Trustworthy Agentic AI: A Survey and Taxonomy of Secure Coordination and Hallucination Mitigation in Multi-Agent Large Language Model Systems

Background: Large language model (LLM)-based agentic systems are evolving beyond single-turn generators into autonomous, toolusing, multi-agent workflows with persistent memory and self-directed planning. When these agents collaborate, hallucinations no longer remain local; they can propagate across agent boundaries and trigger real-world operational failures.  Objective: This paper provides a structured foundation for building secure, reliable, and hallucination-resilient agentic AI by surveying failure modes and proposing a layered trust taxonomy with enforceable controls.  Methods: We reviewed literature on LLM reasoning, retrieval-augmented generation (RAG), hallucination detection, multi-agent frameworks, and AI governance (including zero-trust security principles) and catalogued failure modes across reliability and security dimensions.  Results: We present a seven-layer trust taxonomy spanning identity, planning, communication, memory, retrieval, execution, and oversight. From this taxonomy, we derive six reusable secure-coordination design patterns and propose a model-agnostic reference architecture for auditable, policy-enforced agentic workflows.  Conclusion: Trustworthiness in agentic AI is fundamentally a system property, not merely a model property. The proposed taxonomy and design patterns provide practical, implementation-independent guidance for securing multi-agent LLM deployments in research and high-assurance enterprise contexts.  Plain Language Summary: AI systems built from large language models can now plan tasks, use tools, and work together as teams of specialized agents. This collaboration creates new dangers: when one agent fabricates information, the other agents may act on it as though it were true, spreading errors throughout the system. This paper maps where trust breaks down, from agent identity and message passing to memory and tool use, and proposes design rules and a system blueprint for more reliable and secure AI teams. agentic AI, multi-agent systems, trustworthy AI, hallucination mitigation, retrieval-augmented generation, tool use, zero trust, AI governance.