ELMAS enables Gibson to learn and improve from every interaction without retraining. It tracks outcomes (user feedback on responses), accumulates semantic knowledge (facts and patterns extracted from conversations), evolves prompt templates (which phrasings work best), and optimizes routing rules (which agent handles which tasks). The Meta-Analysis uses a 70B model to identify patterns and propose improvements.
| Agent | Total Outcomes | Avg Score | Positive | Negative | Corrections | Success Rate |
|---|---|---|---|---|---|---|
| Agent | Task Type | Name | Uses | Success | Failure | Success Rate | Generation | Status |
|---|---|---|---|---|---|---|---|---|