trust-reliability-loop-lite

Scope automation to narrow workflows like ops_daily_brief or CI alerts, log every candidate action and outcome, and require confirmation before any external write. Run the trust_reliability_loop_gate.py script to verify compliance.

Eval-first logging records every candidate, action, correction, and outcome for each workflow. This data lets you measure and optimize precision per workflow instead of trusting model outputs blindly.

Tiered inference routes decisions through escalating stages: rules first, then classification, extraction, premium models, and finally human confirmation. Cheaper deterministic checks handle easy cases before expensive model calls.

Avoid autonomy for unconfirmed external writes, do-anything chat, continuous always-on reasoning, and unbounded context windows. Use recommend-first mode with progressive autonomy granted per action class only after eval data supports it.

Provenance via source_url lets reviewers verify where each alert claim originated, which is essential for maintaining precision over coverage under an alert budget. Without it, false positives erode trust in the system.