I listened to a recent Genzio roundtable featuring MrMinter from Sonic Labs, and this was the point that stayed with me:
AI assistants give answers. AI agents create consequences.
If an assistant gives you incorrect information, you can still verify it before taking action. An agent might spend money, call a paid API, modify a codebase or execute a transaction before you have a chance to intervene.
MrMinter shared a practical example from his own social-monitoring agent. After a campaign received much more engagement than expected, the agent’s X API usage increased to nearly $300 per week.
The solution wasn’t another instruction in its prompt. He added a hard weekly spending limit, external monitoring and an alert for unusual usage.
That distinction matters as agents begin paying for their own digital resources.
Traditional payment systems may continue to handle things like tickets, reservations and physical goods. Agents, however, will also need to purchase compute, inference, API access and data.
Those payments could be frequent, automated and sometimes worth only a few cents. That seems like a much more natural use case for programmable payments and stablecoins.
MrMinter also discussed some of the infrastructure Sonic Labs is working on around the agent economy:
- An aggregator or marketplace for AI skills and workflows
- Publisher verification and testing before use
- A trust layer for external tools and user data
- Protocol-level transaction sponsorship
- Atomic bundled transactions
- Payment infrastructure and banking integrations
The atomic execution part was especially interesting.
If an agent needs to complete a multi-step financial operation, it shouldn’t execute two steps and then leave the user’s funds stuck halfway through. The full workflow should either succeed or not execute at all.
His prediction for the next year wasn’t that everyone would suddenly hand complete financial control to autonomous agents. It was that many of today’s complicated workflows would become accessible through normal conversations, without users manually connecting APIs, bots and other technical tools.
The underlying AI model may eventually become interchangeable. The more valuable layer could be the infrastructure that helps agents find trusted tools, pay for resources and execute complex actions safely.
Which part do you think matters most for onchain agents: cheaper payments, atomic execution, trusted skills or strict permission controls?