5 citations · 6 across the 9 of their papers we have counts for
10 papers
Agent Status Protocol (ASP): A Lightweight Specification for State Reporting in Multi-Agent Systems
Diego Gosmar, Deborah A. Dahl, Emmett Coin +2
The Agent Status Protocol (ASP) defines a lightweight way for heterogeneous AI agents to report their operational state to a status server, and for observers such as dashboards to…
MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop
Diego Gosmar, Giovanni Zenezini
Document processing automation remains a critical challenge in enterprise environments, where traditional manual approaches are labor-intensive and error-prone. We present MADP, a…
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
Diego Gosmar, Deborah A. Dahl
This paper describes an approach to hallucination detection and mitigation using a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic simil…
Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
Diego Gosmar, Deborah A. Dahl
Prompt injection remains a central obstacle to the safe deployment of large language models, particularly in multi-agent settings where intermediate outputs can propagate or amplif…
Agentic AI Sustainability Assessment for Supply Chain Document Insights
Diego Gosmar, Anna Chiara Pallotta, Giovanni Zenezini
This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI).…
Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems
Diego Gosmar, Deborah A. Dahl
This paper proposes a novel architectural framework aimed at enhancing security and reliability in multi-agent systems (MAS). A central component of this framework is a network of…
Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks
Diego Gosmar, Deborah A. Dahl, Dario Gosmar
Prompt injection constitutes a significant challenge for generative AI systems by inducing unintended outputs. We introduce a multi-agent NLP framework specifically designed to add…
Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks
Diego Gosmar, Deborah A. Dahl
Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating mult…
AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations
Diego Gosmar, Deborah A. Dahl, Emmett Coin +1
This paper presents a novel extension to the existing Multi-Agent Interoperability specifications of the Open Voice Interoperability Initiative (originally also known as OVON from…
Conversational AI Multi-Agent Interoperability, Universal Open APIs for Agentic Natural Language Multimodal Communications
Diego Gosmar, Deborah A. Dahl, Emmett Coin
This paper analyses Conversational AI multi-agent interoperability frameworks and describes the novel architecture proposed by the Open Voice Interoperability initiative (Linux Fou…