Prevent duplicate runs and lost work by treating three problems separately: which events should start work, which worker may act on an issue at a time, and how accepted work and external side effects survive retries. A GitHub Actions concurrency group can serialize runs, but it is not a durable queue or a guarantee that every event will be processed. For reliable issue automation, combine deliberate event handling, correctly scoped concurrency, persisted task state, and idempotent operations.
Find where duplicate work enters the system
Duplicates can start before the agent process runs. One issue can produce several matching events: GitHub’s workflow syntax documentation gives the example of an issue opened event followed by two label events, which can trigger three workflow runs. Repeated user actions and event delivery should therefore be treated as ordinary inputs, not exceptional failures.
First define the identity of the work you intend to perform. An issue number identifies the issue, but it may not identify a particular task or version of that issue. Decide whether each new event should merge into existing work, wait behind it, or replace it. For example, a new label might add information to an already queued task, while a push that makes analysis of an earlier commit obsolete might justify superseding that analysis.
Keep a durable event or task identity so repeated delivery can be recognized. Do not assume that “one run per issue” is always correct: the right unit may be one task per issue, one task per issue revision, or one task per independent finding.
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Choose concurrency behavior based on whether work may be dropped
GitHub Actions allows multiple workflow runs and jobs concurrently by default. A concurrency group prevents matching work in that group from running simultaneously, but GitHub documents a consequential default: only one pending run is retained, and a newer pending run can replace the older pending run. Serialization therefore does not necessarily mean every accepted event will be processed.
| Policy | Use it when | Main trade-off |
|---|---|---|
| Serialize by issue | Only one worker should mutate or act on a particular issue at a time. | Prevents simultaneous work in the group, but the pending-run policy still matters. |
| Queue accepted work | Every accepted event or intermediate update must be handled. | Preserves work only if the selected queueing mechanism actually retains it and its limits are understood. |
| Cancel or replace older work | A newer input makes an older task obsolete, such as analysis tied to an outdated commit. | Older work may stop before completion; this is safe only if that work is intentionally disposable or recoverable. |
GitHub Actions documentation also describes a queueing mode with up to 100 pending runs. Its exact supported syntax and behavior can change, so check the current GitHub Actions concurrency documentation before relying on a configuration value. A concurrency group is not a substitute for durable task records, particularly when exceeding queue limits or handling work outside that group.
Scope the key to the resource being protected
For issue-level exclusion, a useful conceptual key is workflow-name + issue-number. Including workflow identity helps prevent separate workflows from colliding when they reuse a group name; GitHub documents that concurrency group names can collide across workflows. If several workflows intentionally contend for the same issue, choose a shared coordination design deliberately rather than relying on accidental name reuse.
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At job level, avoid a single static group for independent fan-out tasks: unrelated jobs can then compete for one slot. GitHub Agentic Workflows documents a concurrency.job-discriminator feature for distinguishing such work. That feature is specific to GitHub Agentic Workflows, not generic GitHub Actions syntax, and should not be copied into ordinary workflow configuration.
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A timeout can leave the agent unsure whether a remote action succeeded. For instance, a request to create a pull request may reach the service even if the response is lost. Retrying with a fresh random identity can create a second effect.
Use a stable idempotency key derived from the domain identity and operation, such as the issue identity plus create-pull-request. Persist the operation’s result or state transition, then check it before repeating the effect. This key design is an application pattern, not a guarantee that every external API supports or honors idempotency keys.
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- When the provider supports idempotency keys: send the same stable key on retries and follow that provider’s documented retention and response semantics.
- When it does not: use an application-side ledger or outbox to record intended and completed effects, and reconcile uncertain outcomes before retrying.
- When duplication would be unsafe and cannot be prevented: disable automatic retries for that effect and route uncertain outcomes to a reconciliation or human-review path.
AWS’s Durable Execution SDK guidance explains that replay may rerun a step and that code inside it must be safe to run more than once. It also cautions that an at-most-once setting for an individual retry does not guarantee only one workflow-wide attempt if retries remain enabled. Do not promise “exactly once” unless the whole path—including the external system—provides the necessary contract.
Persist progress outside the agent process
A worker’s memory disappears when its process crashes. Keep orchestration state somewhere durable so recovery does not depend on the original process still existing. A useful task record can include:
- Issue, event, and task identities, plus the issue revision or other input version when relevant.
- Current state, such as accepted, running, checkpointed, waiting-for-agent, completed, failed, or canceled.
- Attempt count, current owner or lease, timestamps, and the next retry or expiry time.
- A checkpoint, session handle, or other information needed to resume or safely restart the work.
- Results of completed external operations and the final outcome or reason for failure.
Make each step report a recoverable outcome. A step should leave enough information to distinguish “not attempted,” “completed,” and “possibly completed but response lost”; otherwise recovery may either drop work or repeat an effect blindly. AWS’s sample coding-agent architecture illustrates admission control, idempotency lookup, durable steps, persisted backend handles, retries, and timeouts. Its particular numerical defaults are sample-specific, not universal recommendations.
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Use explicit recovery rules
For each nonterminal state, define who may reclaim the task and what to do with partial progress. A worker can claim work using a lease or equivalent ownership record; an expired lease can make a task eligible for recovery. Before restarting a step, consult its checkpoint and effect ledger. Do not mark a task complete merely because the agent process exited successfully if required external writes have not been confirmed.
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An agent that comments on or labels an issue may generate another event that starts the same agent again. Filter triggers to the human or system actions that should initiate work, and prevent the automation’s own outputs from recursively triggering it where the platform supports that control.
Also limit what a run can do and how long it can consume resources. GitHub Agentic Workflows documents read-only agent permissions with writes mediated through safe outputs, concurrency controls, timeouts, rate limits, and manual review gates. These controls address different risks: event filters limit self-triggering, permission boundaries constrain changes, review gates add oversight, and timeouts or rate limits cap runaway work. Confirm the current availability and status of these product-specific features before adopting them; preview status can change.
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For scale context only, the GitHub Agentic Workflows rate-limiting documentation specifies a default 20-minute agent execution timeout and a 360-minute GitHub Actions platform default for other jobs unless overridden. It also documents built-in spacing of 10 seconds between agent assignments and 5 seconds between workflow dispatches. These are product defaults described by that documentation, not general timeout or rate-limit recommendations.
Design review: check the whole path, not just the workflow setting
Before enabling issue-driven automation, verify each of these decisions:
- Event semantics: Which events are accepted, deduplicated, merged, queued, or superseded?
- Concurrency scope: What resource is protected by the group, and can unrelated workflows or fan-out jobs collide?
- Pending work: Does the platform retain every accepted task, or can a newer run replace an older pending run?
- Recovery: Where are checkpoints and task state stored, and how does another worker resume an abandoned task?
- Effects: Which writes have stable idempotency support, and how are uncertain outcomes reconciled?
- Observability: Can an operator find stuck, failed, superseded, or possibly duplicated tasks and determine their final outcome?
- Safety limits: Are permissions, output mediation, review gates, timeouts, and rate limits appropriate for the automation?
A basic Actions concurrency group addresses simultaneous execution. A reliable system must additionally preserve accepted work and make replay safe across the agent, orchestration layer, and external services.
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