Ai-Agent

Know Your Agent (KYA): AI Agent Identity, the Standards Race, and What Is Actually On-Chain

When you open a bank account you show an ID. When you sign up for an exchange you take a selfie. That procedure is KYC (Know Your Customer), and since the FATF was founded in 1989 it has been the standard way to keep illicit money out at the entry point of the financial system. Now the entity signing contracts, sending payments and swapping tokens on a DEX is increasingly not a person but an AI agent, and the same question comes back in a new form. The agent that just sent a payment request to my API: who is it, who built it, and what was it actually authorized to do? The trust layer that answers that question is being called KYA (Know Your Agent).

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The A2A (Agent-to-Agent) Protocol: How Agents Delegate to Each Other, and Where Payments Plug In

Every time this blog has covered x402, UCP and MPP or Know Your Agent, the term “A2A” showed up as an assumption. An agent hands work to another agent, pays for it, checks who the counterparty is. All of that sits on a lower layer: how do two agents talk in the first place? The attempt to standardize that layer is the Agent2Agent (A2A) protocol.

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AI Self-Improvement Starts Outside the Model

When people talk about AI self-improvement, the usual image is a model rewriting its own weights. Lilian Weng’s July 4, 2026 post on Lil’Log argues the near-term version looks different. The recursive self-improvement (RSI) we can actually observe today shows up first in the system around the model, not inside it. That system is what she calls the harness. This post walks through her argument: what a harness is, where the optimization target is moving, what a striking SWE-bench number actually means, and what breaks if you get the risk boundaries wrong.

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Apify x402 and Coinbase Wallets, in Depth: How Agents Buy Web Automation Tools Directly

Apify, the web scraping platform, announced x402 support: AI agents can now run more than 20,000 Apify Actors by paying with USDC on Base, with no Apify account and no API key. Apify frames the prior x402 ecosystem as roughly 2,000 endpoints, so by its own count this integration expanded the paid-tool surface by about 10x.

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Hermes Agent v0.18: When a Self-Improving Agent Gets MoA

Nous Research’s open-source agent Hermes Agent shipped v0.18.0 on July 1, 2026. The release notes call it “The Judgment Release.” Reading this as a routine feature update misses the point. The official docs describe Hermes Agent as “the self-improving AI agent,” and the project is built around a loop that accumulates memory and skills the more it gets used. This post covers how that loop was refined in v0.18, and what role Mixture of Agents (MoA), now a first-class model choice in the same release, plays inside it.

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AI Agent Commerce and Coordination: ERC-8183, ERC-8226, ERC-8001, ERC-8041

The ERC-8004/8126/8196 trust stack defines three layers: agent identity, security verification, and policy-constrained execution. It identifies an agent, scores its risk, and keeps it acting within the limits an owner has set. All of that answers one question: can I trust this agent to act on my behalf?

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ERC-8004 Agent Reputation: On-Chain Registration and Lookup

A prior post on this blog covered the ERC-8004/8126/8196 trust stack: three Ethereum registries that handle agent identity, reputation, and validation. If you want the conceptual overview first, start there. This post is about something narrower: how feedback actually gets written to the Reputation Registry, how to read it back, and what services are available today to browse and index that data.

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The AI Agent Trust Stack: ERC-8004, ERC-8126, and ERC-8196

When an AI agent acts on behalf of a user (spending funds, calling contracts, accessing paid APIs), the obvious question is: how do you know this agent is safe? The Ethereum community has three standards that address different layers of that question. They don’t solve the whole problem, but they’re building toward a coherent stack.

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Open Knowledge Format: A Shared Vocabulary for Agent Knowledge

When AI agents fail in production, the model is often not the problem. The missing context is. Table schemas, metric definitions, runbooks, join paths between systems, and API deprecation notices are scattered across catalog vendors, internal wikis, code comments, and personal notes. Every agent developer solves the same context assembly problem from scratch.

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Future AGI: Evaluate, Observe, and Improve AI Agents in One Place

If you have shipped an AI agent, this will sound familiar. The demo runs fine. Then it hits production, the hallucinations start, and you can’t tell what went wrong or why. So you bolt on one tool for evals, another for tracing, another for guardrails. The real problem is that none of them talk to each other, so the loop you need to actually fix things never closes.

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