Este documento detalla el diseño técnico que propuse y desarrollé para un nuevo microservicio de automatización, desde su concepción hasta su plan de implementación, como parte de una arquitectura de microservicios existente en Google Cloud Platform (GCP).

1. Overview & Objectives

Overview

This document outlines the technical design for the AutoEngage service, a new microservice to be built within the existing backend monorepo.

The mission of AutoEngage is to provide users with a powerful tool that automatically monitors relevant social media posts (initially LinkedIn & X) and generates high-quality, AI-driven comments to boost their engagement and social visibility. The service will offer different execution modes, including a manual review queue, to ensure user control and safety.

Objectives

The primary goals of this design document are to:

• Define the system architecture and how the AutoEngage service fits into the existing GCP-based microservices ecosystem.
• Specify the proposed data models, API endpoints, asynchronous worker design, and necessary third-party integrations.
• Document the safeguards and compliance measures required to ensure the service operates reliably and avoids spam-like behavior.
• Provide a detailed implementation plan with clear milestones, potential risks, and a strategy for observability.

2. System Architecture & Data Flow

2.1. Role within the Existing Architecture:

The AutoEngage service will be a new microservice within the backend monorepo. Its primary responsibility is to autonomously monitor social media platforms, generate AI-driven comments, and execute engagement actions based on user-defined watchlists.

2.2. High-Level Data Flow Diagram:

graph TD
    subgraph "Orchestration by autoengage-service"
        A(autoengage-service) -- "Call API with Watchlist" --> B[internal-scraper-service];
        B -- "Return Scraped Posts" --> A;
        A -- "Call API with Post Content" --> C[internal-ai-service];
        C -- "Return Generated Comment" --> A;
        A -- "Save Comment" --> D[(Database / BigQuery)];
    end

2.3. GCP Services to be Used:

• Cloud Scheduler: To trigger the workflow periodically.
• Cloud Tasks: To manage individual scraping jobs, providing retries and rate limiting.