Tenon

AI made it easy to apply to everything, so people did. 67% of hiring managers say AI-generated applications have slowed the hiring process (Robert Half, 2026). Whilst 70% of hiring managers trust AI to make faster and better hiring decisions, only 8% of job seekers call AI hiring fair (Greenhouse, 2025).

I am the user: Tenon grew out of my own job search. It builds its understanding of who you are, shapes an approach for each role, and generates the materials to back it up. Real agent calls, a purpose-built design system, structured outputs: designed around the hard problems in agent-driven apps, consistency and trust. Months of product work in weeks, building with AI. I use it for every role I pursue.

The design system has a fixed set of building blocks: value displays, timelines, input controls. The agent picks which blocks to use and what content goes in them, but it can't change how they look or invent new ones. Each block validates its content before it renders.

The reasoning: if the agent controls appearance, every prompt change risks breaking the look. If the component enforces its own appearance, the agent composes freely without breaking consistency. Appearance decisions are made once, in the component, and held.

This pattern shipped as open infrastructure through 2025-26: Google's A2UI and Vercel's json-render landed on the same architecture: the agent fills pre-built components from a fixed set, and anything not in the set doesn't render. Tenon arrived there independently, with a smaller, more opinionated vocabulary.

Tenon shows the basis behind every recommendation, not a confidence score. LLM confidence is systematically miscalibrated (Xiong et al., 2025), and in controlled tests, showing confidence percentages was the least effective of three interventions for helping users calibrate their reliance (CHI 2025). Instead, each recommendation surfaces what it's based on: which parts of your profile matched, which signals from the role informed the assessment, and where the evidence is thin.

The more a decision matters, the more Tenon checks with you. New content flows without interruption. Content you've already seen or decided on shows old next to new so you can compare. Actions that change your state (dismissing a role, submitting materials) get a clear notice. This avoids the two failure modes: asking permission for everything, and asking permission for nothing.

Each role gets a composed approach: why it fits, what angle to take. Tenon then generates the materials to back it up. Blocks appear as they're generated, with honest placeholders where the agent is still working.

The underlying pattern: layout, structure, and vocabulary are fixed. The content is composed per role, tailored to your positioning and the specific opportunity. The frame is reliable. The content is specific.

A working product, designed and built end-to-end: research, product definition, design system, front-end, agent integration. Consistency, trust, and composition quality in agent-driven interfaces are design problems. I built a product to solve them.