Why agent-friendly

Agents read first

Recruiters, hiring managers and clients increasingly ask an AI assistant before they read a page: "Summarise this candidate", "Is this person a fit for our Head of AI role?", "Who could help us with an AI strategy?" The agent fetches, reads, filters and summarises. A personal website that is only a nice-looking page leaves that agent guessing — scraping layouts, missing context, inventing the gaps.

Jenks's work has always been about this kind of leverage. He spent years making platforms usable by other builders: APIs and SDKs at Xero, a developer platform at Linktree, ecosystems at Filecoin and Babylon. His leadership motto — "I lead by helping my team glow" — extends naturally to AI: build the interfaces that let other agents do good work. So his own site is built the way he would advise a company to build a platform: for the people and the agents that use it.

What agent-friendly means here

• Discoverable: llms.txt at the root lists everything an agent needs; /brief.md is the one-page start; /.well-known/site-agent.json and /openapi.json describe the interfaces.
• Structured: /experience.json and /llms-full.txt give the whole corpus with tags, role lenses, dates and key results, so an agent can filter instead of guess.
• Callable: a remote MCP server, a REST API and a CLI that doubles as a local MCP server — the same tools everywhere, generated from one spec.
• Honest: answers are grounded, separate has done from could do, and state gaps (see Grounding and honesty).
• Documented for agents: these docs follow Diátaxis — tutorials, how-to guides, reference and explanation — and every page is also served as raw Markdown.
• Multilingual: seven languages, with the same structure in each.

Why Diátaxis

Agents and people come with different needs: learn the system, complete a task, look up an exact parameter, or understand a design choice. Mixing those makes every page worse. Diátaxis keeps each page to one job, which also makes the pages easier for an agent to retrieve and quote.

The project rules

Two rules keep the site agent-friendly as it grows. They are written into the repository's AGENTS.md, so any AI working on the code follows them:

1. Every new feature ships to every surface. If a capability is added for people, it is also added to the spec — and therefore to the REST API, the MCP servers, the CLI and the generated reference docs — and the downloadable CLI binaries are rebuilt and published on the agents page.
2. Every change covers every language. Content and interface text are updated in all seven languages: English is authored, and the other six are regenerated and checked against the glossary before release.

The payoff

An agent that can call list_experience with lens=solution-architect gives its user an accurate, sourced answer in seconds. That is better for the person asking, better for Jenks, and a working example of the kind of AI-ready organisation Jenks helps companies build.