The rapid adoption of large language models (LLMs) into AI agents has created new challenges for transparency, reproducibility, and governance. While prior artifacts such as Model Cards and Data Sheets for Datasets support documentation of models and data, no analogous standard exists for describing the operational characteristics of AI agents. This paper introduces Agent Cards, a structured documentation artifact designed to capture the essential attributes of an agent, including its roles, memory taxonomy, tool integrations, communication protocols, monitoring hooks, governance scope, and evaluation metrics. By standardizing how agents are described, Agent Cards provide a lightweight yet powerful mechanism for enabling transparency, comparability, and auditability across deployments. A template is presented, accompanied by an illustrative example, followed by a discussion of the benefits of adopting Agent Cards within broader MLOps and LLMOps practices. Agent Cards are proposed as a potential foundation for future work on agent ledgers, audit bundles, and maturity frameworks, offering practitioners and researchers a common vocabulary for the responsible operationalization of agentic AI.