What question did this study set out to answer?

The study aims to present AnimaCore, an architecture designed for effective AI-assisted software development, addressing technical debt.

April 5, 2026Open Access

AnimaCore: A Multi-Project Ecosystem Architecture for Inductive AI-Assisted Software Development

Key Points

The study aims to present AnimaCore, an architecture designed for effective AI-assisted software development, addressing technical debt.
Introduces a multi-project software ecosystem architecture called AnimaCore.
Describes the Silo/Control Plane architecture managing four sub-systems.
Utilizes a tri-language programming paradigm with Python, C++20, and Rust.
Implements a multi-model AI pipeline with tools like Gemini CLI and Claude.ai.
Demonstrates enhanced architectural integrity across different projects.
Shows that spec-first development approaches can significantly reduce technical debt.
Indicates that inductive reasoning improves rapid software engineering efficiency.

Abstract

This paper presents AnimaCore, a multi-project software ecosystem that operationalizes the Inductive AI-Assisted Development (IAAD) protocol introduced in AnimaLab (Paper 1). It describes the Silo/Control Plane architecture governing four independent sub-systems (PAEChaka, CodeShelter, EduSim, and MMVM), the tri-language paradigm Python/C++20/Rust), and the multi-model AI pipeline (Gemini CLI, Claude.ai, Qwen3) used to maintain architectural integrity across project boundaries. AnimaCore demonstrates how spec-first development and inductive reasoning can mitigate technical debt in rapid AI-driven software engineering at ecosystem scale.

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