[Story3] Feedback Loop & Continuous Learning Integration

[gapilongo]

Summary
Implement feedback processing pipeline that improves triage/correlation/analysis agent performance using analyst corrections. Build automated retraining triggers, A/B testing framework for model updates, and feedback-driven playbook refinement.
Key Deliverables

Feedback Processor (agents/learning/feedback_processor.py): Async processing of analyst feedback to generate training examples. Process FP/TP corrections into labeled datasets, extract high-confidence analyst decisions for ReAct prompt refinement, identify systematic mispredictions requiring rule updates, and aggregate feedback metrics (inter-analyst agreement, confidence calibration errors).
Model Retraining Pipeline: Automated triggers for retraining when: 100+ new feedback samples accumulated, weekly scheduled retraining, FP rate exceeds threshold (>15%), or critical misclassification detected. Implement shadow testing of new models against current production (2-week observation period, 95% confidence interval for performance improvement).
Playbook Learning (agents/response/playbook_learner.py): Track response action effectiveness from analyst feedback. When analysts modify recommended actions, capture new action sequences, conditions triggering modifications, and success/failure outcomes. Auto-suggest playbook updates requiring manager approval.
Performance Dashboard: Add /api/v1/learning/metrics endpoint showing: model performance trends (precision/recall by week), feedback volume and quality scores, top analyst contributors, FP reduction rate, automated vs. manual decision distribution, A/B test results for model candidates.

Acceptance Criteria

Feedback processed within 5min of analyst submission
Retraining pipeline completes full cycle (data prep, train, test, deploy) in <4hrs
A/B testing safely compares model performance without impacting production decisions
Playbook suggestions show 20%+ action modification rate requiring at least 10 occurrences
Learning metrics dashboard updates daily with trend analysis

[gapilongo]

@claude is this task done ?

[claude]

Claude finished @gapilongo's task —— View job

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Task Status Check

Checking if Story3 implementation is complete...

Todo List

• Read repository guidelines (CLAUDE.md)
• Check for feedback processor implementation
• Check for model retraining pipeline
• Check for playbook learner implementation
• Check for performance dashboard/API endpoint
• Verify acceptance criteria
• Provide status report

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Status Report

No, this task is NOT done.

Based on my analysis of the codebase, none of the required components for Story3 have been implemented yet:

Missing Components

1. Feedback Processor ❌

• Expected file: agents/learning/feedback_processor.py
• Status: Does not exist
• The src/lg_sotf/agents/learning/ directory only contains empty placeholder files (base.py, rag.py, fine_tuning.py)

2. Model Retraining Pipeline ❌

• Status: No implementation found
• No automated triggers, shadow testing, or retraining logic exists

3. Playbook Learner ❌

• Expected file: agents/response/playbook_learner.py
• Status: Does not exist
• The src/lg_sotf/agents/response/ directory has playbook.py but no learning component

4. Performance Dashboard ❌

• Expected endpoint: /api/v1/learning/metrics
• Status: No API endpoints found in the codebase
• No metrics dashboard implementation exists

Acceptance Criteria Status

• ❌ Feedback processed within 5min of analyst submission
• ❌ Retraining pipeline completes full cycle in <4hrs
• ❌ A/B testing framework for model comparison
• ❌ Playbook suggestions showing 20%+ action modification rate
• ❌ Learning metrics dashboard with daily updates

Current State

The project has a basic structure with agent directories (learning, response, etc.) but they contain only empty placeholder files. The entire Story3 feature set needs to be implemented from scratch.

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