DevSecOps Architecture with AI Assistants
System design patterns, agent responsibilities, and integration architecture for AI-assisted DevSecOps.
Documentation
Browse the sections below to learn about AI-assisted DevSecOps workflows, including architecture, frameworks, security, paradigms, use cases, and research.
Welcome to the AI-Assisted DevSecOps Workflows documentation. This guide is organized into focused sections that cover the full spectrum of integrating LLM assistants into security-first development and operations.
Start Here
New to AI-assisted DevSecOps? We recommend reading in this order:
- Architecture — Understand the agent pantheon, system design patterns, and how specialized AI agents map to DevSecOps responsibilities.
- Security — Review the threat model, critical controls, and hardening guides before deploying AI assistants in production.
- Paradigms — Learn the three primary approaches (Multi-Agent, Pair Programming, CLI) and when to use each.
- Frameworks — Compare oh-my-opencode-slim, Aider, ShellGPT, AIChat, Claude Code, and Crush.
By Topic
| Section | What You’ll Learn |
|---|---|
| Architecture | Agent responsibilities, workflow patterns, integration design |
| Frameworks | Tool comparisons, strengths, weaknesses, installation |
| Security | Threat model, prompt injection prevention, secret handling, compliance |
| Paradigms | Multi-agent orchestration, pair programming, CLI generation |
| Use Cases | Incident response, IaC security, secret management, CI/CD patterns |
| Secure PR Review | Pre-PR AI-assisted security review workflow |
| Research | Framework landscape, cost analysis, implementation recommendations |
| DevOps Stack | Terraform, Kubernetes, Ansible, Prometheus, Grafana, Loki, ELK workflows |
| Python Developer | Modern Python development with uv, Ruff, Pytest, FastAPI/Django, Docker |
Quick Reference
- Agent Pantheon: Orchestrator, Explorer, Librarian, Observer, Oracle, Fixer, Designer, Council
- Core Principle: Security-first — verify before execute, least privilege, audit trail
- Primary Tools: oh-my-opencode-slim, opencode-go/crush, Aider, ShellGPT, AIChat
This is a living document. Contributions are welcome — see Contributing for guidelines.
LLM Assistant Frameworks: Comprehensive Comparison
Detailed comparison of oh-my-opencode-slim, Aider, ShellGPT, AIChat, Claude Code, and Crush for DevSecOps workflows.
Security Guide: AI-Assisted DevSecOps
Threat model, critical security controls, compliance considerations, attack scenarios, and hardening guides for AI-assisted DevSecOps.
Paradigms: Approaches to AI-Assisted DevSecOps
Three primary paradigms for integrating AI assistants into DevSecOps: Orchestrated Multi-Agent, Single-Agent Pair Programming, and CLI Command Generation.
Practical Use Cases & Patterns
Practical examples and patterns for incident response, IaC security, secret management, compliance, container security, CI/CD, and threat hunting.
Secure PR Review Workflow
Run a 5-step AI-assisted security review over your branch changes before they reach code review.
Research Findings: AI-Assisted DevSecOps Workflows
Comprehensive research findings on LLM frameworks, shell AI assistants, DevSecOps integration patterns, cost analysis, and implementation recommendations.
Modern DevOps Stack: Terraform · Kubernetes · Ansible · Observability
Comprehensive developer workflow guide for Terraform, Kubernetes, Ansible, and full observability with Prometheus, Grafana, Loki, and ELK Stack.
Modern Python Developer: uv · Ruff · Pytest · FastAPI · Docker
Comprehensive developer workflow guide for modern Python development using uv, Ruff, MyPy, Pytest, FastAPI/Django, and secure containerization.