Serverless Orchestration Engine for Autonomous Operations

The need for a robust, scalable, and intelligent system to execute complex, multi-dependency workflows that exceed the capabilities of off-the-shelf automation tools. Existing solutions (Zapier, Make, n8n) lack the sophistication for true enterprise-grade orchestration: dynamic dependency resolution, sophisticated error recovery, and stateful execution across distributed systems.

The Architecture

The system is built on an event-driven, blueprint-based architecture. It leverages a multi-worker, serverless design using AWS Lambda for actions, ensuring both infinite scalability and minimal operational cost. The engine interprets declarative blueprints (workflow definitions) and orchestrates execution across distributed compute resources.

Orchestration Engine Architecture

Core Design Principles:

Key Technical Features

Multi-level Dependency Management

Supports sequential, parallel (fan-out), and conditional dependencies. The orchestrator builds a directed acyclic graph (DAG) from blueprints and executes nodes based on dependency resolution.

Production-Grade Error Handling

Configurable retry logic with exponential backoff, dead-letter queues for failed actions, and automated rollback mechanisms for critical workflows.

Persistent State Management

PostgreSQL-backed state store tracks every workflow execution, action result, and system event. Enables complete audit trails and post-mortem analysis.

CI/CD Pipeline Integration

Automated deployment via GitHub Actions. Every blueprint change triggers automated testing, linting, and deployment to staging/production environments.

AI-Powered Decision Making

Integrated RAG (Retrieval-Augmented Generation) module for dynamic decision-making. The engine can query knowledge bases and make intelligent routing decisions mid-workflow.

Stateful & Idempotent Execution

Ensures workflows can be safely retried and recovered, preventing duplicate operations in critical systems.

Real-time Monitoring & Observability

Custom dashboard for live workflow tracking, performance metrics, and alerting. CloudWatch integration for infrastructure-level monitoring.

Technology Stack

Core: Python (FastAPI, Pydantic, SQLAlchemy)
Infrastructure: AWS Lambda, S3, EventBridge, SQS, RDS (PostgreSQL)
Orchestration: Custom-built DAG execution engine
CI/CD: GitHub Actions, Docker
AI Integration: OpenAI API (GPT-4), LangChain for RAG
Monitoring: CloudWatch, custom dashboards

Why This Matters

This is not a proof-of-concept. This is production-grade infrastructure designed to handle mission-critical workloads. The architectural patterns used—serverless compute, event-driven design, stateful execution—are the same ones proven at scale by companies like Airbnb (Airflow), Uber (Cadence), and Netflix (Conductor). I built this from first principles, demonstrating deep systems thinking and architectural competence.

Autonomous Digital Asset Venture

System Architecture

Process Flow Diagram for the Autonomous Venture

Technical Highlights

Hybrid Low-code/Pro-code Architecture

Leveraged n8n for rapid API orchestration while using custom Python and Playwright scripts for complex transformations where low-code tools failed.

"Last-Mile" Automation

Successfully automated marketplace deployment using custom browser automation, achieving true end-to-end automation without manual intervention.

Scalable Asset Management

Utilized AWS S3 for cost-effective storage, allowing the portfolio to scale to thousands of products without infrastructure constraints.

Business Impact

5,000+
Digital Assets Managed
95%
Automation Rate
< 3 Hours
Weekly Human Oversight

Technology Stack

Orchestration: n8n (workflow management), Custom Python scripts
AI Services: OpenAI API (GPT-4 for content generation)
Storage: AWS S3 (asset storage), Supabase (PostgreSQL)
Automation: Playwright (browser automation), Google Sheets (data queue)
Infrastructure: Google Drive (templates), Digital Marketplace APIs
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