Autonomous CI/CD Quality Assurance Using LangGraph Multi-Agent Orchestration and Risk-Proportionate Human-in-the-Loop Control
Abstract
This paper presents the K11tech Agentic AI QA System, a LangGraph-orchestrated multi-agent framework that autonomously executes a complete CI/CD quality gate — from pull request analysis to defect filing — without requiring manual QA intervention for routine changes. The system orchestrates 14 specialist AI agents across four pipeline phases, decouples agent logic from external tools via 7 Model Context Protocol (MCP) servers, and incorporates a risk-proportionate Human-in-the-Loop (HITL) gate that suspends execution and requests human review when risk score ≥ 0.85. A self-evaluation layer using DeepEval and RAGAS continuously audits the quality of LLM-generated pipeline outputs.
Fields of Study & Subjects
Advanced Data Storage Technologies
Peer-to-Peer Network Technologies
Music and Audio Processing
Publication details
- Journal / source
- International Journal Of Engineering And Computer Science
- Publisher
- Valley International
- Volume
- 15
- Issue
- 06
- Pages
- 28623–28630
- Publication date
- 1 January 2026