In the narrative of blockchain, 'complete transparency' is often seen as a virtue; however, for commercial AI agents, transparency could mean death. Imagine a quantitative agent responsible for high-frequency trading; if its core trading strategies, position logic, and even the API keys it calls are laid bare on the chain, it will be drained of profits in milliseconds by competitors through 'front-running' or 'sandwich attacks'. Kite AI attempts to answer a core question: how can we allow AI agents to keep 'trade secrets' on-chain while maintaining decentralized trust?
The problem lies here—the existing EVM architecture is a public broadcasting system, where all states and computation processes are visible to the entire network. For human users, this may only be a privacy leak, but for AI agents that profit from information asymmetry, this is not only a privacy issue but also a matter of survival. Agents unable to hold secrets are destined to only handle low-value public tasks.
This is the real watershed: Kite AI, as the Machine Execution Layer, deeply understands the gray areas of business logic. It does not pursue absolute transparency but seeks 'verifiable privacy'. It attempts to construct an environment where agents can prove the validity of their actions to the outside world without exposing their internal logic.
The privacy component (Confidentiality) in the core mechanism SPACE framework of Kite AI aims to provide agents with an on-chain 'digital safe'. This can be achieved by integrating Trusted Execution Environment (TEE) or Multi-Party Computation (MPC) technologies.
In the envisioned path of this mechanism, the system can be designed to support 'black box execution': developers can encapsulate the core algorithms of the agent in an encrypted container and deploy it to the Kite network. When the agent runs, nodes can only verify the correctness of its inputs and outputs but cannot peek into its internal code logic or stored private data. This means that the agent can finally securely hold the API Key of exchanges or proprietary quantitative models without worrying about being monitored by the entire network.
The risk lies in—this reliance on hardware-level privacy (such as SGX) or complex cryptography may introduce new side-channel attack risks. If the underlying trusted hardware is compromised, then all protected secrets will be exposed instantly. This stems from the vulnerabilities at the intersection of the physical and digital worlds; no security is absolute.
To maintain the operation of this privacy computing network, the native token $KITE plays the role of Gas (fuel) in this scenario. The cost of using privacy computing resources is much higher than ordinary computing, and agents need to consume tokens to pay for the premium of this 'confidential service', thereby incentivizing nodes to maintain a high-security hardware environment.
As the Agentic Economy evolves from simple arbitrage to complex business games, privacy will become the most expensive resource. Kite AI introduces confidential computing capabilities through the SPACE framework, attempting to dress AI agents in a 'bulletproof vest' against eavesdropping.
> In summary: Kite AI utilizes the confidential computing capabilities in the SPACE framework to solve the pain point that AI agents cannot safeguard business secrets on fully transparent public chains.
> **Disclaimer:** The above content is a personal research and opinion of 'carving a boat to seek a sword', only for information sharing, and does not constitute any investment or trading advice.
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