The field of artificial intelligence is witnessing a significant shift towards the development of causal reasoning and multi-agent systems. These advancements are driven by the integration of large language models (LLMs) with multi-agent architectures, enabling the collaborative or specialized abilities of multiple LLM-based agents to address complex causal relationships and reasoning tasks. This synergy is leading to innovative solutions in various domains, including scientific discovery, healthcare, and power system analysis. A key challenge in this area is the need for verification and validation of the results generated by these systems, which is crucial for ensuring the reliability and trustworthiness of AI-driven discoveries. Noteworthy papers in this regard include Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimation, which explores the design and evaluation of causal multi-agent LLMs, and The Need for Verification in AI-Driven Scientific Discovery, which highlights the importance of verification in AI-assisted discovery. Additionally, papers like GridMind: LLMs-Powered Agents for Power System Analysis and Operations demonstrate the potential of agentic AI in scientific computing, while Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification introduces a novel method for improving the factual accuracy and trustworthiness of LLMs.