Self-Healing Data Pipeline Platform
The first autonomous system that detects and fixes data pipeline issues in under 30 seconds—eliminating 3am alerts for data teams.
1Problem
Data engineers spend 60% of their time fixing pipeline failures—schema drifts, null spikes, row count anomalies. Traditional tools only detect and alert. Teams still manually write fixes, often at 3am.
2Solution
Built multi-agent AI system where Detective Agent analyzes root cause, Fixer Agent generates production-ready code, and Critic Agent validates safety—all in under 30 seconds.
- →Detective: Root cause analysis + urgency assessment
- →Fixer: SQL/Python code generation + rollback plans
- →Critic: Safety validation + confidence scoring
3Impact
- ✓Reduces resolution from 2-8 hours to <1 minute (95%+ reduction)
- ✓Saves enterprise teams $1.5-2M annually in engineering time
- ✓Deployed on AWS (App Runner + RDS) serving live traffic
- ✓40 production scenarios validated with 100% safety rate
Multi-Agent Architecture
Three specialized AI agents work together to detect, fix, and validate pipeline issues autonomously.
Pipeline Issue Detected
↓
┌───────────────────────────┐
│ DETECTIVE AGENT (GPT-4) │
│ │
│ Analyzes: │
│ • Root cause │
│ • Urgency level │
│ • Context gathering │
│ │
│ Output: │
│ "Schema drift in users │
│ table. Urgency: HIGH" │
└─────────────┬─────────────┘
│
▼
┌───────────────────────────┐
│ FIXER AGENT (GPT-4) │
│ │
│ Generates: │
│ • SQL/Python fix code │
│ • Rollback plan │
│ • Confidence score │
│ │
│ Output: │
│ ALTER TABLE users ADD... │
│ Confidence: 92% │
└─────────────┬─────────────┘
│
▼
┌───────────────────────────┐
│ CRITIC AGENT (GPT-4) │
│ │
│ Validates: │
│ • Syntax correctness │
│ • Safety assessment │
│ • Side effect analysis │
│ │
│ Output: │
│ Safety: 85/100 │
│ Status: Approve │
└─────────────┬─────────────┘
│
▼
Human Review
│
┌───────┴───────┐
▼ ▼
Approve Reject
│
▼
Execute + Audit LogDetective Agent
Analyzes WHY the issue occurred
- • Root cause identification
- • Urgency assessment (LOW/MEDIUM/HIGH)
- • Context gathering from logs
- • Affected pipeline analysis
Fixer Agent
Generates HOW to fix it
- • Production-ready code generation
- • Rollback plan creation
- • Confidence scoring (0-100%)
- • Multiple fix approaches
Critic Agent
Validates it's SAFE to execute
- • Syntax validation
- • Safety scoring (0-100)
- • Side effect analysis
- • Risk assessment
Performance vs Manual Process
Self-Healing Platform
Manual Process
95%+ reduction in time-to-resolution. Weekend alerts eliminated. Data teams freed for strategic ML/AI work.
Platform Capabilities
Schema Drift Detection
Catches column additions, removals, and type changes instantly. When product team adds a new column at 2am, Detective identifies it in <1s, Fixer generates ALTER TABLE statement, and Critic validates no data loss risk.
Null Spike Monitoring
Detects data quality degradation when null values spike unexpectedly. Generates validation rules and data backfill scripts to restore data integrity without manual investigation.
Multi-Agent Safety Validation
Three AI agents cross-validate every fix. Detective finds root cause, Fixer proposes solution, Critic validates safety. Disagreement forces human review—preventing dangerous automated changes.
Production AWS Deployment
Running live on AWS App Runner with RDS PostgreSQL managing 40 pipeline scenarios. Containerized with Docker, full CI/CD, and auto-scaling for production reliability.
Real-World Example: Schema Drift
Scenario
Friday 4:47 PM - Product team ships new feature adding loyalty_tier column to users table. Data pipeline doesn't know about it yet.
❌ Traditional Approach:
- 1. Pipeline fails silently
- 2. Discovered Monday morning (64 hours later)
- 3. Senior engineer investigates (2 hours)
- 4. Write fix, test, coordinate (3 hours)
- 5. Deploy Tuesday afternoon
Weekend ruined • Data stale • Users affected
✅ Self-Healing Platform:
- 1. Detected: 0.8 seconds after change
- 2. Detective analysis: 12 seconds
- 3. Fixer generates code: 18 seconds
- 4. Critic validates safety: 8 seconds
- 5. Human approves via mobile: 2 minutes
Weekend saved • Data fresh • Zero downtime
Research Foundation
OpenAI Residency Application
This platform serves as research foundation for studying multi-agent coordination in autonomous systems and safe AI deployment in mission-critical infrastructure.
- • Novel dataset: 40 real-world (anomaly → fix → outcome) examples
- • Key finding: Multi-agent disagreement improves safety
- • Target venues: NeurIPS, ICML, AAMAS (2026-2027)
Academic Contributions
Open source codebase available for research community. Dataset and findings contribute to advancing safe autonomous systems.
- • "Multi-Agent Disagreement in Autonomous Pipeline Remediation"
- • "Safety Through Specialization: AI Critic Agents"
Try It Yourself
The platform is running live on AWS. Click around, generate fixes, see the multi-agent coordination in action.