Self-Serve Agent Improvement Loop
Automatically analyze 30-day agent performance trends and generate prioritized improvement recommendations.
Overview
Every week, an AI agent analyzes each agent's 30-day performance data, correlates it with failure logs, identifies patterns, and generates a prioritized list of improvements. The system writes recommendations directly to each agent's feedback file and flags level-up candidates when performance consistently exceeds targets.
How It Works
A weekly job pulls performance data from monthly JSONL files, calculates trends (improving, stable, declining), cross-references failures.jsonl for recurring issues, and generates markdown reports with High/Medium/Low priority recommendations. Each agent's memory/feedback.md is auto-updated with actionable next steps. The system also detects when an agent meets level-up criteria and notifies leadership.
Tools Used
Outcome
Generated 12 improvement reports in first month. Identified 2 agents ready for level-up (L1→L2) based on sustained performance above targets. Recommended 18 actionable improvements, of which 14 were implemented.