Self-Healing Autonomous Data Pipeline Agent

1. Project Title: Self-Healing Autonomous Data Pipeline Agent

1.1 Vision: Imagine a data ecosystem where pipelines never fail—or if they do, they autonomously detect, debug, and resolve issues before any human intervention. The Self-Healing Autonomous Data Pipeline Agent is designed to bring this vision to reality by transforming fragile data workflows into intelligent, resilient systems.

1.2 Problem Statement Modern data engineering pipelines are inherently fragile. Even minor issues—such as: ->Schema changes in CSV files ->Unexpected null values ->Data type mismatches

can lead to complete pipeline failures, resulting in:
     ->Hours of manual debugging
     ->Delayed data availability
     ->Increased operational cost

 This project addresses a critical gap:
       ->Moving from reactive debugging to proactive, autonomous pipeline recovery

1.3 Solution Overview ->The system introduces an AI-powered autonomous agent that not only generates data pipelines but also understands, validates, and self-corrects them before deployment.

1.4 Core Workflow 1. Input User provides: ->Raw CSV dataset ->Plain English transformation requirements 2. Intelligent Data Understanding ->Automatic data profiling ->Schema inference ->Detection of sensitive PII (Personally Identifiable Information) such as: ->Emails ->Phone numbers ->Credit card details 3. Pipeline Generation ->The agent generates: ->ETL scripts (Python-based transformations) ->SQL schemas (structured storage design) ->Airflow DAGs (workflow orchestration)

1.5 The Self-Healing Loop (Key Innovation) Before delivering any output, the system executes a closed-loop validation mechanism: ->The generated pipeline is executed inside a sandbox simulation environment ->If errors occur (e.g., column mismatch, null handling issues): ->The agent captures runtime logs ->Analyzes the failure ->Automatically rewrites the pipeline code

		This cycle repeats until:
			->The pipeline executes successfully with zero errors

1.6 Key Features 1. Autonomous Debugging ->Eliminates manual troubleshooting ->Ensures only validated, working code is delivered 2. Compliance-First Design ->Built-in PII detection and flagging ->Prevents accidental exposure of sensitive data ->Suitable for healthcare, finance, and enterprise use cases 3. Production-Ready Outputs ->Generates complete, deployable packages: ->Dockerized environments ->Airflow scheduling setup ->SQL schemas ->No additional engineering effort required 1.7 Impact ->Reduces pipeline development lifecycle from: ->3 days → 60 seconds ->Eliminates repetitive debugging tasks ->Improves reliability and scalability of data systems

1.8 Tech Stack ->Backend: FastAPI ->Frontend: Next.js ->AI Engine: GLM 5.1 (Zhipu AI) ->Orchestration: Apache Airflow ->Containerization: Docker