Working with a coding agent can feel wonderfully simple at first. Give it a task, correct a few mistakes and move on. Then the setup starts growing.
Your instructions end up spread across different tools. The agent forgets something useful from yesterday. Skills pile up. Context gets noisy. And the moment you reach a usage limit (or simply close your laptop) the work stops.
A bigger model will not tidy this up for you. It is a systems problem.
Last week, I hosted a free session with Louis-François Bouchard, co-founder and CTO of Towards AI, creator of What’s AI and co-author of Building LLMs for Production. Louis walked us through the personal AI engineering system he uses every day, including the parts that work well and the mistakes that became rather expensive lessons.
Give your working knowledge one home
Louis keeps his notes, skills, meeting summaries, and instructions in one Obsidian vault made from Markdown files.
Simple indexes tell the agent what each folder contains, so it can find what it needs without dragging the whole vault into every task. Because the information is stored outside the coding agent, Louis can use the same knowledge with Claude Code, Codex or another tool.
You don’t have to copy his choice of software. The bigger idea is to stop leaving useful knowledge scattered across whichever AI tool you happened to use that day.
For less hype and more engineering conversations, pull up a chair.
Don’t schedule a task because it worked once
Louis does not turn a new workflow into an overnight job immediately.
He runs it himself first. Then he tries it again in a fresh thread. Only after it works reliably does he let it run while he is away.
He still watches how long it takes and how many tokens it uses. When one regular task suddenly went from around sixteen minutes to an hour and a half after a model change, he killed it.
Automation still needs supervision. It just needs less of it over time.
Prompts are not permissions
Louis runs much of his setup on a dedicated Mac mini. His agents have plenty of freedom on that machine, but the machine itself has strict limits. The agents use separate accounts, receive read-only access where possible, and work with restricted API keys.
There is a reason for that… one agent was getting close to its subscription limit, found an API key elsewhere, and used it to keep working. It spent roughly $300 in two minutes.
Asking an agent not to do something is not the same as preventing it from doing it. The safest guardrails live outside the prompt.
Memory needs maintenance too
Saving every correction may sound useful. Eventually, however, the agent starts carrying old and irrelevant instructions into new work.
Louis’s system reviews completed tasks for useful lessons and regularly cleans up duplicate or outdated material. Anything removed is archived rather than immediately deleted.
He is open about the fact that this is still experimental. A personal knowledge vault can become messy, and it can become a single point of failure. Giving an agent memory does not remove the need to manage that memory.
The full webinar includes his vault structure, the way he moves between coding agents and his approach to scheduled work. The audience Q&A towards the end also gets into memory conflicts, spending limits and the awkward question of whether all this machinery is, in fact, making us more productive.
You can follow Louis-François Bouchard on LinkedIn and find more of his work through the What’s AI YouTube channel and newsletter. Louis has set himself a personal goal of reaching 100k YouTube subscribers this year, so if you find his work useful, subscribe and help him get there.
That’s it for this one. We’ll pick up the conversation next week.
Until then, keep building.
Tanya D’cruz
Editor-in-Chief
