You've just pulled a few all-nighters, wanting to deploy an AI data trading model on the chain.
The code runs well on the testnet, but when it hits the mainnet, it gets stuck on gas fees and high latency.
What's even more frustrating is that this chain offers almost zero support for AI agents—you need to write the underlying adaptations yourself and even learn a new programming language.
You can't help but think: how great would it be if there were a chain that could be compatible with the existing Ethereum ecosystem while tailoring rules specifically for AI agents?
This is not a fantasy.
In fact, many teams have already been trying to combine AI with blockchain, but the paths vary: some choose to start from scratch, creating a so-called 'AI exclusive chain'; others hope for Ethereum Layer 2, attempting to make lightweight modifications within the existing framework.
The former often falls into ecological isolation, while the latter is constrained by the rules of the upper network.
While KITE took a smarter approach:
A Layer 1 network compatible with EVM, specifically designed for AI agents.
Not reinventing the wheel, but standing on the shoulders of giants.
KITE's EVM compatibility means developers do not need to relearn a new smart contract language.
The AI agent logic you wrote in Solidity can be almost seamlessly migrated.
This not only lowers the development threshold but, more importantly, it directly connects to Ethereum's mature tool ecosystem—from MetaMask to Truffle, from Infura to The Graph.
For teams already familiar with Web3 development, the migration cost is almost zero.
Autonomy of Layer 1: Customizing rules for AI agents
Doing AI applications on Layer 2 always gives a sense of 'living under someone else's roof'.
When the network is congested, your transactions get stuck, and when gas fees fluctuate, costs get out of control, not to mention the lack of governance voice.
As a Layer 1, KITE can autonomously design transaction ordering mechanisms, fee models, and consensus algorithms, specifically optimizing for the high-frequency, low-latency needs of AI agents.
For example, prioritize AI inference tasks or design dedicated privacy protection schemes for model trading.
The choice of real developers
I know several teams that transitioned from Web2 to AI+Web3, and they ultimately leaned towards solutions like KITE.
Not because the technology is the most 'flashy', but because it is practical.
A senior developer said: "We don't have time to build a chain from scratch, but we also don't want to be constrained by the rules of Layer 2. KITE gives us a balance point—able to quickly go live with existing technology stacks while having enough freedom to optimize AI scenarios."
The value of technology lies not in whether it is cutting-edge, but in whether it can be implemented.
KITE did not chase after concepts that sound cool but are impractical; instead, it chose a pragmatic, iterative path.
This may be the key step for AI agents to truly move towards large-scale applications.