The application of Large Language Models (LLMs) in software engineering is frequently undermined by syntax hallucinations, logical inconsistencies, and the inability of monolithic models to autonomously validate their own outputs. This paper presents DAIDALOS OS (V3.1), a sophisticated multi-agent orchestration pipeline designed to transition AI interactions from conversational text generation to deterministic software engineering. The architecture implements a strict "Separation of Concerns" paradigm, dividing the cognitive load among specialized agents: a Semantic Router, a JSON-driven Planner, a Technical Executor, and a Logic Validator. Furthermore, DAIDALOS OS introduces a Closed-Loop Re-Planning mechanism powered by a Multi-Language Docker Sandbox. Instead of relying solely on static analysis, the system ephemerally compiles and executes generated code (Python, Java, Bash), capturing runtime tracebacks to force autonomous self-correction. Hosted via an asynchronous Unix Socket daemon and supporting dynamic routing between local edge-compute clusters and cloud-based Mixture of Experts (MoE) endpoints, DAIDALOS OS establishes a highly resilient, enterprise-grade framework for autonomous code generation.
Peter Novota (Fri,) studied this question.