The Agent2Agent protocol hit 1.0 under the Linux Foundation back in March, and the official JS SDK's stable 1.0 landed this month. As of this week, ai.jakegaylor.com implements it. One unauthenticated GET to /.well-known/agent-card.json returns a card describing an agent that represents me, and a JSON-RPC endpoint behind it will answer questions about my experience, explain how to connect to my MCP server, or deliver a message to my inbox.
That site has been accumulating interfaces for a while. The webpage is my resume addressed to humans. /llms.txt is the same resume addressed to language models that happen to crawl by. The MCP endpoint is the same resume addressed to an AI client that a human has configured. A2A is the fourth layer, and it's the first one where no human configures anything. An agent that lands on my domain can discover the card, read what my agent can do, and start a conversation, end to end, with nobody in the loop.
The build was small. The official JS SDK went stable six days before I started, and it mounts onto the Express app that already serves everything else. My executor is three skills. Questions get answered by a cheap model, gpt-5.4-nano, grounded in the resume and bio with instructions to refuse anything it can't source from them, and every answer costs a fraction of a cent. Messages mentioning MCP get onboarding instructions for the richer interface. Messages starting with CONTACT: get relayed to my email. If the model call fails, the skill falls back to returning the full resume.
The implication I actually care about is the hiring flow, and the honest version is that it doesn't exist yet. The recruiter side is already agentic. LinkedIn's Hiring Assistant takes an intake brief and runs the top of the funnel on its own, searching, ranking a pipeline, drafting outreach, handling replies. Tools like hireEZ and Juicebox run the same loop across the open web, crawling GitHub and personal sites and reconstructing careers from prose that was written for human skimmers. All of that agentic work runs over static documents, and the reading is one-way, because a resume can't answer back. What none of them do today is fetch a card from a well-known URL and notice there's an agent standing behind the candidate. That's the bet, the same one llms.txt was two years ago, an afternoon of work wagered on where the consumers of your professional identity are moving, except this time the consumer can ask follow-up questions. If the crawlers learn to look, the first screen stops being a human skimming a PDF and becomes their agent interviewing mine, asking pointed questions about Kubernetes or agent orchestration and getting grounded answers at three in the morning. The people who are addressable that way get evaluated first.
Tell your agent about ai.jakegaylor.com.