What Are DeFi AI Agents? A Developer’s Handbook

Written by Max Crawford

Published on November 26, 20258 min read

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AI agents are here and they are coming onchain. Are you prepared?

With smart contracts, we have code that enables trustless transactions onchain. And now with AI agents, we have code that can operate autonomously across contracts. These autonomous agents can execute multi-step transactions with just a prompt in simple English.

Welcome to the age of agentic finance.

This is beyond simple automation. Developers can now build systems that transact natively onchain. And this article will be your guide to understanding this new narrative many are calling “DeFAI” (a portmanteau of DeFi and AI).

What Are AI Agents?

AI agents are software programs that can take action when prompted, combining the capabilities of LLM chatbots and conventional automation to understand intent, plan out steps, and execute a transaction.

LLM chatbots (like ChatGPT) can interpret natural language and produce relevant text-based outputs. Conventional automation tools can perform predefined tasks using APIs and triggers. AI agents combine the ability to understand human language and reasoning capabilities of machine learning models with automation capabilities.

The result is a software product that is capable of perceiving its environment, making decisions, and executing complex multi-step actions without constant human intervention.

What Are DeFi AI Agents?

DeFi AI agents combine decentralized finance and artificial intelligence and enable autonomous programs that can trade and transact instantly on a global scale. The open, global financial economy enabled by DeFi is now a lot more complex and autonomous.

As traditional finance allows only approved parties to transact and innovate on its rails, users end up with limited autonomy and very little flexibility in how financial actions can be programmed. It (TradFi) always needed humans to intervene (think moving money or managing portfolios). DeFi, on the other hand, is autonomous in more ways that you may realize. For instance, lending protocols (like Aave) can approve loans, disburse interest, and much more by themselves.

Now, AI agents take DeFi’s openness one step further. In the pre-AI age, only humans could interact with smart contracts. Today, AI agents can interact with protocols (and each other) directly to transact, coordinate liquidity, and perform virtually any multi-step DeFi activity. In essence, DeFi is going from being human-driven to a code-driven permissionless network that is more open and autonomous.

Why Build AI Agents In DeFi?

DeFi is a permissionless financial system that allows anyone to transact openly without intermediaries. AI agents extend this idea by enabling code to interact with code (think agents transacting with protocols or even other agents).

Humans move at turtle-speeds relative to computers. AI agents being faster, unlock new possibilities (by acting on behalf of users) for arbitrage, yield routing, auto-balancing, and many micro-optimizations that aren’t practical manually.

Google CEO Sundar Pichai said, “The future of AI is not about replacing humans, it’s about augmenting human capabilities.” The same can be extended to DeFi. In the coming years, we can expect a large portion of DeFi activity to be driven by agents operating for their users, forming the foundation of an agentic economy.

Identifying Trends

We can understand DeFi as a set of programmable rules that define how transactions occur, whether through AMMs, vaults, or lending protocols. AI agents can understand these onchain parameters (liquidity depth, interest rates, vault balances, etc.) and act almost instantly based on user intent.

AI agents can identify trends and data as soon as they’re visible and act on that information much faster than humanly possible. Developers can use this to their advantage by designing protocols that anticipate and leverage these rapid feedback loops (automating liquidity routing, optimizing yield strategies, or dynamically adjusting parameters in real time to stay ahead of the market).

Agentic Commerce

AI agents in DeFi unlock agentic commerce through agentic workflows. Agentic workflows in DeFi are essentially self-governing sequences where AI agents interpret intent, coordinate multi-protocol actions, and execute strategies autonomously.

For developers, this means instead of coding every step, you define what you want done, and the agent figures out how to do it.

For example, something as simple as yield farming involves tracking liquidity pools, comparing APYs, moving funds between chains, and more. Naturally, most users who only want to explore the use case (and not become “blockchain experts”) lose interest and drop off. This has been a persistent blocker for DeFi’s mainstream adoption.

With AI agents, users can condense that lengthy multi-step process to typing a sentence as simple as “maximize my stablecoin returns.”

Intuitive UX

One of the biggest setbacks of DeFi has been usability. By integrating AI agents within applications, developers can freely build complex financial products (that are generally not easy to use for the uninitiated) and leverage AI’s NLP ability to let users interact with just plain English.

A great example is Pass App, which uses AI to let users perform a range of DeFi actions (say, swaps, staking, yield optimization, and more) just by typing requests in plain English.

This way, AI agents open up new markets and demographics for builders because now, they don’t have to worry about adoption (or a lack thereof) due to knowledge/usability barriers.

AI Agent Use Cases for DeFi

1. Yield Farming & Liquidity Optimizer

Yield farming changes constantly (think fluctuating APYs and new farms coming up every day). Naturally, simple scripts can’t keep up with these changes nor can they act on their own by factoring in new changes. An AI agent, however, can reason about when transferring funds is worth it, how long the inflated yields might last, and whether the risks justify the switch.

With DeFi AI agents, developers can scan yield opportunities and reallocate funds to the most efficient pools. This saves them the hassle of manually checking APYs or juggling multiple dashboards. It’s the agent that evaluates lending markets, liquidity pools, and farming rewards, calculates net returns after fees, and shifts positions when a better opportunity opens up.

An example of such an implementation is Genius Yield, which uses AI to manage liquidity and maximize yield generation.

2. Cross-Chain Arbitrage Agent

Cross-chain activity is much harder to build and manage than single-chain activity. Every network has different fees and confirmation times. And if two chains lack a direct bridge, then the flow will require many additional steps to execute the same transaction.

Handling cross-chain arbitrage manually is often too slow, and the arbitrage opportunity can quickly disappear. Simple bots wouldn’t cut it as well as they cannot reason about multi-step paths, changing liquidity, or whether the total cost of bridging cancels out the opportunity itself.

DeFi AI agents fix that perfectly. The agent can track prices, lending rates, and liquidity across chains and executes cross-chain moves whenever there is a clear advantage.

Say, USDC earns a higher lending rate on Polygon than Ethereum, the agent can shift liquidity accordingly.

Monetizing this is straightforward. Teams can run the agent with their own capital and keep the arbitrage or yield spread. If offered to users, the agent can function as a managed vault with performance fees.

3. Treasury Management

Companies and DAOs often struggle with treasury management (even more so in case of small teams) because they lack capable dedicated financial operators. And to hire a DeFi-native team costs much more than small teams can afford.

DeFi AI agents can solve that effectively. The DeFi agent can manage the DAO or protocol treasury with the discipline of a professional portfolio manager. The agent can allocate between stablecoins, volatile assets, and yield opportunities based on market conditions. It can even hedge during downturns, park surplus funds in safe lending markets, or rebalance as per risk exposure. It’s basically an AI-CFO that costs pennies on the dollar.

A good example of this model in practice is Safe Smart Accounts. Safe lets teams run AI agents inside strict multisig and spending-limit guardrails, so the agent can monitor balances, propose rebalances, automate routine treasury actions, and execute limited transactions while humans retain final authority.

4. Virtual Development Assistant

Developers can deploy an AI agent that works alongside them throughout the entire build cycle. For example, it can be a PR reviewer, transaction tester, and/or documentation helper.

As a code reviewer, the agent can scan pull requests (PRs) for vulnerabilities and exploit patterns, and spot critical issues like reentrancy bugs that could lead to multimillion-dollar failures.

As a transaction simulator, the agent could preview how on-chain actions will behave before deployment. It can estimate gas, predict whether a swap might fail due to low liquidity, highlight potential MEV exposure, and give developers readable insights that prevent costly mistakes.

As a documentation assistant, DeFi AI agents can parse merged PRs, categorize changes, and produce clean changelogs and developer-friendly docs. This keeps project knowledge up to date without forcing engineers to switch context or postpone documentation work.

Web3 AI Agent Projects

Olas

Olas (formerly Autonolas) is a platform that allows creation, co-ownership, and monetization of autonomous AI agents. It combines crypto with AI and builds a network of off-chain services ( think automation tools and oracles) that let users own and operate AI agents.

Pearl v1 is the team's most recent product shipment. Described as the first "AI Agent App Store," Pearl lets users download, own, and run multiple AI agents locally on desktop devices with self-custody of funds and data.

ChainGPT

ChainGPT offers AI-assisted smart contract creation, code auditing, trading analytics, NFT creation, and blockchain education through a chat-like interface.

Notably, ChainGPT offers comprehensive SDKs and REST APIs for developers to integrate crypto-specific AI capabilities into their applications. Additionally, the team runs CGPT.fun.

CGPT.fun is a practical sandbox where creators can deploy autonomous agents, test token-linked agent economies, and ship fully on-chain agent workflows without writing code.

ElizaOS

ElizaOS is a popular open framework for building AI agents in DeFi. Notably, ElizaOS provides modular components for multi-agent communication. So, developers can create agents that can coordinate and execute transactions across DeFi protocols (while each agent retains its own context and memory).

Developers can even combine ElizaOS with other APIs like that of Secret Network or Ankr for workflows beyond simple chat interactions.

HeyElsa

HeyElsa is an AI-powered DeFi co-pilot. The project’s main offering is a chat-like interface wherein users can connect their web3 wallets and give natural-language instructions like “maximize my stablecoin yield” or “bridge ETH to Solana and stake it”. Behind the scenes, HeyElsa agents translate intent into multi-step, cross-chain actions thereby handling gas payments, transaction routing, and protocol execution autonomously.

You already know ChatGPT, an AI-powered chat interface that answers ‘almost’ any question you might have and even performs a few tasks (browse the web, summarizing reports, etc.). HeyElsa is like ChatGPT, but for degens.

Build AI Agents with Alchemy

AI agents need a reliable way to fetch onchain data, interpret user intent, and execute transactions safely across multiple networks. We make it simple for developers to build, deploy, and scale AI agents by provisioning the APIs, node infrastructure, and semantic interfaces that let agents see, reason, and act on-chain.

Instead of spinning up nodes, parsing raw RPCs, or writing infrastructure glue code, developers can plug into Alchemy’s agent-ready stack.

With the Model Context Protocol (MCP) Server, agents can query live on-chain data in natural language, interpret complex states, and trigger actions like transfers, swaps, or staking through programmable smart wallets.

Use the configuration given below to set up MCP:

You’ve seen what DeFi AI agents can do and you know where to find the best building stack. The next step is for you to start putting what you’ve learned into practice. Start experimenting today by registering for the free tier.

Frequently Asked Questions

Can I build AI agents for free in DeFi?

Yes. You can prototype AI agents for free with Alchemy. The free tier gives you access to everything you need, including a reliable connection to blockchain APIs via our Node RPCs. With Node APIs, your DeFi AI agents can fetch real-time onchain data (like recent transactions and liquidity pool depth)) for situational awareness and execute user-defined intents.

Can AI agents talk to each other?

Yes, it is possible to build AI agents that can communicate with each other to achieve a goal.

Can AI agents make money in DeFi?

Yes. ​​Developers can build agents that earn fees, yield, or rewards by providing liquidity, executing arbitrage, or running DeFi automations.

What are the benefits of DeFi AI agents?

• Automating complex tasks, 24/7 activity, no human fatigue.

• Ability to process a lot of data (on‐chain & off‐chain) and act faster than humans.

• Lower barrier to entry for users (especially non-experts) via AI assistance.

• Potential for better returns/risk management if the agent is well-designed.

What are the risks of DeFi AI agents?

A big risk of DeFi AI agents is also its feature, i.e., autonomy. Since DeFi AI agents are expected to interpret intent and act with minimal human oversight, there is a non-zero probability of unintended actions. If not designed well, the agent can cost millions for the protocol.

How do AI agents reason?

AI agents combine a large language model (LLM) for decision-making with onchain data. The agent retrieves context (balances, gas fees, pool data), evaluates it against goals or constraints, and then formulates the next action plan.

When to use DeFi AI agents?

Use DeFi AI agents when you need automated, real-time on-chain decision making. They are ideal for tasks like monitoring markets, optimizing trades, managing liquidity, rebalancing portfolios, and executing multi-step DeFi workflows without manual input.

Can AI agents code?

Yes. It is possible to build AI DeFi agents that can write code with minimal human intervention. However, the code must be audited and tested heavily before deploying to production.

Will AI agents replace developers?

It’s unlikely AI agents will completely replace developers. Some roles may become increasingly redundant while others may change, but developers would not go extinct. Agents can automate testing, monitoring, and execution, but developers still design logic, verify safety, and set intent boundaries.