Atlas reports a server error and retries because parseStreamError treats the provider codes server_is_overloaded and server_error as a single retryable api_error, with isRetryable set to true. The first thing to do is wait and let Atlas retry, since the error is explicitly marked retryable. Check the provider status page before assuming a config problem, reduce concurrency because parallel subagents launched with the task tool multiply request volume, and fall back to a second configured provider with `atlas models` if the outage persists.
Why does Atlas retry when the provider returns a 5xx
Atlas retries a provider 5xx because parseStreamError treats the provider codes server_is_overloaded and server_error as a single retryable api_error, with isRetryable true. The provider is failing on its own side, and a repeat attempt genuinely can succeed, so Atlas tries again automatically.
The retry is a considered decision, not a fallback. parseStreamError collapses server_is_overloaded and server_error into one retryable api_error and preserves the provider's own message when it supplies one, so you still see what the provider actually said. Because isRetryable is true, Atlas keeps the run alive and attempts the request again rather than handing you an error to act on. That is the correct behavior for a transient server-side failure, and it is the reason a provider blip often resolves itself in an Atlas session without you touching anything.
How do I fix a provider server overloaded error in Atlas
Step 1 of the documented fix is to wait and let Atlas retry, because the error is explicitly marked retryable. server_is_overloaded and server_error are provider-side conditions, and most of them clear without any change on your machine, so a retry costs nothing but time.
Resist the urge to start editing configuration. A retryable api_error tells you the provider accepted your identity, accepted your request, and then failed to serve it. Nothing about your credential, your atlas.json, or your model choice is implicated. Give the automatic retries time to land. If the run recovers, the outage was transient and there is nothing to fix. If retries continue failing over a longer period, move to the provider status page, then to concurrency, then to a fallback provider, in that order.
How to tell a provider outage from an Atlas config problem
Step 2 of the documented fix is to check the provider status page before assuming a config problem. A retryable api_error mapped from server_is_overloaded or server_error means the provider failed to serve a request it already accepted, which is a different signal from an authentication rejection or a malformed provider entry.
The status page settles the question in seconds and prevents hours of misdirected debugging. A declared incident on the provider side means the correct action is to wait or to fall back, not to reconfigure Atlas. If the status page shows everything healthy while your requests keep hitting server_is_overloaded, the load may be regional or specific to the model you are calling, and the concurrency question below becomes the more likely explanation. Either way, reading the status page first is the cheapest diagnostic available for a 5xx.
Do parallel subagents cause provider overload errors in Atlas
Step 3 of the documented fix is to reduce concurrency. Parallel subagents launched with the task tool multiply request volume in Atlas, which makes server_is_overloaded more likely, because Atlas fans out work to subagents that run in the foreground or in parallel background sessions and each one issues its own provider requests.
Reducing concurrency is a real fix, not just a courtesy to the provider. A fan-out that launches many subagents at once turns a single logical task into many simultaneous provider calls, and a provider that would comfortably serve a serial workload starts returning server_is_overloaded under that burst. Cut back the number of parallel subagents you launch with the task tool and the request rate drops accordingly. This is the one lever on the client side that reliably changes a 5xx rate, because it is the one thing about the request pattern that you actually control.
How to fall back to a second provider during an outage
Step 4 of the documented fix is to fall back to a second configured provider with `atlas models` if the outage persists. Atlas lets you switch the active model and provider on the fly with favorites and recents, so a provider stuck returning server_error does not have to end your session.
The fallback is the right move once the retries have clearly stopped helping and the provider status page confirms a real incident. `atlas models` moves the active session to another configured provider immediately. To make that possible you need a second provider configured before you need it, which is worth setting up while things are calm rather than during an outage. The fallback is a workaround, and it is honest to call it one: the original provider will keep returning server_is_overloaded until the provider fixes its own capacity problem.
How to verify the server overload error has cleared
Verify that a provider overload has cleared by sending 1 request to the original provider and watching for a clean completion with no retry activity. A reply that lands on the first attempt means parseStreamError saw no server_is_overloaded or server_error code to map into a retryable api_error.
The signal to look for is the absence of retries, not merely a successful answer. A run that eventually succeeds after several automatic retries tells you the provider is still degraded and Atlas is working around it. A run that completes immediately tells you the incident is genuinely over. If you switched away with `atlas models` during the outage, point the session back at the original provider before testing, since a clean run on the fallback provider says nothing about whether the first one recovered.
How to fix it
- 01Wait and let Atlas retry. The error is explicitly marked retryable.
- 02Check the provider status page before assuming a config problem.
- 03Reduce concurrency: parallel subagents launched with the task tool multiply request volume.
- 04Fall back to a second configured provider with `atlas models` if the outage persists.
Frequently asked questions
- how to fix provider server overloaded in atlas
- Wait and let Atlas retry, since the error is explicitly marked retryable. Check the provider status page before assuming a config problem, reduce subagent concurrency, and fall back to a second configured provider with `atlas models` if the outage persists.
- does atlas retry 5xx errors automatically
- Yes. parseStreamError treats the provider codes server_is_overloaded and server_error as a single retryable api_error with isRetryable true, so Atlas retries the request automatically rather than stopping the run.
- what is server_is_overloaded in atlas
- server_is_overloaded is a provider error code that Atlas maps to a retryable api_error, alongside server_error. Atlas preserves the provider's own message when it supplies one, and retries the request.
- do parallel subagents in atlas cause rate problems
- Parallel subagents launched with the task tool multiply request volume in Atlas, which makes server_is_overloaded more likely. Reducing the number of subagents you fan out to lowers the request rate and the 5xx rate with it.
- atlas keeps retrying and failing on a provider, what should I do
- Check the provider status page. Persistent retries against server_is_overloaded or server_error usually mean a real provider incident. Fall back to a second configured provider with `atlas models` while it is resolved.
- is a 5xx from my provider an atlas configuration problem
- No. A retryable api_error mapped from server_is_overloaded or server_error means the provider accepted the request and then failed to serve it, so your credential and atlas.json are not implicated.
- how do I know the provider outage is over in atlas
- Send a request to the original provider and watch for a clean completion with no retry activity. Success only after several automatic retries means the provider is still degraded and Atlas is working around it.
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