Slack Message: Automated Feedback Loop System
Deployment: Automated Feedback Loop for Agents
📎 Demo: [Add demo link when recorded]
What this is
A system that closes the loop from agent runs → feedback → pattern learning → agent improvements. After you run any agent, you get prompted for structured feedback (2-3 min), which auto-generates run logs, analyzes patterns using “thinking to summary” approach, and shows the impact of learnings. First test: Ticket Creation Agent (Eden Wikipedia data request).
PRD highlights
• Feedback-driven learning — Every agent run prompts for structured feedback (outcome, quality, what worked/didn’t, completeness). Feedback is auto-logged and analyzed for patterns.
• Pattern extraction with confidence levels — Patterns move from LOW (1-2 examples) → MEDIUM (3-4 examples) → HIGH (5+ examples). When patterns reach MEDIUM confidence, PRs are suggested to update agent PRDs.
• Context graph evolution — Each deployment adds new entities (agents, patterns, run logs), relationships (campaign → agent → output), and process steps (traceable workflows). This helps assess PR quality: does it improve process knowledge, not just code?
Process this replaces or creates
• Replaces: Manual, ad-hoc agent improvements based on gut feel. No systematic way to learn from agent runs or track what actually works.
• Creates: Structured feedback loop: Run agent → Prompt feedback → Auto-log → Analyze patterns → Suggest improvements → Update agent PRD → Agent gets smarter.
• Creates: Pattern library (PATTERNS.md) that tracks learned behaviors (e.g., “Ticket titles should not include ‘Linear Ticket:’ prefix”, “All tickets need success criteria and point assignment”).
• Creates: Run log (RUN_LOG.md) that captures traces: which agents ran, what inputs/outputs, what decisions were made, what outcomes occurred. This becomes the foundation for pattern analysis.
Where it lives
In brainforge-vault (PR ready):
• System spec: gtm/agents/AGENT_FEEDBACK_LOOP.md
• Context graph approach: gtm/agents/CONTEXT_GRAPH_APPROACH.md
• Process guide: gtm/agents/FEEDBACK_LOOP_PROCESS.md
• Pattern library: gtm/agents/PATTERNS.md
• Run log: gtm/agents/RUN_LOG.md
• PR quality checklist: gtm/agents/PR_CONTEXT_GRAPH_CHECKLIST.md
• Feedback prompts: gtm/agents/feedback-prompts/
• First test run: gtm/agents/feedback-sessions/ticket-creation-2026-02-05-eden-wikipedia.md
How this evolves our context graph
• New entities: automated-feedback-loop-system, ticket-creation-agent, pattern-library, run-log, context-graph-approach.
• New relationships: Agent runs → feedback sessions → pattern analysis → PR suggestions → agent improvements (traceable via run logs and pattern confidence levels).
• New process step: Run agent → prompt feedback → auto-log → analyze patterns (thinking → summary) → show impact → suggest PRs when patterns reach MEDIUM confidence.
• Enables trace capture: Every agent run is logged with metadata (run ID, inputs, outputs, decisions, outcomes, quality scores). Patterns are extracted and tracked with confidence levels. This creates a learning system where agents improve over time based on actual usage patterns, not assumptions.
First test results (Ticket Creation Agent):
• Time saved: 14.5 minutes (0.5 min agent vs 10-15 min manual)
• Quality: 8/10 (good, with 3 clear improvements identified)
• Patterns identified: 4 patterns (3 fixes: title format, success criteria, point assignment; 1 reinforcement: data source references)
• Impact: 3 PRs ready to create when patterns reach MEDIUM confidence (after 2-3 more runs)
Invite critical feedback: What’s broken, missing, or annoying? What would make you actually use this (or use it more)? Reply in thread or DM me.