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Gemini Executive Synthesis

Inconsistent error classification and retry logic for transient HTTP errors when interacting with AI providers (e.g., DashScope).

Technical Positioning
Robustness, reliability, and fault tolerance in multi-provider AI agent operations. The system aims for "Token-Efficient AI Agent with same budget, higher intelligence density," which requires stable interaction with underlying LLM providers.
SaaS Insight & Market Implications
This issue highlights a critical flaw in OpenSquilla's error handling and retry mechanism, directly impacting its reliability when integrating with external AI providers. The discrepancy between `provider/failures.py` and `engine/fallback.py` means transient network issues, which are common with third-party APIs, are not being retried. This leads to premature task termination and wasted compute cycles, undermining the "token-efficient" promise. For a B2B SaaS agent platform, consistent and robust retry logic is non-negotiable for production stability and developer trust. Failing to automatically recover from transient errors forces manual intervention, increasing operational overhead and diminishing the value proposition of an automated agent. This requires immediate architectural alignment to ensure `TRANSPORT_TRANSIENT` errors are correctly handled by `FallbackPolicy`.
Proprietary Technical Taxonomy
transient HTTP request errors UNKNOWN max_provider_retries DashScope provider/failures.py ProviderFailureKind.TRANSPORT_TRANSIENT engine/fallback.py FallbackPolicy.classify_error

Raw Developer Origin & Technical Request

Source Icon GitHub Issue May 14, 2026
Repo: opensquilla/opensquilla
[Feature]: Transient HTTP request errors are classified as UNKNOWN and skip max_provider_retries

### Problem

Hi! While running batches against DashScope, I noticed turns sometimes
terminate immediately on a single transient HTTP error (e.g. `Request error: Server disconnected without sending a response`), without the
configured `max_provider_retries` taking effect.

There appear to be two classifiers that disagree:

- `provider/failures.py` maps `"request error"` / `"timeout"` to
`ProviderFailureKind.TRANSPORT_TRANSIENT`.
- `engine/fallback.py` `FallbackPolicy.classify_error` only recognizes
`RATE_LIMIT`, `AUTH_FAILURE`, `OVERLOADED`, `CONTEXT_OVERFLOW`;
anything else returns `UNKNOWN`, and `should_retry` returns `False`.

So `httpx.RequestError` from `provider/openai.py` ends up as `UNKNOWN`
and skips retry.

Is the split intentional? If so, is there a recommended way to have
`FallbackPolicy` honor `TRANSPORT_TRANSIENT` so transient errors
flow into `max_provider_retries`?

Thanks!

### Proposed behavior

Transient transport errors should be retried according to `max_provider_retries`.

Specifically, errors classified by the provider layer as
`ProviderFailureKind.TRANSPORT_TRANSIENT` should not become `UNKNOWN` in
`FallbackPolicy`; they should be considered retryable by `should_retry`.
Non-transient and truly unknown errors can keep the current behavior.

### Area

CLI

### Alternatives considered

I tried increasing `max_provider_retries`, but it does not help because the
error is classified as `UNKNOWN` before retry logic runs.

Developer Debate & Comments

No active discussions extracted for this entry yet.

Adjacent Repository Pain Points

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Frequently Asked Questions

Market intelligence mapped to Inconsistent error classification and retry logic for transient HTTP errors when interacting with AI providers (e.g., DashScope)..

What problem does Inconsistent error classification and retry logic for transient HTTP errors when interacting with AI providers (e.g., DashScope). solve?
Based on our AI analysis of the original developer request, its primary technical positioning is: Robustness, reliability, and fault tolerance in multi-provider AI agent operations. The system aims for "Token-Efficient AI Agent with same budget, higher intelligence density," which requires stable interaction with underlying LLM providers.
Which technical concepts are associated with Inconsistent error classification and retry logic for transient HTTP errors when interacting with AI providers (e.g., DashScope).?
Our proprietary extraction maps Inconsistent error classification and retry logic for transient HTTP errors when interacting with AI providers (e.g., DashScope). to adjacent architectural concepts including transient HTTP request errors, UNKNOWN, max_provider_retries, DashScope.

Engagement Signals

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Issue Status

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like RATE_LIMIT and transient HTTP request errors by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.