Mitigating LLM Hallucinations in Code Generation for BDI Agents

• Natália Mendes Goes UTFPR
• Rafael C. Cardoso University of Aberdeen
• Gleifer V. Alves UTFPR
• André P. Borges UTFPR

DOI: https://doi.org/10.5753/wesaac.2026.31818

Resumo

Este artigo apresenta o MASPY-LLM, um pipeline autônomo para geração de código projetado para mitigar as alucinações de Large Language Models (LLMs) na programação de agentes Belief-Desire-Intention (BDI) no framework Multi-Agent System for Python (MASPY). A arquitetura híbrida integra injeção de contexto, Retrieval-Augmented Generation (RAG), orquestração ator-crítico e higienização algorítmica para corrigir quebras topológicas e previnir amnésia de contexto. Para avaliar a abordagem da ferramenta, instanciou-se um estudo de caso focado no protocolo de negociação contract-net. Os resultados demonstram a capacidade do pipeline em compilar códigos sintaticamente válido e aderente às restrições do MASPY, reduzindo a curva de aprendizado na modelagem de sistemas no framework.

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