In-depth Analysis of the Core Standards for AI Agents in 2026: Deconstruction of Technical Logic and Industry Cases
Focusing on your most concerned 'zero-knowledge implementation path for inference proof' and 'application scenarios for agent DID', combined with the real business logic of Web3, supplement specific technical details and landing cases—using practitioners' 'technical narrative sense' instead of vague discussions, avoiding AI-style framework expressions.
1. Inference Proof: How can Zero-Knowledge Proofs (ZKP) make the 'thinking' of AI agents verifiable?
The 'verifiable inference' is not just about presenting results, but allowing agents to 'prove' to any party on the chain: 'My decision is based on data A and derived according to rule B, with no cheating in the process', while not exposing specific data and model details (protecting privacy and core algorithms). The implementation path in 2026 will focus on 'lightweight ZKP + modularization of inference steps', landing in three steps:
