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Do not let an AI outage become an engineering outage. First determine whether the failure is the provider, a single model, your editor, authentication, or a quota. Then switch to a fallback that the team has already configured and tested. A second hosted model can cover a model-specific incident; a separate provider or compatible endpoint reduces dependence on one service; and a local model can keep some chat work available without internet. Build, tests, review, debugging and deployment should remain usable even when every assistant is offline.
This runbook is designed for startup teams using AI-assisted development. It treats an assistant as an accelerator rather than a prerequisite for shipping code.
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What to do first when the AI coding assistant is down
- Check the provider’s official status page and incident feed. GitHub’s troubleshooting guidance specifically recommends checking its Status page for active incidents affecting Copilot or model availability.
- Identify the scope. Try a second model in the same service, a different editor surface (chat versus inline completion), and a second developer account. A failure affecting one model is different from a provider-wide outage.
- Check the client path. Verify the editor extension is current, the user is signed in, the account still has the required entitlement, the network can reach the service, and no rate-limit or quota message is present.
- Use the prepared fallback. Select an approved alternative model, provider endpoint, or local model. Do not spend the outage inventing a new setup on a production task.
- Continue independent work. Run tests, inspect logs, reproduce bugs, review diffs, update documentation and prepare small changes while assistant access is restored.
- Record the event. Capture the service, UTC start and end times, symptom, diagnostic result, fallback used and any task that remained blocked.
The first decision is therefore not “Which local model should we buy?” It is “Which dependency failed, and what work can continue without it?”
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Map the workflow before choosing a fallback
List every place the hosted service appears in your development process. The same outage may disable one feature while leaving others usable.
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| Workflow function | Questions to answer | Continuity plan |
|---|---|---|
| Chat and explanations | Can developers ask questions about selected files or pasted context? | Use an alternate model or local chat; otherwise write a focused question in the issue or pull request and solve it manually. |
| Agent edits | Can the assistant modify files, run commands or use repository tools? | Check whether the fallback supports the same tools. If not, make small manual edits and review each diff. |
| Inline completions | Are suggestions delivered as the developer types? | Expect this to be a separate dependency. VS Code documents inline suggestions as unavailable through offline BYOK. |
| Repository search and semantic context | Does the feature depend on hosted indexing or embeddings? | Use ordinary editor search, command-line search and code navigation. |
| Test generation and debugging | Can the assistant inspect failures and propose tests? | Run the normal test command, read the failure output and create a minimal reproducer. |
| Code review and summaries | Are review comments or commit messages generated remotely? | Review the diff manually and use a local utility model where configured. |
For each function, write down the repository, credentials, network access, tools and privacy rules it requires. A fallback is useful only if it covers the particular task the team needs during an incident.
Prepare a fallback before an outage
Keep a second path available
In VS Code, BYOK can connect compatible providers, provider extensions and custom endpoints that use supported API shapes. Configure this while the primary service works, and obtain approval from security and the organization administrator first. Administrators can disable BYOK for Copilot Business and Enterprise users, so a developer cannot assume the setting will be available.
A second cloud provider reduces dependence on one vendor, but it is not an offline solution. It still needs internet access, credentials, an active account, acceptable data handling and an independent availability check. Store its configuration in the team’s approved setup documentation rather than in a private note on one engineer’s laptop.
Install and test a local route
VS Code supports local models through its language-model provider system. Its documentation states that a local model can be used completely offline, without a GitHub account, Copilot plan or internet connection. That statement applies to supported chat and model-provider functions; it does not mean that every cloud feature moves to the laptop.
The official Ollama integration requires:
- VS Code 1.127 or newer.
- Ollama installed and running.
- At least one local model available to the Ollama service.
The extension discovers 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.
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Install the official Ollama extension rather than relying on VS Code’s deprecated built-in Ollama provider. Existing users of the built-in provider should migrate using the current VS Code and Ollama documentation.
Test the exact work you expect to do
A “model responds” check is not a rehearsal. Use a representative repository and run the tasks the team would perform during an outage: explain a failing test, edit a small function, generate a test, summarize a diff and answer a question about project conventions. Check whether the model receives the intended files, whether tools are available, whether repository data stays within policy and whether the response is good enough for a human review.
Do not describe the local route as equivalent to a hosted coding assistant. VS Code identifies semantic search, embeddings and inline suggestions as requiring GitHub connectivity; those capabilities are not available through offline BYOK. The documented route also says locally hosted models cannot currently connect to VS Code inline suggestions through that path.
Keep the codebase independently operable
- Retain normal scripts for builds, tests, linting, type checks, packaging and deployment.
- Document how to run each script and where its logs appear.
- Keep changes small enough to review without an AI-generated summary.
- Ensure at least two people can debug the main services without the assistant.
- Keep dependency caches, test fixtures and local development instructions current.
These practices reduce the blast radius of an outage regardless of which fallback you choose.
Compare the realistic fallback options
| Option | What it helps with | What it does not solve | Checks before relying on it |
|---|---|---|---|
| Another model in the same hosted service | Can keep work moving when one model is degraded or unavailable. | Shares the provider, account, network and often the same control plane; it will not protect against a full provider outage. | Confirm the model is enabled for the organization and test its context, tools and rate limits. |
| Another cloud provider or compatible endpoint | Reduces dependence on one provider and can support chat or agent work if the editor integration is compatible. | Still requires internet, credentials, policy approval and independent service availability. | Validate data handling, billing controls, supported API shape, model access and an emergency sign-in path. |
| Local model through VS Code and Ollama | Supports local chat without internet and avoids a GitHub account or Copilot-plan requirement for local models. | Does not preserve all cloud features. Semantic search, embeddings and inline suggestions are not available through the documented offline route. | Use the supported VS Code version, keep Ollama running, confirm a model appears and test the team’s real tasks. |
| Manual engineering workflow | Keeps implementation, testing, review, documentation and debugging moving without an assistant. | May be slower for work that normally relies heavily on AI; the slowdown is team-specific and has not been quantified here. | Maintain scripts, checklists, ownership and code-review capacity. |
Rank alternatives on five dimensions: independence from the primary provider, feature coverage, authentication and policy requirements, local hardware and setup effort, and whether the team has rehearsed the path.
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Use a local model offline in VS Code
Verify the prerequisites
- Open VS Code and confirm the editor is version 1.127 or newer.
- Install and start Ollama on each developer machine that is expected to use the fallback.
- Ensure at least one model is available to Ollama.
- Install the official Ollama VS Code extension.
- Open the model picker and select the available local model.
Local models do not require sign-in. The extension normally looks for the local service at http://127.0.0.1:11434.
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- Confirm that Ollama is running.
- Run
ollama listin a terminal and verify that a model is listed. - Refresh models from the VS Code Command Palette.
- Run
Ollama: Diagnose Modelsfrom the Command Palette. - Check that the editor and Ollama are running under the same user account and that local security software is not blocking the loopback service.
Earlier VS Code versions did not reliably cancel language-model provider requests. Upgrade the editor before judging the integration during a real incident.
Configure small utility tasks
VS Code documents settings that can direct title-generation and commit-message utility work to a local model: chat.utilityModel and chat.utilitySmallModel. Configure and test these settings in advance if commit messages or titles are part of the team’s normal flow. They do not restore cloud semantic search or inline completion.
Run the incident diagnosis in branches
If the status page reports an incident
Record the incident reference and timestamp, then move to the approved fallback. Recheck the provider’s updates at an agreed interval rather than having every developer repeatedly reload the editor. Keep sensitive source code out of unapproved emergency services.
If the status page is healthy
Inspect the local path: extension version, sign-in state, account entitlement, network route, proxy or firewall behavior, and rate-limit or quota messages. GitHub’s troubleshooting documentation treats these as distinct failure classes. For some VS Code authentication problems, its documented recovery sequence includes signing out, reloading the window and signing in again. Apply that only after preserving any useful error text.
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If only one model fails
Switch to a model that is already approved and configured. GitHub’s June 2026 availability report documents a 55-minute degradation for Opus 4.8 on June 16, while other Copilot models were unaffected. The same report describes a June 17 incident running approximately 03:50–04:44 UTC, about 54 minutes, when most frontier Copilot chat models were unavailable across regions but users could continue by selecting models that remained available.
These incidents show why “the AI service is down” is often too broad a diagnosis. A model-level fallback can work, but it still shares the hosted service’s wider dependencies.
If the whole hosted path fails
Select the preconfigured local model for supported tasks. In VS Code, use Chat: Manage Language Models or the model picker. With the Ollama extension, make sure the local service is running and that a model is available. Move tasks that depend on unavailable cloud features—such as semantic search or inline suggestions—to ordinary editor and command-line tools.
Keep shipping while assistance is unavailable
Work the queue in smaller units
Split broad requests into independently reviewable changes. A developer can inspect the failing stack trace, write a minimal test, implement one fix, run the relevant checks and open a focused pull request without an agent performing every step.
Replace assistant-specific actions with standard tools
- Use repository search and language-server navigation instead of semantic search.
- Read compiler, test and linter output directly.
- Use the project’s documented build and deployment commands.
- Review the complete diff rather than relying on an automated summary.
- Write release notes and commit messages from the change description and test results.
Protect review quality
Do not relax review requirements because an outage creates time pressure. Require the same tests, security checks and human approval. If a local model produced code, label that internally so reviewers know to verify assumptions, tool calls and dependency changes carefully.
Security, privacy and policy checks
Before enabling any fallback, decide which source code, logs, customer data and secrets may be sent to it. A second cloud provider may have different retention, region and training policies. A local model reduces network exposure but does not automatically make secrets safe; prompt logs, editor extensions and local telemetry still need review.
Confirm that emergency access does not bypass organization controls. BYOK may be disabled for Copilot Business and Enterprise users, and experimental support for BYOK in VS Code Agent Host sessions may change or be removed. Document who can approve a temporary provider, how credentials are revoked and where incident evidence is stored.
Rehearse and maintain the runbook
Run a quarterly failure exercise
- Choose one representative repository and a normal sprint task.
- Disable or disconnect the primary assistant for a short, announced exercise.
- Have developers diagnose the failure, select the fallback and complete the task.
- Measure which editor functions worked, which context was missing and which policy questions arose.
- Update the setup instructions, ownership list and approved-model inventory.
Test on the exact editor versions and operating systems used by the team. A fallback that works on one engineer’s machine is not a team capability until another person can reproduce it from the runbook.
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Assign an owner to check provider documentation, extension compatibility and account policy periodically. Do not assume that an endpoint remains available indefinitely. GitHub announced that GitHub Models—including its playground, model catalog, inference API and BYOK endpoints—was fully retired on July 30, 2026. It should not be used as a current fallback. This retirement is a practical reminder to revalidate every emergency dependency instead of treating a successful setup as permanent.
Close the incident with useful evidence
When service returns, do not immediately delete the temporary configuration. Record:
- UTC start and end times.
- The affected provider, model and editor feature.
- Status-page information and local diagnostic results.
- The fallback selected and the tasks it supported.
- Tasks that remained blocked because of missing tools or context.
- Any credential, privacy or policy exception used.
Turn each gap into a concrete action: add a model, update an extension, document a command, change an approval rule or remove an obsolete endpoint. The objective is not to make the startup independent of AI; it is to ensure that AI unavailability does not stop engineering work.
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