Tool Promises Hard Metrics on Whether AI Agents Self-Correct or Just Loop

A developer tool called the Agent Self-Reflection & Sentiment Scanner aims to address a blind spot in LLM observability: determining whether autonomous agents are genuinely learning from errors or silently repeating failed actions. Rather than routing logs through a secondary LLM for analysis, the tool uses exact string matching to detect predefined correction phrases and success markers within execution logs. It calculates a self-correction frequency rate per loop, giving engineers a concrete metric to flag when an agent is stuck cycling rather than progressing. Two core functions — analyze_sentiment and detect_reflection — work together to surface the internal reasoning states that standard API traces typically obscure. The approach is positioned as a cost-efficient alternative to redundant LLM auditing, particularly for high-volume agentic workflows.