AI Agents Struggle in Web3 Due to Data Fragmentation and Latency

AI integration in Web3 faces significant challenges, primarily due to data issues. Key points include:

AI agents operate on a loop of observation, decision-making, action, and learning, requiring reliable and fresh data.
Web2 allows data rental from a few platforms, while Web3's data is fragmented across multiple chains and oracles.
Prominent figures assert that AI and crypto complement each other by merging generative capabilities with ownership and market access.
DeFi is evolving towards intent-based designs, reducing reliance on raw on-chain data.
The proposed ERC-7683 aims to standardize cross-chain intents for better execution.

The current data landscape presents several barriers:

Heterogeneity among chains complicates basic queries.
Trade-offs exist between data speed and cost.
Converting logs into actionable insights requires constant processing.
Network congestion affects reliability for autonomous agents.

Actionable data must meet specific criteria:

Normalized semantics across chains.
Low-latency and finality-aware freshness.
Cryptographic verifiability.
Proximity of compute to data.
Streaming capabilities with historical data access.

Recent failures in AI×Web3 products highlight the impact of latency and fragmentation:

Planet Mojo’s platform shut down in July 2025 due to market changes.
Brian, an on-chain transaction builder, ceased operations in May 2025 after losing first-mover advantage.
TradeAI faced a class-action lawsuit after stopping withdrawals.
BitAI went offline in March 2024 after failing to deliver promised profits.
Worldcoin faced temporary operational suspension in Indonesia due to regulatory issues.

Solutions to improve AI agents include:

Adopting intent-based frameworks over direct calls.
Ensuring finality-aware data freshness.
Implementing compute-near-data strategies.
Utilizing independent sources for critical signals.
Incorporating human oversight for important decisions.

A cohesive AI-ready data layer should be programmable, verifiable, and real-time, featuring:

Multi-chain connectors with normalized schemas.
Kakfa-like streaming and OLAP snapshots.
Deterministic mirrors for data integrity.
On-stream computational capabilities.
APIs providing freshness metrics.
Intent hooks for streamlined actions.
Safety mechanisms for audits and risk management.

The future of AI in Web3 involves creating markets for agents and provable data, emphasizing safety and governance. Proper architecture will determine success in deploying effective AI agents capable of navigating complex market conditions.