Research Findings: AI-Assisted DevSecOps Workflows

Mon, 01 Jan 0001 00:00:00 +0000

Comprehensive research summary conducted on April 23, 2026
Research scope: LLM frameworks, shell AI assistants, DevSecOps integration patterns

Executive Summary
Framework Landscape
oh-my-opencode-slim Deep Dive
Comparative Analysis
Security Research
DevSecOps Integration Patterns
Cost Analysis
Implementation Recommendations
Research Methodology
Sources & References

Executive Summary

Key Findings

Multi-agent orchestration (oh-my-opencode-slim) provides the best balance of quality, cost, and specialization for complex DevSecOps workflows
Three paradigms have emerged: Orchestrated Multi-Agent, Single-Agent Pair Programming, and CLI Command Generation
Security-first integration is critical - AI assistants require strict controls around command execution, secret handling, and audit logging
Cost optimization through intelligent model routing can reduce AI spend by 60-80% for routine tasks
MCP (Model Context Protocol) standardization is enabling better tool integration across frameworks

Research Scope

Domain	Coverage
AI Assistant Frameworks	6 primary tools analyzed
Shell Integration Tools	4 tools evaluated
Security Patterns	25+ controls identified
DevSecOps Use Cases	15+ scenarios documented
Cost Models	Per-provider pricing analyzed

Framework Landscape

Primary Frameworks Identified

1. oh-my-opencode-slim

Repository: alvinunreal/oh-my-opencode-slim
Stars: 3.3k
Language: TypeScript
License: MIT
Type: Multi-agent orchestration plugin
Status: Active, mature

Core Innovation: Instead of forcing one model to do everything, route each part of the job to the agent best suited for it, balancing quality, speed, and cost.

Research on AI-Assisted DevSecOps Workflows

Research Findings: AI-Assisted DevSecOps Workflows

Table of Contents

Executive Summary

Key Findings

Research Scope

Framework Landscape

Primary Frameworks Identified

1. oh-my-opencode-slim