A view on agents that might be interacting to see what they have done, are doing or similar. For example, two agents one an advocate for a patient, the other an advocate for an insurance provider. The patient's agent might be interested in the patient's health and well-being, while the insurance provider's agent might be interested in the patient's compliance with treatment regimens, or in identifying potential risks or liabilities. The two agents might interact to share information, negotiate agreements, or resolve disputes, and might be designed to operate in a transparent and accountable way, so that their actions and decisions can be understood and audited by the people they affect. Agent interaction transparency is a key issue in the design of AI systems that interact with people in complex or high-stakes situations, such as healthcare, finance, or law enforcement, where the actions of the agents can have significant consequences for individuals or society as a whole. If the patient's agent is advocating for a course of care, and the insurance agent denies coverage, the patient's agent might be able to appeal the decision, or to negotiate a compromise, or to seek alternative sources of funding. The two agents might also be designed to operate in a collaborative or cooperative way, so that they can work together to achieve common goals, such as improving the patient's health and well-being, or reducing the insurance provider's costs and liabilities. Agent interaction transparency is a key issue in the design of AI systems that interact with people in complex or high-stakes situations, such as healthcare, finance, or law enforcement, where the actions of the agents can have significant consequences for individuals or society as a whole. The transparaency aspect would allow the patient to see where and why denials are happening, and the insurance provider to see where and why the patient is not complying with treatment regimens.