Composable Multi-Tool Pipelines for AI Agents in DevSecOps: Architecture, Orchestration, and Evaluation

Abstract

The integration of AI agents into DevSecOps workflows represents a paradigm shift from static au-
tomation to autonomous, goal-driven systems capable of complex reasoning and adaptation. This paper investigates
the design and implementation of composable multi-tool pipelines for AI agents in DevSecOps environments, focus-
ing on architectural patterns, orchestration strategies, and evaluation methodologies. We propose a novel hierarchical
orchestration model that balances centralized control with specialized execution, enabling dynamic workflow recon-
figuration based on real-time context. Our analysis of orchestration patterns reveals that hierarchical supervision
achieves optimal scalability and fault tolerance for complex DevSecOps workflows. Through case studies in automated
security remediation and intelligent CI/CD optimization, we demonstrate the practical benefits of agent-driven
automation. We introduce a multi-dimensional evaluation framework incorporating performance, security, and hu-
man trust metrics, addressing the critical gap between technical capability and real-world adoption. The proposed
architecture and evaluation methodology provide a foundation for deploying trustworthy, efficient, and scalable
AI-driven DevSecOps systems in enterprise environments.