In the past year, the competitive focus in the field of artificial intelligence has mainly centered around model scale and human-like conversational abilities. However, with the continuous advancement of technology, a new trend is quietly emerging, particularly attracting the attention of on-chain AI agent developers. This trend reveals a key challenge for the future development of AI: obtaining real, timely, and unaltered data.

Experts point out that even the most advanced AI systems will still produce outputs that lack value if the input data is false, outdated, or manipulated. This view highlights the significant impact of data quality on AI performance.

Taking the on-chain investment AI assistant as an example, such an agent needs to accurately grasp the user's asset situation, verify transaction execution in real time, and be able to synchronize data across multiple blockchain platforms (such as Solana, BNB, and zkSync). Without effective cross-chain verification, data aggregation, and on-chain compression technologies, such an AI system will be unable to realize its potential and can only make decisions based on limited information.

In this context, technical solutions such as Lagrange have emerged. Lagrange is not just a simple data provision tool; it serves more as a multifunctional assistant for on-chain AI, integrating data management, verification, and triggering. Specifically, Lagrange plays three key roles:

1. Data officer: responsible for collecting and organizing cross-chain data.
2. Certifier: Ensure the authenticity and integrity of the data.
3. Trigger official: Activate the corresponding AI behavior based on the data status.

Through SQL-style cross-chain data aggregation capabilities, Lagrange provides a clear "vision" for on-chain AI, enabling it to comprehensively understand the dynamics of the blockchain ecosystem. This capability is crucial for developing efficient and reliable on-chain AI applications and may become a key factor in driving the next generation of blockchain and AI integration.

As this trend deepens, we can foresee that the future competition in AI will not only depend on the advancement of algorithms but also on how to build an ecosystem capable of providing high-quality, real-time, and verifiable data. This will open up new application scenarios for the combination of blockchain technology and artificial intelligence, potentially revolutionizing fields such as decentralized finance, supply chain management, and autonomous systems.