Lifecycle-Aware GenAI Assistance with MCP via Context Refinement Loops: A Reference Architecture
TL;DR
DevCoach is presented as a reference architecture built on the Model Context Protocol to make GenAI assistance more lifecycle-aware, governable, and inspectable in organizational settings and reframes GenAI integration as a software engineering design and governance problem rather than a prompt-level optimization problem.
Abstract
The rapid, often unstructured adoption of Generative AI (GenAI) in software-intensive organizations creates a socio-technical challenge. While GenAI can accelerate individual tasks, these gains are often offset by added coordination, verification, and governance effort across the software development lifecycle (SDLC). This paper presents DevCoach as a reference architecture built on the Model Context Protocol (MCP) to make GenAI assistance more lifecycle-aware, governable, and inspectable in organizational settings. DevCoach treats model outputs as proposals rather than authoritative actions, and couples generation to lifecycle-scoped retrieval, bounded tool use, deterministic validation, and provenance-linked audit artifacts. Its core mechanism is two Context Refinement Loops (CRLs): CRL 1 stabilizes user intent into an explicit retrieval contract, and CRL 2 performs bounded retrieval, evidence verification, and provenance construction to produce a verified context pack. The contribution is therefore architectural and design-oriented rather than a benchmarked implementation study. In this way, the paper reframes GenAI integration as a software engineering design and governance problem rather than a prompt-level optimization problem.