AI Fundamentals: Valuing AI Agents & Data Assets

摘要

Large Language Model (LLM) agents now read the world through managed-context pipelines, write to it via tool-calling APIs, and continuously re-wire themselves with fresh experience. Stakeholders therefore need a Generally Accepted Accounting Principles (GAAP) compatible method to price both (i) the agent's labour-like output and (ii) the data traces that fuel learning. We formalise a single unifying metric - agent Economic Value (AEV)- and demonstrate that these metrics are measurable today. We then extend the template to reinforcement-learning regimes in which grounded rewards equal cash flows. Lastly, we propose a financial settlement layer, which transforms the agent from a passive software user into an active economic participant.