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When a GitHub Actions job is stuck or fails during a suspected outage, first check GitHub Status (including the Actions component and any applicable regional Enterprise Cloud page). Then classify the symptom: a run that never starts usually points toward runner assignment or provisioning, while a run that starts and fails usually needs ordinary workflow diagnosis. Keep caches optional, preserve artifacts, capture logs before retrying, and treat self-hosted runners as a separately operated capacity pool rather than an outage-proof replacement.
Start with scope: is GitHub Actions having an incident?
Open GitHub Status and inspect the Actions component before changing workflow code. Read the incident updates, not just the banner. Updates can identify affected runner types, regions, trigger types, and whether GitHub is seeing queue delays, start failures, or recovery. For Enterprise Cloud, check the regional status page that applies to your organization as well.
A green status page does not prove that an individual run is healthy. It means GitHub has not declared a matching platform issue on that page. If there is no incident that fits your symptom, move promptly to the run’s logs, permissions, dependencies, and external services instead of waiting for a platform explanation.
Status information is time-sensitive. At one point on October 11, 2026, GitHub Status reported all systems, including Actions, as operational; that observation says nothing about availability at another time.
Use the symptom to choose your first hypothesis
The first visible symptom narrows the investigation, although it cannot prove the underlying cause.
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| Symptom | Likely area to examine first | What to do next |
|---|---|---|
| Workflow run remains queued and no job starts | Runner assignment, hosted-runner provisioning, capacity, labels, or concurrency | Check the status incident, then inspect runner availability and the job’s requested labels. |
| Run starts, then a step fails immediately | Workflow syntax, permissions, action behavior, image/tooling, or a service used by that step | Open the failed step’s log and compare it with a known-good run. |
| Dependencies cannot be restored | Cache service or package registry availability | Allow a cache miss and test a clean download or regeneration path. |
| Deployment appears to have stopped after a retry | Lost execution state or an already-completed side effect | Verify the target system before starting another deployment. |
| Self-hosted job is queued | No online idle runner matches the job’s labels or group | Inspect runner state and connectivity; a self-hosted runner is not used merely because it exists. |
Hosted-runner incidents can arise from different layers. GitHub’s 2026 reports distinguish network failure, upstream API throttling, runner provisioning capacity, and job-assignment concurrency. A queue symptom therefore warrants checking the platform and the runner request, not blindly increasing retries.
What recent GitHub incidents show
October 5, 2026: hosted-runner networking failure
GitHub reported that, during the full October 5 incident, 14.3% of workflow runs using GitHub-hosted runners and 26.5% of individual GitHub-hosted jobs did not start within five minutes. During approximately 90 minutes of highest impact, 47.0% of hosted-runner workflow runs failed and 71.9% of GitHub-hosted jobs did not start within five minutes.
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GitHub attributed the event to a partial networking failure between an on-premises datacenter region and some cloud-hosted regional database and storage services. It mitigated the problem by shifting traffic to healthy regions and endpoints and increasing affected compute capacity. GitHub stated, “Self-hosted runners were not affected.” That statement describes this incident only; it is not a guarantee for future events or for failures in your own network.
October 1, 2026: throttling followed by an assignment limit
In a separate October 1 incident-response window, approximately 0.68% of workflow runs experienced start delays and 0.196% of jobs failed to start. GitHub said a cloud-provider routing change concentrated API traffic on one backend, producing rate-limit errors. Incorrect retry behavior increased the load, reduced regional capacity, and exposed a job-assignment concurrency limit. GitHub responded by increasing assignment concurrency and runner capacity and coordinating with its cloud provider to reduce API load.
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These percentages are incident-specific figures published by GitHub, not a normal failure rate, uptime promise, or forecast. They explain why “the job is queued” can have several causes and why indiscriminate retries can make an incident worse.
Make correctness independent of caches
Dependency caching is an acceleration mechanism, not durable storage. Use it to avoid repeated downloads or rebuilds, but design the job so it can obtain the required dependencies when the cache service is unavailable or a key misses. GitHub Docs states: “A job should always be able to re-download or regenerate the files if there’s a cache miss.”
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- Cache: reusable dependencies or intermediate files that make later runs faster.
- Artifact: a produced output that must be preserved, inspected, or passed between jobs.
Never make a cache the only copy of a package, test report, build bundle, or release input needed to complete a run. Upload required outputs as artifacts and consume those artifacts in later jobs. If a cache miss causes a build to fail, the workflow has turned an optional optimization into a correctness dependency.
Handle cache contents as untrusted input
GitHub documents cache sharing rules based on branches or tags. Treat restored cache data as untrusted: validate it before use and never store secrets in it. A cache can be stale, populated by another run allowed to share that key, or unavailable during a service incident. A clean rebuild path is both a reliability feature and a security boundary.
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Keep fallback paths observable
Log whether a cache was restored or missed, then make the fallback download or regeneration explicit in the job log. This lets an operator distinguish “cache unavailable but build succeeded” from “dependency registry unavailable” without guessing. Do not weaken token permissions or trust checks simply to force a cache hit.
Preserve evidence before you retry
- Open the workflow run and record its start time, event, branch or tag, commit, and the point at which it stopped.
- Identify whether the run has no assigned job, a queued job, or a started job with a failed step.
- Open the job log, locate the first failed step, and read the surrounding timing and error lines. Later failures may be consequences of the first one.
- Use the log search and download controls when the web view is truncated or you need to share evidence with an incident team.
- For GitHub-hosted runners, inspect the “Set up job” log for runner-image details. An image change can explain a tool or operating-system discrepancy.
- If ordinary logs are insufficient, enable runner or step debug logging for a controlled rerun, or turn on the relevant tool’s verbose mode. Capture the resulting logs and remove any sensitive values before sharing them.
- For self-hosted runners, check each runner’s state (Idle, Active, or Offline) and inspect runner and worker diagnostics on the machine.
Do not infer a platform outage solely from a long queue, and do not infer a workflow defect solely from a red run. Correlate the symptom, status updates, log timing, runner state, and any external service incident.
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GitHub lets you rerun all jobs, only failed jobs, or a selected job within 30 days of the original run. A workflow run can be rerun at most 50 times. A rerun retains the original GITHUB_SHA and GITHUB_REF, and it uses the privileges of the actor who originally triggered the run. A rerun is therefore not a new commit test and does not automatically gain the permissions of the person pressing the button.
Classify the failed job
- Usually repeatable: a test job with no external writes, a dependency installation that only reads from a registry, or a build that writes to an isolated workspace.
- Potentially repeatable: publishing, provisioning, or infrastructure steps that have an idempotency key and a way to query current state.
- Unsafe to repeat blindly: database migrations, releases that create a new immutable version, payments, destructive operations, or notifications sent to external systems.
Before retrying a deployment or publication, query the destination and deployment system to determine whether the earlier attempt completed. GitHub’s October 1 incident guidance warned that some lost execution state could not be restored by retrying an approval and advised checking completed deployment steps before initiating a new run.
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Choose the narrowest rerun
If one failed job is safe to repeat and its prerequisites are intact, rerun that job rather than every job. Rerunning all jobs can repeat tests, consume scarce runner capacity, rebuild artifacts, and trigger side effects that already succeeded. If a prerequisite artifact was not retained, a full run may be necessary; document why and verify each external effect as it occurs.
Use permissions and cache access that survive an incident safely
Reliability changes should not broaden the blast radius. Set GITHUB_TOKEN permissions to the minimum needed by each workflow or job, and use per-job permissions when different jobs have different requirements. A job that only checks out code and runs tests should not receive write access to unrelated repository resources.
GitHub’s workflow syntax supports cache access modes of read, write, write-only, and none. Select the least permissive mode that still supports the workflow. In particular, do not grant write access on low-trust triggers merely to make cache restoration succeed. GitHub warns that doing so can bypass the secure read-only default and reintroduce cache-poisoning risk.
When an outage causes a fallback path to run, keep the same permission boundaries, secret handling, and artifact validation as the normal path. A resilient workflow that downloads unverified content with a write-capable token is not a safe recovery design.
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Decide whether self-hosted runners belong in your contingency plan
Self-hosted runners give an organization control over hardware, operating system, installed software, private-network access, and specialized hardware. They can run on physical machines, virtual machines, containers, on-premises systems, or cloud machines. That control can provide useful alternate capacity when a GitHub-hosted-runner incident affects your workloads, but it transfers maintenance and security responsibility to you.
| Decision axis | GitHub-hosted runners | Self-hosted runners |
|---|---|---|
| Dependence on GitHub-hosted capacity | Jobs wait for GitHub’s available hosted capacity and affected service layers. | Jobs can use your capacity when an eligible runner is online and idle. |
| Hardware, operating system, and tools | GitHub supplies the runner environment and image updates. | You choose and maintain the machine, operating system, tools, and images. |
| Maintenance responsibility | Runner maintenance is handled by GitHub. | Your team patches, hardens, monitors, replaces, and cleans the runner. |
| Private networks or specialized hardware | Access may be limited by the hosted environment and network design. | Can be placed near private services or specialized equipment under your control. |
| Monitoring and scaling | Capacity is managed by the service, subject to its incidents and limits. | You must monitor queue depth and runner state and add capacity or autoscaling. |
| Exposure to broad GitHub failures | Depends directly on GitHub-hosted runner services. | Still requires communication with GitHub; a broader GitHub or customer-network failure can affect it too. |
During the October 5, 2026 hosted-runner incident, GitHub reported that self-hosted runners were not affected. That is evidence for one event, not proof that self-hosting bypasses every outage. A GitHub-wide connectivity problem, expired credentials, DNS failure, firewall change, unavailable private dependency, or broken customer infrastructure can stop both runner types.
Understand the self-hosted runner queue rules
A self-hosted runner is useful only when it is online, idle, and eligible for the job. The job’s labels and runner group must match; an unrelated online machine will not be selected. GitHub documents that a job can remain queued until a matching runner comes online and that “If the job remains queued for more than 24 hours, the job will fail.”
- Give runners distinctive labels that describe stable capabilities, such as operating system, architecture, or a required device.
- Use runner groups to restrict which repositories or organizations may dispatch jobs to a pool.
- Monitor the Idle, Active, and Offline states and alert on an unexpected Offline count or a growing queue.
- Verify outbound communication to GitHub, DNS, proxy, firewall, credentials, disk space, and available concurrency on the machine.
- Keep the runner software and installed tools patched, and remove workspaces or credentials according to your security policy after jobs complete.
For Kubernetes-based autoscaling, GitHub’s documentation recommends Actions Runner Controller. It can add elastic capacity, but it also introduces Kubernetes, controller, image, permissions, and observability responsibilities. Use it only when your team can operate that additional system during an incident.
Build a practical outage playbook
When jobs are stuck in the queue
- Check GitHub Status, the Actions component, and the applicable regional Enterprise Cloud page.
- Read incident updates for affected regions, runner types, and recovery status.
- Confirm whether the job requests a hosted runner or a self-hosted label and group.
- For self-hosted jobs, verify that at least one matching runner is online and idle.
- Do not increase retries or cancel and recreate large batches of runs without a capacity reason; repeated assignment requests can add load during throttling or concurrency incidents.
- If the queue exceeds your operational deadline, invoke your documented release or test contingency, preserving the original commit and approvals.
When a job starts and fails
- Identify the first failed step and inspect its log and timing.
- Check runner-image details, permissions, dependency sources, and external APIs used by that step.
- Test the cache-miss or clean-download path if dependency restoration is involved.
- Enable debug logging only for a controlled diagnostic rerun and redact secrets from exported logs.
- Rerun the smallest safe unit after verifying that no external side effect already completed.
When a release or deployment is interrupted
- Pause automatic retries and record the commit, environment, run identifier, and last completed step.
- Query the deployment target, artifact store, migration system, or publication endpoint for current state.
- Determine whether the operation is idempotent and whether it has a unique release or request identifier.
- Resume from a supported checkpoint or rerun only the verified-safe job.
- Record the final state and evidence so a later operator does not repeat the same side effect.
Test resilience before the next outage
- Run a scheduled build with caches unavailable and verify that dependencies are downloaded or regenerated successfully.
- Confirm that required build outputs are artifacts, not only cache entries, and that downstream jobs can retrieve them.
- Review every job’s
GITHUB_TOKENpermissions and remove unused write access. - Exercise the self-hosted runner path with a deliberately drained or offline runner, then verify alerting and queue behavior.
- Document which jobs are safe to rerun and which require a destination-state check.
- Record the 30-day rerun window and 50-rerun limit in the incident runbook.
- Verify that logs can be searched, downloaded, retained, and shared without exposing secrets.
- Define the point at which a queued release is deferred rather than repeatedly resubmitted.
The objective is not to make every component redundant. It is to ensure that a temporary GitHub service problem, a cache outage, or a runner-capacity reduction does not turn an optional optimization into a corrupted release or an unrecoverable deployment.
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