Nexora AI: Autonomous SDLC Modernization

Modernizing legacy software is difficult because engineering teams must understand complex dependencies, identify technical debt, define safe migration boundaries, design testing strategies, and coordinate execution across many activities. This planning process is often time-consuming, difficult to reproduce, and vulnerable to incomplete or disconnected analysis. Nexora AI addresses this challenge with an autonomous multi-agent SDLC modernization platform. A developer begins by describing a high-level modernization goal. Nexora analyzes the goal, identifies constraints and success criteria, and automatically converts it into a dependency-aware workflow represented as a DAG (Directed Acyclic Graph). The platform coordinates specialized AI agents for different stages of modernization. The Code Analysis Agent identifies technical debt, coupling hotspots, end-of-life technology risks, and potential service boundaries. The Architecture Modernization Agent develops migration phases, API contracts, target architecture recommendations, and risk considerations. The Test Strategy Agent creates testing strategies, contract-testing requirements, CI/CD quality gates, and regression checkpoints. A Strategy Agent synthesizes these outputs into an executive roadmap, while a Verification Agent evaluates the resulting work for accuracy, completeness, relevance, and actionability. Nexora also grounds agent reasoning using a Retrieval-Augmented Generation (RAG) knowledge layer and memory system. For the demonstration, the platform includes a fictional 480,000-line Spring 4 / Java 8 RetailCore monolith with legacy dependencies, high coupling, and low test coverage. The system uses this knowledge to generate a realistic modernization workflow covering service boundaries, strangler-fig migration, API contracts, testing, risks, and phased execution. The target users are software engineers, architects, technical leads, engineering managers, and modernization teams working with complex or legacy systems. Users interact through a unified dashboard where they can create goals, inspect the generated DAG, execute tasks, review agent outputs, monitor verification results, and view analytics. A key differentiator is that Nexora is not simply a chatbot producing a single response. It assembles a team of specialized agents, manages their dependencies, shares context between tasks, grounds outputs in knowledge, verifies results, and supports human-in-the-loop approval for sensitive operations. This creates a more structured, traceable, and governed approach to AI-assisted software modernization.