Automation Platform

Project Overview

The Automation Platform is a centralized catalog and execution engine that lets teams register, discover, and run reusable automations across environments. Users submit automations with metadata—name, description, target host or IP—and attach the actual scripts or code that execute on the specified systems. A built-in promotion workflow moves automations through e1 (development), e2 (staging), and e3 (production) with configurable approval gates. Every automation integrates automatically with our internal ticketing system for traceability, comes with out-of-the-box monitoring dashboards, and is fully accessible via both a user-friendly web interface and a RESTful API for programmatic management.

Beyond individual automations, users can also build multi-step workflows that chain together existing automations in a defined sequence, complete with success and failure conditions. These workflows are constructed via the web interface and visualized with a slick flowchart view, making it easy to design, review, and share complex operational processes. The result is a self-service automation ecosystem that dramatically reduces duplication, improves change governance, and accelerates operational efficiency.

Key Features

• Centralized Automation Catalog: Submit automations with rich metadata (name, description, target host/IP, tags) for easy discovery and reuse.
• Attachment-Based Execution: Attach scripts or code snippets that execute on specified targets, decoupling catalog entries from implementation details.
• Promotion Pipeline: Built-in lifecycle with e1 (development), e2 (staging), and e3 (production) stages, each gateable by customizable approval workflows.
• Workflow Orchestration: Chain existing automations into multi-step workflows with success and failure conditions, designed via a visual flowchart editor in the web UI.
• Scalable Execution Engine: Leverages Ansible under the hood for reliable, parallel execution across large fleets of servers.
• Web Interface & Visual Flowcharts: Intuitive UI for catalog management and workflow design, complete with drag-and-drop flowchart visualization of automation sequences.
• Monitoring & Alerting: Built-in dashboards for real-time automation status, error rates, and performance metrics, integrated with our internal monitoring system.
• API & Programmatic Access: RESTful API for programmatic management of automations and workflows, enabling seamless integration with other systems.
• Security & Compliance: Built-in security scanning and quality gates, integrated with our internal ticketing system for traceability.
• Scalability & Performance: Designed for high throughput, with built-in load balancing and horizontal scaling capabilities.
• Self-Service Automation: Users can register, discover, and run automations without requiring IT support, empowering teams to manage their own automation needs.

Implementation Details

I architected the Automation Platform’s catalog entirely from scratch, using React to deliver a dynamic, component-driven user interface and Go to power a performant backend API. The catalog’s data model, backed by PostgreSQL, was designed to store rich metadata for each automation—such as name, description, target host or IP, tags, and version history—while execution logic is decoupled into attachments that users upload and manage independently. I implemented a secure secrets integration with HashiCorp Vault so that automations can reference credentials at runtime without ever exposing sensitive data in transit or at rest.

To enable complex orchestration, we built a workflow engine that leverages React Flow in the UI for drag-and-drop flowchart creation and encodes each workflow as a Kubernetes Custom Resource Definition. Workflows chain automations together in sequence, allow for branching on success or failure conditions, and are executed by a Go-based controller that watches the API for new or updated CRDs. Under the hood, Ansible serves as the execution engine, dispatching playbooks or scripts across target fleets in parallel and reporting status back to the platform. We instrumented all services with Prometheus exporters and exposed metrics such as queue depth, execution latency, and success rates. Grafana dashboards were assembled to give operations teams real-time visibility into platform health and workflow progress.

For delivery and reliability, I authored comprehensive CI/CD pipelines in GitHub Actions for each microservice. Pipelines begin by running EarlyBird, our internal security scanner, then execute unit tests and integration tests against a disposable OpenShift cluster spun up on demand. Following test success, artifacts are containerized with Docker and deployed via Helm charts into our OpenShift environments, progressing through development, staging, and production namespaces according to configurable promotion policies. The entire platform sits behind an OAuth-protected API gateway, enforces role-based access controls via LDAP group membership, and maintains an immutable audit log of all user actions and automation executions to meet compliance requirements.

Challenges and Solutions

Building the Automation Platform from the ground up presented several architectural and operational challenges. Designing a flexible yet robust catalog schema that could accommodate varied metadata—targets ranging from IPs to hostnames, version histories, and custom tags—required careful data modeling in Couchbase. I resolved this by implementing a normalized schema with fields for extensibility, which allowed us to evolve metadata without schema migrations. Integrating with HashiCorp Vault to secure credentials posed its own hurdles: ensuring that secrets could be fetched at runtime without blocking execution or exposing data. I addressed this by caching short-lived Vault tokens in memory and leveraging Vault’s dynamic secrets capability, which both improved performance and upheld least-privilege principles.

Orchestrating complex workflows at scale also introduced significant complexity. The React Flow–based UI needed to be intuitive for drag-and-drop sequence creation, yet the underlying controller had to interpret success and failure branches reliably in Kubernetes. To solve this, we defined each workflow as a custom resource and built a Go operator that watches for changes, enforces idempotency, and retries failed steps according to configurable policies. Integrating Ansible as the execution engine required adding parallelism controls and backoff strategies to avoid throttling target systems. Lastly, ensuring end-to-end security and compliance meant embedding audit logging at every layer, enforcing RBAC, and validating each release via GitHub Actions pipelines that ran EarlyBird security scans and on-demand OpenShift test clusters. These solutions collectively yielded a resilient, secure, and user-friendly automation ecosystem.

Results

• Catalog grew to over 3,500 registered automations within the first year, with 70% of entries reused by at least two different teams.
• Reached 100,000 automated runs per month, serving hundreds of internal applications with self-service automation.
• Automated incident workflows cut median time-to-resolution from 45 minutes to under 10 minutes, a 78% improvement.
• CI/CD pipelines reduced average release cycle time by 50%, from 20 minutes down to 10 minutes.
• EarlyBird scans in the GitHub Actions pipelines caught and remediated 100+ security findings before production deployment in the first six months.
• Integration with the ticketing system eliminated manual ticket creation, saving approximately 200 operational hours per quarter.
• The platform achieved 99.99% uptime and maintained a 95%+ successful automation execution rate across all environments.

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