The field of autonomous agentic AI systems is moving towards developing more robust and trustworthy frameworks for deployment at scale. Recent research has focused on creating unified frameworks for risk assessment and mitigation, as well as leveraging Large Language Models (LLMs) to automate tasks such as linear reformulation of nonlinear optimization problems and metamorphic testing of autonomous driving systems. These advancements have the potential to enable more widespread adoption of agentic AI in enterprise environments. Notable papers in this area include: AURA, which introduces a gamma-based risk scoring methodology for autonomous agentic AI systems. LinearizeLLM, which presents an agent-based framework for LLM-driven exact linear reformulation of nonlinear optimization problems. ATA, which proposes a neuro-symbolic approach to implementing autonomous and trustworthy agents. AutoMT, which develops a multi-agent LLM framework for automated metamorphic testing of autonomous driving systems.