Featured Project~12 hrs

Autonomous Agentic Data Pipeline

Design AI agents capable of orchestrating complex data workflows, writing their own SQL queries to fix pipeline failures autonomously.

4 Parts/5 Agents/4+ Data Sources

agentic-de / multi-agent-pipeline

INGEST

PostgreSQL

REST APIs

S3 Files

Kafka

→VALIDATE

Schema Check

Anomaly Detect

Quality Score

Remediation

→TRANSFORM

Business Logic

Schema Evolve

Dedup

Enrichment

→ORCHESTRATE

Supervisor

Checkpoint

Recovery

Alerting

fig 1 — multi-agent data pipeline

AGENTS

Autonomous Workers

SOURCES

Data Connectors

STATE

Redis

Shared Checkpoint

OBSERVE

Traces

LangSmith Integration

What You'll Build

A production-ready multi-agent system that automates data pipeline operations with intelligent decision-making and self-healing.

Ingestion Agent

Extracts data from PostgreSQL, REST APIs, S3, and Kafka with retry logic, connection pooling, and error handling

Quality Agent

Validates schemas, detects anomalies, scores data quality, and autonomously decides on remediation strategies

Transform Agent

Applies business logic, handles schema evolution, deduplication, and data enrichment with rollback support

Supervisor Agent

Orchestrates all worker agents, manages checkpointing, handles recovery, and escalates via alerting

Curriculum

4 parts, each with a clear checkpoint. Build incrementally, test as you go.

Agent Foundation

3 hours

Set up LangGraph, create base agent classes, implement tool framework, and configure state management with Redis checkpointing.

Base agent responds to tool calls

Tool Implementation

3 hours

Build production-ready tools for database queries with connection pooling, API clients with retry logic, and data validation pipelines.

Tools pass integration test suite

Multi-Agent Pipeline

4 hours

Connect agents into a supervised multi-agent data pipeline with orchestration, error recovery, and checkpointing for resilience.

Supervisor orchestrates end-to-end run

Production Deployment

2 hours

Add LangSmith observability, structured logging, alerting, Docker containerization, and Kubernetes deployment manifests.

Deployed with traces in LangSmith

Technical Standards

Production patterns you'll implement across the multi-agent pipeline.

AUTONOMY

5agents

Supervisor-worker pattern with LangGraph — agents decide routing, retry, and escalation autonomously

OBSERVABILITY

100%traced

Full LangSmith tracing on every agent step, structured logging, and real-time alerting

RESILIENCE

Redischeckpoint

Shared state with Redis checkpointing — recover from any failure without data loss

Environment Setup

Spin up the agent stack and run your first supervised pipeline execution.

agentic-de

# Clone the project & launch agent stack
$ git clone https://github.com/aide-hub/agentic-de.git
$ cd agentic-de

# Start PostgreSQL + Redis + LangSmith
$ docker-compose -f docker-compose.agents.yml up -d

# Run the multi-agent pipeline
$ python -m agents.supervisor run \
$   --source postgres --target snowflake \
$   --checkpoint redis --trace langsmith

Tech Stack

LangGraphLangChainGPT-4PostgreSQLRedisLangSmithDockerPytest

Prerequisites

Python 3.10+ (async/await, decorators, type hints)
Basic understanding of data pipelines (ELT/ETL)
REST API concepts (HTTP methods, JSON, auth)
Docker basics (containers, compose files)

Related Learning Path

Master agentic workflows, LangGraph patterns, tool design, and multi-agent orchestration before diving into this project.

Agentic Workflows Path

New to agentic workflows? Read the complete guide covering agents, tools, supervisor patterns, and LangGraph.

What are Agentic Workflows? — Full Guide

Ready to build your agent system?

Start with Part 1: Agent Foundation