The field of artificial intelligence is witnessing a significant shift towards the development of autonomous AI agents that can operate independently and collaborate with each other. This trend is driven by the need for more sophisticated and dynamic interactions between AI systems and their environment. Recent research has focused on creating infrastructure-grade trust and lifecycle control for these agents, as well as developing standardized communication protocols to enable seamless interactions between them. Noteworthy papers in this area include FairSteer, which proposes a novel inference-time debiasing framework for large language models, and the paper on Trusted Identities for AI Agents, which leverages telco-hosted eSIM infrastructure to serve as a root of trust for AI agents. The A Survey of AI Agent Protocols paper provides a systematic overview of existing communication protocols for LLM agents, classifying them into four main categories and conducting a comparative performance analysis. The Building A Secure Agentic AI Application Leveraging A2A Protocol paper provides a comprehensive security analysis of the A2A protocol and recommends practical secure development methodologies for building robust and secure next-generation agentic applications.