documented rules for ownership, commit, and cancellation.
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What should a safe retry contract specify?
Before enabling automatic replay, establish the boundaries of the guarantee with the tool owner. In particular, document:
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- Delivery scope: whether delivery is at-least-once, at-most-once per attempt, or retried by a higher-level workflow.
- Operation behavior: whether the operation is a read, a naturally repeatable action, an idempotent write, or a non-idempotent side effect.
- Identity rules: how keys are generated and scoped, how long they remain valid, and how the receiver checks that a repeated key has the same normalized payload.
- State authority: which durable journal or coordinator can confirm whether an operation is pending, committed, rejected, or otherwise resolved.
- Lease safety: whether commits validate current ownership and generation or epoch, rather than trusting only the worker’s local lease state.
- Retention horizon: how long deduplication records survive compared with the maximum retry and recovery delay.
- Unknown-outcome behavior: whether callers should wait, inspect, compensate, or expose an unresolved state instead of replaying.
Do not claim exactly-once external effects unless the external service also enforces the propagated idempotency identity or fencing condition. A workflow’s internal retry policy cannot, by itself, guarantee that a separate system applied an effect only once.
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