The original title is about on-chain AI agent execution architecture. Let me rewrite it for a mobile feed.

Short summary

On-chain AI agents fail most often not at the smart contract or LLM layer, but at the perception layer where stale chain state leads to bad decisions. The article breaks agents into perception, decision, and execution layers, showing how polling-based data ingestion and LLM inference latency combine to produce transactions against outdated world state. It advocates real-time event streaming, mempool visibility, and typed structured data to keep agents grounded.

• •Perception layer staleness is the root cause of most on-chain AI agent failures
• •LLM inference latency steals time from perception freshness, compounding staleness
• •Real-time event streaming and mempool visibility are needed instead of polling

Generated with AI, which can make mistakes.