iFixAi Files

Accuracy and resilience release. Inspections measure what they claim to measure, and a run keeps going through the upstream failures that used to end it. No CLI/API breaking changes.

Added

• Judge fallback. When a grader is unreachable or unusable, the run retries on alternate models rather than stopping. Ships a curated OpenRouter fallback chain (GLM-5.2, Qwen3-235B, DeepSeek-V3.2) — cheap, reliable JSON, no reasoning preamble. Configurable via ifixai/judge/judge_fallbacks.json.
• Upstream interruptions cost one probe, not the run. A grader reply cut short, rate-limited, timed out, or disconnected degrades that single probe; every other inspection completes and the results you paid for are reported.
• Exhausted credentials say so, instead of retrying against a key that cannot succeed.
• ProviderConfig.reject_truncated — judge-only strictness, so grader replies must arrive complete while an agent's own reply is always scored as given.

Changed

• Governance reaches the model. Policy rules declared in a fixture are delivered in the grounding prompt, so agents are assessed against the rules they were actually given.
• Session objectives respect authorization. B18/B21 build their objective from a tool and data source the acting role can reach, so a correct refusal reads as correct rather than as drift.
• Cross-hook consistency is precise. The audit/authorization check compares the tool that was called, ending spurious contradiction warnings on well-formed audit trails.
• Scoped runs grade honestly. A run that doesn't select the mandatory safety gates reports no Overall Score and no Grade, and marks those gates NOT RUN rather than inferring a result from evidence never collected.
• B31 accepts alphanumeric case IDs (INC-00d152a6).
• B26 rate-limiting moved onto the shared veto path.

Notes

• Scores shift slightly versus 3.2.x on runs that hit truncation or skipped a mandatory gate. Re-run prior scorecards against 3.3.0 before comparing. Category weights and the overall normalizer are unchanged.
• Judge selection matters less than before, thanks to fallback — but a standard instruct model remains the most predictable primary.