AI Agents in DeFi: What’s Holding them Back and What’s the Solution?

It’s 2025 and AI agents are everywhere. From telegram bots to track your portfolio and whisper buy signals to AI companions that help manage DAO proposals. AI agents redefine the way traditional program functions. The promise is clear: AI agents can work faster, cheaper, and smarter than any human, and they never sleep.

But while everyone’s focused on building smarter agents, one question often goes unasked:

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What data are these agents using? Because no matter how advanced the model is, the agent will make bad decisions if the data behind it is broken, incomplete, or misunderstood. And in crypto, bad choices are expensive.

The Illusion of Intelligence

Let’s break this down with a simple example.

Imagine you’re using an AI agent to manage your DeFi portfolio. You want it to monitor your positions, rebalance your risk exposure, and auto-compound your rewards.

To do that, it needs to know:

Which protocols you’re currently using
How much capital you’ve allocated in each
What the yields are
Whether a position is still active or expired
Whether there are new opportunities that match your strategy

Now here’s the reality: most on-chain data is raw and contextless. It’s a stream of transactions, logs, token balances, and contract calls. There’s no built-in meaning. A wallet might receive tokens from a vault contract, but is that a withdrawal? A reward? A refund? Is the user staking, bridging, or simply dusting?

Humans can sometimes piece this together manually by looking at a block explorer and checking protocol docs. But for an AI agent? That ambiguity is a problem. Unless the data has already been structured, labelled, and interpreted, the agent is only guessing.

The Hidden Cost of Poor Data in Web3

Suppose your AI agent sees a large token deposit into your wallet and assumes it’s profit. Based on that, it rebalances your assets, moving stablecoins into a higher-risk vault. But what if that deposit was a refund from a failed transaction?

The agent acted with confidence on the wrong input. And that’s the issue. AI agents don’t just read data—they act on it. When the data is poor, the consequences compound.

This is especially dangerous in:

DeFi trading: Agents making decisions based on misread liquidity flows or fake volume can buy into pumps or exit too early.
Portfolio management: Misidentifying protocol positions leads to poor allocation, overexposure, or missed opportunities.
Security and compliance: Mistaking scam tokens for legitimate ones can lead to reputational damage or financial loss.

What we need isn’t more agents scraping logs. We need better data pipelines that deliver:

Clean, deduplicated transaction history
Labelled activity with protocol-specific logic (e.g. “user added liquidity to PancakeSwap”)
Real-time updates that agents can access without delay
Unified APIs so agents don’t have to integrate 10 different sources

Datai Network: The Quiet Engine Behind the Agents

This is where Datai Network steps in. While others are building the brains, Datai is creating the nervous system. It turns raw on-chain activity into structured, labeled, and AI-ready data that agents can actually understand.

Let’s look at a few examples:

A user stakes USDT-WBNB on BabyDogeSwap. Datai detects the LP tokens and tags the action as a liquidity provision, labeling it with pool details, timestamp, and APY context.
An address receives multiple airdrops in a short time frame. Datai filters out likely scams and classifies real versus spam tokens.
A user’s WBNB is split across PancakeSwap, BiSwap, and a farming vault. Datai aggregates these positions and provides a unified view of their exposure.

This kind of structured intelligence is what allows agents to be truly autonomous. Instead of relying on guesswork or building massive internal logic trees, developers can tap into Datai and focus on building smarter behaviors.

Whether you’re designing a Telegram assistant for crypto newbies, an institutional-grade trading bot, or an on-chain reputation engine, the same principle applies: Good agents come from good data. So next time an AI agent impresses you, ask yourself, how does it know what to do? Chances are, it’s using Datai.