Practices for embedding AI agents into enterprise systems [Feedback Flywheel] 💡 Is your agent "deploy and forget"? You need a mechanism to channel production user feedback into a continuous improvement cycle. This pattern systematically collects user feedback, converts it into eval datasets, and drives ongoing quality improvement. 🔥 Problems it solves - No post-deployment quality improvement cycle exists - Quality degradation from model changes, data drift, or new query patterns goes undetected - No data to prioritize which problems to fix first 🏗️ Proposed pattern Collect both implicit signals (retries, abandonment, manual corrections) and explicit signals (thumbs up/down, text feedback, human escalations). Convert low-rated and escalated cases into eval datasets and add them to regression tests. Prioritize improvements by frequency and severity of negative feedback, then apply fixes to prompts, tools, routing, or RAG. Validate all changes through eval gates before redeployment. Since reviewing every interaction is impossible, start by sampling 100% of negative cases and 5-10% of successful ones, then adjust based on measured review capacity and improvement yield. ✅ Selection criteria - Good fit: Every production agent intended for ongoing operation - Not a fit: Throwaway experiments or PoCs ⚠️ Pitfalls - Ignoring implicit signals means you miss quality degradation that users never explicitly complain about - Without an operational process connecting collected feedback to improvement actions, data piles up with no impact - Failing to update golden datasets causes eval gates to become stale and miss new failure patterns 🛠️ Implementation Approach 1. Collect feedback via in-app UI and Slack reactions (thumbs up/down), and automatically detect implicit signals (retries, abandonment, manual corrections) as trackable events 2. Build a pipeline that automatically converts low-rated and escalated cases into eval datasets and adds them to the regression test suite 3. Create a dashboard that visualizes improvement priorities by frequency and severity of negative feedback (start with 100% of negative cases and 5-10% of successful ones as the sampling baseline) 4. Validate all prompt, tool, and routing changes through eval gates (P26) to confirm no quality regression before redeployment 5. Set up an A/B testing infrastructure combining LLM-as-judge automated evaluation with human review to quantitatively measure improvement impact #AIAgents# #EnterpriseArchitecture#