Why AI Agents Could Be the Next Big thing in DeFi

By design, new technologies come in waves that reinforce each other. Mobile, social, and cloud reshaped the last ￰0￱ next era looks like AI, crypto, and agents – where “architecture is destiny,” and user intent becomes the primary interface AI is Penetrating Web3, and its happening Fast As per DappRadar over last 18 months, AI has moved from novelty to substrate in crypto: LLMs summarize governance, agents rebalance portfolios, and bots execute on-chain strategies in real ￰1￱ are voting with capital: by June 26, 2025 , AI-agent projects had raised $1.39B year-to-date, already outpacing 2024’s ￰2￱ Dixon frames the macro well: AI and crypto are ￰3￱ supply ownership, credible commitments, and identity, primitive AI systems lack but desperately need if we want open markets for compute, data, and ￰4￱ his words, “AI needs blockchain-enabled computing.” – a16z crypto Zooming out, even AI’s industrial impact supports this agentic shift.

NVIDIA’s Jensen Huang points to AI as the start of “a new industrial revolution,” which implies new user layers and automation patterns in finance, too – Nasdaq From Apps to Agents: The Backend Abstracts Away The emerging end-state is simple to describe and hard to build: **you state an intent; an autonomous agent composes the stack-**data, liquidity, risk checks, settlement-then ￰5￱ on agentic systems and “the Agentic Web” sketches this world where agents pay other agents for data and services, coordinate via smart contracts, and transact without human ￰6￱ Developer tooling is catching up: frameworks like elizaOS show how to wire LLM agents to wallets and DeFi actions (“transfer” and “swap” from natural language), hinting at a future where the app is an agent ￰7￱ Data Problem: Web3 Is Still Fragmented Agents thrive on reliable, low-latency data.

Web3, however, is splintered by chains, schemas, and ￰8￱ posts and vendor docs converge on the same point: raw chain data is time-ordered and scattered; meaningful queries require specialized indexing, subgraphs, replication, and ETL pipelines – often repeated per ￰9￱ like Goldsky and The Graph help, but even they highlight the need for cross-chain mirroring , real-time streaming, and composable subgraphs to serve complex apps-exactly what agents will demand ￰10￱ analyses echo the cost of fragmentation for DeFi risk and UX. Takeaway: if the UI becomes an intent box, the heavy lifting moves to a programmable data layer that normalizes on-chain/off-chain context, exposes deterministic APIs to agents, and supports low-latency computation (alerts, scoring, routing) across ￰11￱ AI Agents Are a Natural Fit for DeFi DeFi is machine-native: transparent ledgers, programmable liquidity, and composable ￰12￱ makes it a perfect playground for autonomous agents to: Trade and rebalance via structured prompts (“sell long-tail assets into ETH if volatility exceeds X”).

Scan risks (contract anomalies, oracle drift) continuously and price them into ￰13￱ and MM across AMMs/CEXs without UI ￰14￱ (draft proposals, simulate outcomes) using on-chain and forum ￰15￱ work surveying autonomous AI agents in DeFi forecasts exactly these roles, linking agent decision-making to market microstructure and governance ￰16￱ similarly suggests the most viable role is **AI “as a player” in crypto games**, which maps cleanly to ￰17￱ Emerging Landscape: Chat-Based DeFi Platforms Below are six chat-based or agent-first products that illustrate the spectrum, from consumer bots to intent-centric ￰18￱ : AI crypto co-pilot with natural-language/voice, aiming to route , bridge , swap , lend across chains with MPC-secured wallets and safety ￰19￱ “type the task, Elsa handles the stack.” Projected USP: unified chat/voice control plus custody model (MPC) for mainstream ￰20￱ : Brands itself as Agentic Finance ; “Don’t trade, just hoot.” Text-to-trade execution across DeFi, positioning agents as solvers that connect user intent to ￰21￱ USP: end-to-end intent pipeline and cross-domain ambition (finance, identity, gaming). ￰22￱ : Focused on destination-based swaps : you specify what outcome you want (“end with 2 ETH on Base”), and the system determines the portfolio source, route, and fees between ￰23￱ USP: Centered on destination rather than source-reducing user decision load and tapping latent portfolio ￰24￱ App : Explicit intent-based swaps via solver/relayers; routes across multiple aggregators/DEXs; full ￰25￱ USP: Native intent-based execution (solver model): Users specify outcomes; off-chain solvers/relayers compete to route across multiple liquidity ￰26￱ : AI wallet companion that can analyze holdings and execute on-chain transactions from chat; lives as a web app/Farcaster ￰27￱ USP: AI wallet companion (analysis → action): chat interface that understands your portfolio and surfaces tailored insights What the Agentic User Layer Requires Infra If agents are the new UI, infra must be refactored for machines : Programmable Data Layer: cross-chain ingestion → normalized schemas → real-time replication/mirroring → deterministic APIs consumable by agents.

Latency-aware Compute: triggers for price/volatility/MEV risk, agent policy evaluation, and pre-trade ￰28￱ & Permissions: wallet-bound permissions, cryptographic attestations (“proof of personhood/humanity”), and policy guards around agent autonomy: concepts Dixon directly connects to blockchain’s ￰29￱ Rails: Vitalik’s cautions:restricted APIs, circuit breakers (“kill switches”), and alignment layers:need to be ￰30￱ This is Important (and Why Now) The intent-centric pattern is catching on: users type goals; agents handles the ￰31￱ status quo-click across bridges, DEXs, and dashboards – can’t scale by the next 100M users. Architecturally, the fix isn’t just a better front end; it’s open rails for ownership and programmable data so that many agents-not just a few closed super-apps:can compete on user ￰32￱ big waves arrive, they “complement each other and work together.” AI brings creativity and automation; crypto offers open ownership and incentives; new devices (from phones to wallets to wearables) conclude distribution-together forming a user stack that reads like agents by ￰33￱ Thought If “read-write-own” was the last era, the next one introduces “act”: software that acts on the user’s ￰34￱ DeFi, that means agents that understand your intent, price risk, and settle across broken markets-safely and ￰35￱ won’t simply provide nifty chat UIs; they’ll think architecture as destiny and invest on programmable data and incentive layers that let agents thrive at scale