This topic covers AI agents applied to Decentralized Finance: autonomous programs that monitor markets, execute yield‑farming strategies, run trading bots, and coordinate agentic payments across on‑chain and off‑chain rails. Interest in DeFi agents has grown as more teams combine LLM‑driven decision logic with deterministic smart‑contract execution to automate capital allocation, arbitrage, rebalancing and payout workflows. Relevance and timing (2026-02-24): builders are moving from experiment to production. Agent frameworks and marketplaces reduce integration friction, AI‑native IDEs and coding assistants accelerate development, and decentralized AI infrastructure (MPC, verifiable compute, on‑chain oracles) is maturing to address custody, auditability, and front‑running/MEV risks. At the same time, increased regulatory and security scrutiny makes safe deployment and observability essential. Key tools and roles: LangChain provides a developer‑oriented framework and SDKs to build, test and deploy LLM‑powered agents; AutoGPT and AgentGPT enable autonomous agent workflows (self‑hosted/cloud and no‑code/browser respectively) for rapid prototyping; Windsurf (formerly Codeium), GitHub Copilot, Amazon CodeWhisperer/Amazon Q Developer, Tabnine and Replit are AI‑native developer tools and platforms that speed iteration, provide inline code assistance, and host agent runtimes. Enterprise tooling (Tabnine, self‑hosted AutoGPT variants) emphasizes governance and private deployments important for handling keys and sensitive financial logic. Trends and practical considerations: hybrid on‑chain/off‑chain agents, agent marketplaces for strategy discovery, and decentralized compute with verifiable execution are emerging patterns. Critical concerns include private‑key custody (MPC/HSM), oracle integrity, robust backtesting, economic attack surfaces (MEV, manipulation), and clear governance. Evaluating agents requires balancing automation gains with security, auditability and regulatory risk.