Agentic-WebOps

Agentic-WebOps is a full-stack AI-powered automation platform designed to reduce DevOps friction, remove repetitive work, and automate microservice deployment end-to-end .

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Inspiration

Modern DevOps workflows are powerful but painfully complex — engineers constantly juggle Dockerfiles, CI/CD YAMLs, Kubernetes manifests, GitHub Actions failures, and manual fixes. Small mistakes like YAML indentation or missing Docker tags can break entire pipelines.

Agentic-WebOps was inspired by the idea of the Agentic Web: intelligent AI agents that don’t just answer questions, but take action. The goal was to eliminate repetitive DevOps toil by letting developers describe infrastructure and deployments in plain English — and let AI agents handle everything end-to-end.

What it does

Agentic-WebOps is an AI-driven DevOps orchestration platform that automates the full lifecycle of microservice deployment.

From a single natural-language prompt, it can:

  • Generate Dockerfiles (if missing)
  • Build and push Docker images to DockerHub
  • Generate or fix Kubernetes deployment YAMLs
  • Create or patch GitHub Actions CI/CD workflows
  • Commit and push changes using GitOps principles
  • Trigger CI/CD pipelines automatically
  • Fetch and summarize GitHub Actions logs

Example prompt: Deploy microservice billing-service, port should be 4000

The system autonomously moves from code → container → CI/CD → Kubernetes cluster.

How we built it

The platform is built as a multi-agent system with clear separation of responsibilities:

1. Frontend (Next.js)

  • A chat-based UI where users give DevOps instructions in natural language
  • Built with Next.js and Tailwind CSS
  • Communicates with backend via REST APIs

2. Backend Orchestrator (FastAPI)

  • Acts as the central brain of the system
  • Receives user input and forwards it to a Router Agent
  • Coordinates multiple specialized AI agents

3. Agentic Architecture

Each agent handles one DevOps responsibility:

  • Router Agent → decides which agent(s) should handle the request
  • Deployment Agent → Dockerfile creation, image build & push
  • YAML Agent → Kubernetes YAML generation & fixes
  • DevOps Agent → GitHub Actions CI/CD workflow generation
  • GitOps Fixer Agent → commits and pushes changes using GitPython
  • Log Fetcher Agent → fetches GitHub Actions logs
  • Feedback Loop Agent → validates outputs and self-corrects errors

4. GitOps + CI/CD Automation

  • Automatically writes .github/workflows/deploy.yml
  • Fixes YAML syntax and indentation issues
  • Pushes changes to GitHub
  • Triggers CI/CD pipelines
  • Applies Kubernetes manifests and waits for rollout success

Challenges we ran into

  • Reliable YAML generation: Preventing indentation and schema errors in Kubernetes and GitHub Actions YAMLs
  • Agent coordination: Ensuring agents collaborate without conflicts or duplicated actions
  • Error handling: Designing feedback loops so agents can self-correct failed deployments
  • End-to-end automation: Making the pipeline robust from prompt to production rollout

Accomplishments that we're proud of

  • Built a fully autonomous DevOps pipeline driven entirely by natural language
  • Implemented true multi-agent collaboration, not just a single LLM
  • Automated Docker, CI/CD, GitOps, and Kubernetes together in one system
  • Created a developer-friendly chat interface for complex infra operations
  • Designed the platform to be modular and extensible for future agents

What we learned

  • Agentic AI systems are far more powerful when agents have clear, narrow responsibilities
  • Feedback loops are essential for reliable automation
  • DevOps automation isn’t just about code — state management and validation matter
  • GitOps is a natural fit for AI-driven infrastructure changes
  • LLMs become significantly more useful when paired with real tools (Git, Docker, CI/CD APIs)

What's next for Agentic-WebOps

Planned future enhancements include:

  • Autonomous testing agents (unit, integration, regression)
  • Multi-cloud deployment agents (AWS, GCP, Azure)
  • Terraform and Helm chart generation agents
  • Security agents (SAST, dependency scanning, Trivy)
  • Canary, blue-green, and rollback deployment agents
  • Observability agents (Prometheus, Grafana dashboards)
  • CI/CD optimization and workflow intelligence
  • PR review, linting, and dependency upgrade agents

The long-term vision is a fully autonomous DevOps AI platform capable of managing an organization’s infrastructure with minimal human intervention.

Technologies and Tools Used

AI & Agent Framework

  • Large Language Models (via Groq API)
  • Custom multi-agent orchestration
  • Feedback-loop based self-correction

Backend

  • Python
  • FastAPI
  • GitPython
  • Uvicorn
  • uv (modern Python package & venv manager)

Frontend

  • Next.js
  • React
  • Tailwind CSS
  • NextAuth (GitHub OAuth)

DevOps & Cloud

  • Docker & DockerHub
  • GitHub Actions
  • GitHub API
  • Kubernetes
  • GitOps workflows

Infrastructure & Tooling

  • REST APIs
  • Environment-based configuration
  • CI/CD automation
  • YAML generation and validation

Agentic-WebOps turns DevOps from a manual engineering task into an autonomous AI-driven workflow.

Built With

  • ai-agents
  • ci/cd-automation
  • docker
  • dockerhub
  • environment
  • fastapi
  • github-actions
  • github-api
  • github-oauth
  • gitops-workflows
  • gitpython
  • kubernetes
  • large-language-models-(via-groq-api)
  • microservices-architecture
  • next.js
  • nextauth.js
  • python
  • rest-apis
  • tailwind-css
  • uv-(python-package-and-venv-manager)
  • uvicorn
  • variable
  • yaml-generation-and-validation
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