The Agentic AI Framework for Optimizing Yield Aggregators in Decentralized Finance

Abstract

As decentralized finance (DeFi) ecosystems continue to expand, yield aggregators play an important role in automating capital allocation across lending and liquidity protocols. However, most existing aggregators still rely on static strategies and governance-driven update cycles, limiting their ability to respond to rapidly changing market conditions. This paper proposes a conceptual Agentic AI approach for yield aggregation that introduces adaptive, policy-constrained autonomy into decentralized financial systems. The proposed approach adopts a modular three-layer architecture consisting of a Perception Module for contextual data collection, an Agentic AI Core for reasoning and strategy formulation, and an Action Execution Module for controlled on-chain interaction. The study follows a conceptual exploratory design combining architectural modeling, policy-aware pseudocode, and scenario-based behavioral evaluation using historical DeFi data drawn from Aave V3 and Compound V3 lending markets. Rather than benchmarking yield performance, the evaluation focuses on behavioral properties such as decision timing, responsiveness to market signals, and compliance with governance and risk constraints. The results indicate that the proposed approach reduces response delays associated with governance update cycles while maintaining controlled and selective decision behavior under volatile conditions, without compromising policy compliance or introducing unnecessary transaction overhead. This work contributes a reference architecture and a behavioral evaluation approach for integrating Agentic AI into DeFi yield aggregation, offering practical design insights for adaptive, governance-aligned decision systems, and provides a foundation for future empirical validation using live on-chain deployment and learning-based extensions.