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Writing

Selling News to Robots

Yesterday's tour of the whole protocol ended with a loose thread: "A knowledge economy ends with a payment — and RFC-0005 is still sitting at the RFC stage, waiting."

It didn't wait long. v0.25 landed on main the same day: Economic Metadata, the full promotion of RFC-0005. payment.methods[] — free, x402 micropayments, metered billing, subscriptions — plus per-tier rate_limits, all at manifest and unit level. Nothing of RFC-0005 remains RFC-only.

Which means something new is possible on the agentic web this weekend that wasn't possible last weekend: you can open a shop. So let's open one — a newswire that sells to agents — and then play the customer: an agent with a funded wallet, a briefing to write, and a budget. Step by step, both sides of the counter.

The Agentic Web Has No Login Page

Think about what makes the human web economically viable. Not the browser. Not HTML. It's the login page — and everything it implies. Paywalls, licenses, subscriptions, terms of access. The mundane machinery that lets someone publish valuable knowledge without giving it away. Remove that machinery and the web would contain only what people are willing to publish for free.

Now look at the agentic web. Agents consume knowledge from manifests, MCP servers, and context files across organisational boundaries — and there is no equivalent machinery. A knowledge source is either open to every agent that finds it, or it's locked behind a bespoke API that no standard agent can negotiate. Nothing in between. No standard way for a publisher to say "this knowledge is for certified consumers only — prove who you are."

The consequence is quiet but enormous: the knowledge layer of the agentic web contains only what publishers are willing to give away. Authoritative sources — legal data providers, regulatory interpreters, standards bodies, paid research — stay off it entirely. So agents answer compliance questions from scraped blog posts instead of authoritative guidance, because the authoritative guidance has no way to come online on terms its publisher can accept.

KCP v0.22 and v0.23, both shipping today, are the missing machinery.

One Agent's Journey Through the Whole Protocol

This morning the Knowledge Context Protocol got its login page — v0.22 and v0.23, the consumer half of the trust model. This afternoon, v0.24 landed on main: Org-Federation, from RFC-0011. The enterprise front door.

That's twenty-four versions in six months — v0.1 shipped January 10th. And with the front door in place, something has quietly become true: an agent can now traverse the entire protocol, from "I know nothing but a company domain" to "I hold a signed receipt for the restricted knowledge I just consumed", and every step of that traversal is declared, verifiable, and standard.

So instead of another release note, let's take the tour. One agent, one traversal, every layer annotated with the release that built it.

Three Hooks That Give Claude Code Memory

Every Claude Code session starts from zero. You know your codebase, your conventions, your past decisions. Claude doesn't — until you explain them. Again. Every time.

This is not a Claude problem. It's an architecture problem. The context window is the right unit of work, but it has no built-in mechanism for accumulating knowledge across sessions.

I've been running three passive hooks to fix this for months. Today I packaged them up: kcp-hooks.

Production Scars Are Architecture

A production scar is not a bug you fixed. It is a category of failure that was surprising enough to cause architectural change — something you now defend against mechanically because trusting the model to avoid it didn't work.

Santander AI Lab called their open-source release "battle-tested solutions from production scars." ExoCortex has its own. Six of them. Each one left a hook file on disk that implements the lesson. This post documents what failed, why prose instructions couldn't fix it, and what the mechanical fix looks like.

Your AI Agent Does Not Know the Law (and How to Fix That)

You're building a product. It handles personal data. You've added an AI assistant that helps customers understand their compliance obligations. Good instinct, bad outcome -- because the assistant will tell a customer their processing of health data is fine since they have consent. It will say this fluently, with bullet points, citing GDPR Article 6. It will be wrong.

Article 9 requires a separate legal basis for special category data. Consent under Article 9(2)(a) must be explicit -- a higher bar than the regular consent in Article 6(1)(a). The agent doesn't know this, because it has no authoritative source for it. It's working from training data where "consent" is the answer to most GDPR questions.

This post shows the architecture that fixes that. Six layers, each solving a distinct failure mode, each buildable independently. By the end you'll have a pattern for turning any regulation into machine-readable knowledge, wiring it into an agent, and proving the agent's answers are correct.