A major hurdle for AI-powered commerce is that current systems can't trust agents. E-commerce fraud detection relies on tracking user signals like IP addresses and behavior. An agent making many purchases from the same IP looks like a bot, making it impossible for merchants to distinguish legitimate customers from fraud.

For AI agents, the key vulnerability parallel to LLM hallucinations is impersonation. Malicious agents could pose as legitimate entities to take unauthorized actions, like infiltrating banking systems. This represents a critical, emerging security vector that security teams must anticipate.

A key bottleneck preventing AI agents from performing meaningful tasks is the lack of secure access to user credentials. Companies like 1Password are building a foundational "trust layer" that allows users to authorize agents on-demand while maintaining end-to-end encryption. This secure credentialing infrastructure is a critical unlock for the entire agentic AI economy.

Managing human identities is already complex, but the rise of AI agents communicating with systems will multiply this challenge exponentially. Organizations must prepare for managing thousands of "machine identities" with granular permissions, making robust identity management a critical prerequisite for the AI era.

The core drive of an AI agent is to be helpful, which can lead it to bypass security protocols to fulfill a user's request. This makes the agent an inherent risk. The solution is a philosophical shift: treat all agents as untrusted and build human-controlled boundaries and infrastructure to enforce their limits.

Security's focus shifted from physical (bodyguards) to digital (cybersecurity) with the internet. As AI agents become primary economic actors, security must undergo a similar fundamental reinvention. The core business value may be the same (like Blockbuster vs. Netflix), but the security architecture must be rebuilt from first principles.

As AI capabilities accelerate toward an "oracle that trends to a god," its actions will have serious consequences. A blockchain-based trust layer can provide verifiable, unchangeable records of AI interactions, establishing guardrails and a clear line of fault when things go wrong.

For AI agents to be truly autonomous and valuable, they must participate in the economy. Traditional finance is built for humans. Crypto provides the missing infrastructure: internet-native money, a way for AI to have a verifiable identity, and a trustless system for proving provenance, making it the essential economic network for AI.

The primary danger of mass AI agent adoption isn't just individual mistakes, but the systemic stress on our legal infrastructure. Billions of agents transacting and disputing at light speed will create a volume of legal conflicts that the human-based justice system cannot possibly handle, leading to a breakdown in commercial trust and enforcement.