Explore how AI agents could become DeFi’s main users, reshaping trading, lending, liquidity, and risk across crypto markets.
AI agents capable of executing trades, rebalancing portfolios, and managing treasury operations without human input are moving from prototype to production, raising questions about what DeFi looks like when its primary users are software rather than people.
The shift is not hypothetical. Alchemy’s AI-agents platform already powers 17,000+ agents and builders with more than $8 billion in lifetime transaction value. These agents execute multi-step DeFi actions from plain-English prompts, interacting with protocols directly for arbitrage, yield routing, auto-balancing, and treasury-style workflows.
A recent BeInCrypto report based on exclusive interviews with executives from Phemex, Wirex, Zoomex, and ChangeNOW argues that if agents become the dominant DeFi participants, blockchains will function more like machine coordination and settlement systems than retail trading platforms.
What to Know
- DeFi’s permissionless, API-accessible architecture makes it structurally more compatible with autonomous software agents than traditional finance.
- If agents scale to become a major share of DeFi users, the resulting shifts in liquidity, fee dynamics, and security assumptions will reshape crypto market structure.
Why DeFi Is Built for AI-Native Users
DeFi protocols are open, permissionless, and composable. Smart contracts do not require KYC documents, business hours, or human-facing interfaces. An AI agent, defined here as an autonomous software system that can analyze onchain conditions and execute transactions, can interact with lending pools, DEXs, and yield vaults through the same wallet and contract interfaces as any human user.
That structural openness is what separates DeFi from traditional finance for machine participants. A software agent cannot open a brokerage account or pass a video KYC check, but it can hold a wallet, sign transactions, and call smart contract functions 24 hours a day.
The practical applications already in production include arbitrage detection across DEX pairs, automated collateral rebalancing for lending positions, and treasury management workflows that route idle capital to the highest-yielding venues. These are rule-based tasks that map directly to what autonomous systems do well: monitor continuously, evaluate conditions against parameters, and execute without delay.
This is distinct from AI-assisted trading tools that help humans make decisions. The agents discussed here operate independently, holding their own wallets and executing transactions on their own authority, a pattern that companies working on corporate treasury automation are also beginning to explore.
How Agent Activity Could Reshape Trading, Lending, and Liquidity
Wirex co-founder Dmitry Lazarichev framed the shift directly in the BeInCrypto interviews:
“Once agents become the main actors, the chain starts behaving less like a marketplace of people and more like a piece of machine infrastructure.”
Dmitry Lazarichev, Wirex co-founder
Trading: Agents can rotate portfolios across multiple protocols simultaneously, executing swaps and rebalances faster than manual traders. At scale, this compresses simple arbitrage opportunities as more machine participants compete for the same edges.
Lending: Automated collateral management means positions can be adjusted in real time as prices move, potentially reducing the frequency of liquidation events but also increasing the speed and intensity of collateral flows during volatile periods.
Liquidity: Always-on machine participation could deepen liquidity in major venues. The scale of the market these agents would operate on is substantial: Ethereum alone holds approximately $52.9 billion in DeFi TVL with roughly $2.07 billion in daily DEX volume.
The competitive dynamics shift meaningfully. Human traders already face pressure from MEV bots and automated market makers. Agent-driven activity adds another layer: software that does not just extract value from transaction ordering but actively manages positions, allocates capital, and responds to governance proposals.
Protocol usage metrics would increasingly reflect machine demand rather than discretionary retail behavior, a change that could complicate how projects like spot ETF products measure organic user adoption.
What Risks Protocols Face When Machines Become the Core User Base
The security concerns are not theoretical. ChangeNOW CMO Pauline Shangett put it bluntly in the same interview series:
“Give an AI agent a wallet, and you’re not just securing code anymore, you’re securing a black box that can be manipulated with words.”
Pauline Shangett, ChangeNOW CMO
The core insight from the BeInCrypto analysis is that for autonomous agents, the main wallet-security risk shifts from cryptographic key theft toward prompt-layer manipulation. An attacker who can influence what an agent “decides” to do can drain funds without ever touching a private key.
Research supports this concern. An arXiv paper benchmarking web agent security found that prompt injection attacks partially succeeded in up to 86% of cases in realistic end-to-end scenarios, confirming that decision-layer manipulation is a material threat vector.
Alchemy’s agent infrastructure addresses some of these risks with scoped permissions, spending limits, and optional human approval gates. But these guardrails assume a design model where agents operate under supervision, not the fully autonomous paradigm the BeInCrypto interviews describe.
Protocol-level risks compound when many agents react to the same market inputs. Correlated automated responses to a price drop or oracle update could amplify liquidation cascades and blockspace congestion far faster than human-driven sell-offs. The recent Drift hack and its aftermath illustrated how quickly protocol exploits cascade through interconnected DeFi positions.
Governance incentives designed for human participants may also break down. Token voting, proposal evaluation, and delegation systems assume users who read, deliberate, and vote with judgment. Agents optimizing for yield or risk parameters could dominate governance in ways protocol designers did not anticipate.
The regulatory dimension adds further complexity. Existing financial rules were built for human actors and conventional software. Agent wallets raise unresolved questions about liability when a model executes a harmful transaction, how permissioning and audit logs should work, and whether agency law can accommodate software actors that lack legal personality.
No independent dataset yet quantifies what share of current DeFi transactions originate from autonomous AI agents. The thesis that agents will become DeFi’s main users remains forward-looking, supported by clear tooling momentum and structural compatibility but not yet by hard adoption numbers. What the evidence does show is that the infrastructure is live, the security risks are measurable, and the protocol design challenges are already overdue for serious attention.
Disclaimer: This article is for informational purposes only and does not constitute financial or investment advice. Cryptocurrency and digital asset markets carry significant risk. Always do your own research before making decisions.
