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When an AI coding assistant is down, your team should be able to diagnose the failure, switch to a rehearsed fallback, and continue building, testing, reviewing, and shipping code. Treat the assistant as an accelerator—not as a prerequisite for engineering work.
The practical plan is simple: verify whether the outage is global, local, account-specific, or model-specific; move to an approved alternate model, provider, or offline setup; then use ordinary development tools for everything the fallback cannot cover.
The outage runbook at a glance
- Check the provider’s official status page. Look for an active incident affecting the service or a specific model.
- Classify the failure. Separate a provider outage from an expired sign-in, old editor extension, network problem, account entitlement issue, or rate limit.
- Try an already-approved alternative. A different model in the same service may work during a model-specific incident; a second provider is more independent.
- Use the prepared local path. In VS Code, a local model connected through the official Ollama extension can support chat without internet or sign-in for supported tasks.
- Keep non-AI work moving. Run tests, inspect logs, reproduce bugs, review diffs, update documentation, and prepare small changes with normal engineering tools.
- Record what happened. Capture the time, affected service or model, symptoms, fallback, and blocked tasks so the next rehearsal is based on evidence.
Do not improvise a new provider or download an unapproved model in the middle of a release. A fallback is useful only when credentials, policy, editor configuration, model availability, and team procedures have been checked in advance.
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An error that looks like an outage can originate in several places. Identifying the failure class prevents wasted time and tells you which fallback is appropriate.
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Provider-wide incident
Start with the provider’s official status page and incident updates. GitHub’s troubleshooting guidance specifically recommends checking its Status page for incidents affecting Copilot or model availability. If the status page reports a broad event, avoid repeatedly changing local settings; move through the fallback steps in your runbook.
Model-specific degradation
A hosted service can remain healthy while one model fails. GitHub’s June 2026 availability report records a 55-minute degradation for Opus 4.8 on June 16, from 17:20 to 18:15 UTC, while other Copilot models were unaffected. On June 17, most frontier Copilot chat models were unavailable from approximately 03:50 to 04:44 UTC, but users could continue by selecting models that remained available.
That distinction matters: switching models may restore work during a model incident, but it does not protect you from a provider-wide outage because the models still share the same service, account, network path, and control plane.
Client, authentication, or entitlement failure
If the status page is healthy, inspect the client path:
- Confirm the editor extension is current. GitHub notes that older Copilot clients may no longer communicate with its servers.
- Check that the expected account is signed in and still entitled to the service or organization plan.
- Read the exact error for quota, rate-limit, or usage-limit information.
- Verify network access, proxy settings, and firewall rules.
- For some VS Code authentication failures, GitHub documents a sign-out, reload, and sign-in sequence.
Have one developer perform this diagnostic while others continue with the fallback. A team-wide sign-out or extension upgrade during an incident can make recovery harder if the original problem is elsewhere.
Local service or configuration failure
When the fallback is a local model, the hosted provider may be healthy and your local service may still be the problem. Check that the local runtime is running, the model is installed, and the editor can discover it before concluding that offline inference is unavailable.
Build the fallback before an incident
Resilience comes from preparation, not from finding a download link after the primary assistant fails.
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Map each workflow dependency
List the AI-assisted functions your team actually uses and mark whether each is essential during an outage:
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- Chat questions and code explanations
- Agent edits and multi-file changes
- Inline completions
- Repository or semantic search
- Test and test-data generation
- Pull-request review assistance
- Commit-message and title generation
For every function, document an ordinary non-AI method and at least one tested fallback. For example, a developer can run the project’s search tools and inspect call sites manually even if semantic search is unavailable.
Configure a second cloud path
VS Code supports bring-your-own-key (BYOK) connections to compatible providers, provider extensions, and custom endpoints that implement supported API shapes. A second cloud provider can reduce dependence on one vendor, but it still needs internet access, credentials, service capacity, and policy approval.
Check organization settings before promising this option. Administrators can disable BYOK for Copilot Business and Enterprise users. For VS Code Agent Host sessions, BYOK support is marked experimental and may change or be removed, so record the exact editor version and configuration used in your rehearsal.
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VS Code documents that a local model can be used completely offline. Local models do not require a GitHub account or Copilot plan. The documented Ollama integration requires:
- VS Code 1.127 or newer
- Ollama installed and running
- At least one model downloaded and available
The official Ollama extension discovers local models at http://127.0.0.1:11434 by default. Ollama recommends version 0.17.6 or newer for cloud sign-in and richer model metadata; older versions may still work with local models, but version behavior can change.
Rehearse with real work
Do not validate a fallback only by asking it a toy question. Use a representative task from the team’s backlog and verify:
- What repository context the model can read
- Whether it can edit one file or multiple files
- Which tools and terminal commands it can invoke
- Whether tests, linters, and type checks still run normally
- How secrets and proprietary code are handled
- Which features disappear when the internet or hosted account is unavailable
Official offline documentation does not establish equivalent quality, latency, or feature coverage for every model and workload. Record the tasks the fallback handles acceptably and the tasks that must wait for the hosted service.
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Every project should retain normal scripts and documented procedures for building, testing, linting, type checking, packaging, deployment, rollback, and debugging. Keep changes small and reviewable, and ensure at least two people know how to execute the critical commands without an assistant. This is the difference between losing an accelerator and losing the ability to ship.
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Fallback choices and their trade-offs
| Fallback | What it can do | Dependency and checks |
|---|---|---|
| Another model in the same hosted service | May restore chat or generation when one model is degraded; GitHub reported incidents where other models remained usable. | Still depends on the same provider, account, network, and service control plane. |
| Another cloud provider or compatible endpoint | Can reduce dependence on one provider when configured through a compatible VS Code provider, extension, or endpoint. | Requires internet, credentials, capacity, security review, and organization approval. |
| Local model through VS Code and Ollama | Supports chat without internet and without a GitHub account or Copilot plan for supported workflows. | Requires compatible VS Code and Ollama versions, local model storage and runtime, and a rehearsal on the team’s hardware. Offline semantic search, embeddings, and inline suggestions are not provided through the documented route. |
| Manual engineering workflow | Keeps implementation, tests, debugging, review, and documentation moving without an assistant. | Uses normal engineering practices and may be slower for tasks that usually rely heavily on AI; the slowdown varies by team. |
Evaluate each option on five axes: independence from the primary provider, editor-function coverage, authentication and policy requirements, local setup effort, and whether the team has practiced it.
How to switch to a local model in VS Code
The exact labels can change, but the current documented path uses the official Ollama extension rather than VS Code’s deprecated built-in Ollama provider.
Install and verify the prerequisites
- Update VS Code to version 1.127 or newer.
- Install Ollama for the team’s operating system and start the Ollama service.
- Download at least one model appropriate for the team’s codebase and machine.
- Install the official Ollama extension in VS Code.
- Open the model picker and confirm that the downloaded model appears.
The local service is normally discovered at http://127.0.0.1:11434. A local model does not need a sign-in.
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Select the model for chat
- Open VS Code Chat or the model picker.
- Choose the Ollama provider.
- Select an available local model.
- Ask a small repository question first, then try a representative coding task.
- Run the project’s tests and inspect the diff yourself before accepting any generated change.
VS Code also exposes model management through Chat: Manage Language Models. If your team wants title-generation and commit-message utility work to use a local model, the documentation identifies the chat.utilityModel and chat.utilitySmallModel settings for that purpose.
Understand what remains unavailable offline
Offline BYOK does not reproduce every cloud feature. VS Code identifies semantic search, embeddings, and inline suggestions as requiring GitHub connectivity. Locally hosted models cannot currently connect to its inline suggestions through the documented route. Plan for chat and selected utility tasks, not for a drop-in replacement for every hosted assistant feature.
Troubleshoot missing Ollama models
- Confirm that Ollama is running.
- Run
ollama listin a terminal and verify that at least one model is installed. - In VS Code, refresh models from the Command Palette.
- Run Ollama: Diagnose Models.
- Check that the extension is pointing to the expected local service address.
Earlier VS Code versions did not reliably cancel language-model provider requests, which is another reason to use the current supported editor version for an outage plan.
What to do during the incident
Step 1: Open the status page and start a timestamp
Record when the team first noticed the failure and attach the provider’s incident link or status message to the incident channel. A status page that says “operational” does not rule out a local authentication or model-specific problem, but it changes the next diagnostic step.
Step 2: Test one known-good client
Use a developer account and editor setup that worked earlier. Check extension version, sign-in state, account entitlement, network access, and rate-limit messages. Avoid having every developer change settings simultaneously.
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Step 3: Try an approved alternate model
If the provider reports that one model is degraded, select a model already approved and configured for the repository. Compare the result with the task’s requirements; a smaller or different model may answer questions but fail on large edits or tool use.
Step 4: Move to the independent fallback
If the provider is broadly unavailable, select the prepared second provider or local model. In VS Code, use the model picker or Chat: Manage Language Models. With Ollama, confirm the local service is running and that a model is available before assigning outage work to it.
Step 5: Partition the backlog
Give developers work that does not require hosted AI:
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- Inspect logs, traces, metrics, and recent deployment changes.
- Write, run, and debug unit, integration, and end-to-end tests.
- Review diffs and check error handling, security, and migration safety.
- Update documentation, runbooks, and release notes.
- Prepare small, independently reviewable changes for later implementation.
Do not claim that a local model preserves cloud agent behavior. Keep human review and normal CI gates in place for every generated change.
Step 6: Log blocked work
For each blocked task, note the required feature, repository context, model or provider attempted, error message, and whether the task can be decomposed. This prevents the same experiment from being repeated by several developers and shows where the fallback needs improvement.
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A fallback changes where code and prompts are processed, so treat it as a production configuration change.
- Confirm that the organization permits the alternate provider, endpoint, extension, and model.
- Document whether prompts, source files, telemetry, or logs leave the company network.
- Use the same secret-management rules as the primary workflow; never paste credentials into chat.
- Limit repository access to the files required for the task.
- Require normal code review, automated tests, and security scanning before merge.
- Define who can approve a new provider or model during an incident.
Local inference can reduce account and network dependency, but it does not automatically make a workflow safe. The machine, model files, editor extensions, and generated artifacts still need the team’s security controls.
Post-incident review and recurring validation
After service is restored, compare the fallback’s actual behavior with the runbook. Record which tasks were completed, which features were missing, how long setup took, and which instructions were ambiguous. Update the configuration while the incident is still fresh.
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Assign an owner to review provider credentials, extension versions, model availability, and organization policy on a recurring schedule. Hosted integrations can be retired or changed. GitHub announced that GitHub Models—including its playground, model catalog, inference API, and BYOK endpoints—was fully retired on July 30, 2026. A fallback that worked last quarter is not necessarily available today.
Rehearse at least two scenarios: a single-model outage and a provider-wide outage. The first tests model switching; the second tests provider independence and the local or manual path. Include a developer who did not configure the original setup so the procedure is usable by the whole team.
Common mistakes that prolong an outage
Assuming every error is a provider outage
Ignoring sign-in state, extension age, entitlement, network access, or rate limits can leave a local problem unresolved while the team waits for a status update.
Relying on a second model in the same service as the only backup
Model switching helps with partial incidents, but it does not protect against a provider-wide failure.
Installing a local model for the first time under release pressure
Download time, model discovery, editor compatibility, policy review, and unfamiliar commands all become bottlenecks. Install and test the local path before it is needed.
Expecting offline chat to replace every cloud feature
Semantic search, embeddings, and inline suggestions remain unavailable through the documented offline route. Define the supported task boundary explicitly.
Skipping ordinary engineering controls
Generated code still needs tests, review, type checks, security checks, and deployment safeguards. An outage is not a reason to bypass them.
The practical standard for startup resilience
Your startup is resilient when a hosted AI outage removes convenience rather than capability. Developers should know how to identify the failure, select an approved alternate, use a tested local model for supported chat tasks, and continue with normal coding and verification tools. Rehearsals, clear ownership, and periodic lifecycle checks matter more than choosing a particular model.
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