# Aaron Chartier

Aaron Chartier is a technologist working on agent orchestration, context engineering, and GenAI infrastructure and security — the unglamorous discipline of getting AI agents to do real work reliably.

His path runs from helpdesk through IT operations and process engineering into AI leadership: sixteen years in technology, the last several leading applied-AI work in a large enterprise. The operational background is the edge — he understood how enterprise systems actually break long before he started writing specifications for agents that take instructions literally.

His throughline is specification precision: vague intent is what makes agents fail in production, and writing a spec an agent can execute reliably is a learnable, teachable discipline. He pair-codes with agents — he specifies, the agent writes, he reviews for correctness and safety — evaluates and integrates the surrounding stack (Langfuse, LiteLLM, n8n, Azure OpenAI), and mentors developers from their first prompt to production agents.

He builds tools designed to be driven by agents — inkjet, a set of agent-first command-line tools, and this site, which is built to be read by machines as well as people.

## Background

Helpdesk → IT operations → process engineering → AI technology leadership — an operations-to-AI path, not a computer-science one. Sixteen years in technology, the last several leading applied-AI work.

## Focus

- specification precision
- agent orchestration
- context engineering
- prompt engineering
- LLM evaluation
- GenAI infrastructure
- AI security and guardrails
- token economics
- technical mentorship
- process engineering
- agent-first developer tooling
- CLI design

## Ideas Aaron works through

- **Specification precision is the bottleneck** — Agents fail in production because the spec was vague, not because the model was weak. Writing specs an agent executes reliably is a learnable, teachable discipline.
- **The agent maturity progression** — Assisted → captured → compressed playbooks → self-executing skills → autonomous. Not magic — a compression-and-pattern-recognition pipeline.
- **Agentic memory** — Stop ten agents from independently making the same mistake ten times: agents that record learnings, compress them, and build on each other's.
- **The headless agentic web** — Agents want fast structured data, not rendered pages — the web is being rebuilt for machine consumers. It's why this site ships a machine layer.
- **Process engineering for agent specs** — Years spent documenting processes for humans to execute is the exact skill for documenting them for agents.

## How Aaron works

- Specs precise enough for agents — writing a problem down precisely enough that an agent can execute it, then building the agent, the tools, and the infrastructure underneath.
- Legible, observable systems — dull, legible things with clear seams and honest state.

## Projects

### inkjet — shipped

Open-source CLI for pocket Bluetooth thermal printers — text, images, and QR codes from the terminal. Ships an agent-first SKILL.md so AI agents can discover and drive it autonomously.

Link: https://github.com/AaronChartier/inkjet

## Stack

- This site: TypeScript, Astro, Tailwind, Vitest
- Tools & ops: Python, BLE, Docker, networking
- Browser platform: Web Audio, WebGL / GLSL, WebGPU, WebCrypto, BigInt

## Find Aaron

- GitHub: https://github.com/AaronChartier
- LinkedIn: https://www.linkedin.com/in/aaronchartier/

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Canonical: https://aaronchartier.com/about · Machine-readable profile of Aaron Chartier · © 2026 Aaron Chartier