Pei C.: Institutions Need Verifiable Trust Systems for AI Agent Adoption
Pei C. pointed out that the premise for institutions to adopt AI Agents on a large scale is to establish a verifiable, traceable, and private trust mechanism.
This trust system will unlock significant market opportunities such as agentic payments, AI-enhanced asset issuance and management, and on-chain and off-chain global liquidity pools.
Institutional investors and AI infrastructure providers in the market are driving the development of trust protocols. Pei C. accelerates institutional entry through public discussions, benefiting compliant blockchain and privacy computing projects, while purely centralized Agents face short-term pressure, with funds concentrating on auditable AI Agent ecosystems.
Source: Public information
ABAB AI Insight
Pei C., as an observer in the intersection of crypto and AI, has previously emphasized that infrastructure precedes application. This viewpoint continues his analysis of the institutional adoption path, similar to how early DeFi hesitated due to a lack of KYC and audits, which were gradually addressed through Chainlink oracles and zero-knowledge proofs.
In terms of capital pathways, institutions will direct compliant blockchain, zero-knowledge proofs, and trusted execution environment (TEE) resources towards Agent authentication and auditing layer construction, while exploring agentic payments and AI asset management protocols. The motivation is to transform AI Agents from experimental tools into trusted financial execution units, forming a closed loop from trust infrastructure to high-value liquidity and asset management fee income.
Similar institutions have been slow to enter DeFi due to trust issues, until the expected explosion of RWA and compliant blockchain in 2024-2025. Pei C. currently positions AI Agents at a critical juncture of transforming from consumer-grade tools to institutional-level financial infrastructure, pushing the industry from single Agent experiments to a verifiable multi-Agent economic network.
Structural judgment: This fundamentally belongs to regulatory changes. AI Agents executing financial actions must simultaneously meet verifiability and privacy requirements. The mechanism combines blockchain, zero-knowledge proofs, and TEE to address the single-point risks of traditional centralized trust, forcing institutional capital from observation to building auditable Agent protocols, thus achieving a transfer of pricing power from human intermediaries to trusted AI execution layers.
ABAB News · Cognitive Law
Trust precedes adoption.
Verifiability is the ticket to entry, privacy is the moat.
Agent execution in finance, trust determines scale.