I Let an AI Agent Run My Dev Environment for a Week

I've been using AI coding assistants for a while — Copilot, ChatGPT, the usual suspects. They're useful, but they're also fundamentally limited: you paste code in, you get code out, and the context resets every time you close the tab. They don't know your project structure. They don't remember your preferences. They can't run anything.

A few months ago I started using something different — an AI agent that lives in my terminal and Telegram, has persistent memory, and can actually execute things. Not a chatbot that suggests code. An agent that runs commands, reads files, schedules jobs, deploys to production, and remembers what I told it three sessions ago.

It sounded like overkill. It turned out to be the most productive tool change I've made in years.

What it actually does

The agent (Hermes, if you want to look it up) runs as a CLI process connected to my Telegram. I send it a message like "audit the portfolio project and suggest improvements," and it clones the repo, reads the source files, checks every URL in the project data against live servers, identifies placeholder descriptions and copy-pasted tech stacks, and writes up a structured analysis.

That's the simple version. Here's where it gets interesting:

• It remembers. It has persistent memory across sessions. I told it once that my portfolio uses Next.js 13.5.3 and deploys to Vercel. Three weeks later, I asked about upgrading Next.js and it already knew my version, my deploy target, and my constraints. No re-explaining.

• It schedules. I can set up cron jobs through it. "Check the portfolio site every morning and tell me if any project URLs are down." It runs the check at 9am, and if something's broken, I get a Telegram message. If everything's fine, silence.

• It delegates. For bigger tasks, it spawns sub-agents that work in parallel. "Research the top 3 approaches for adding search to my blog, and audit the project data file for stale entries" — it runs both simultaneously and merges the results.

• It has skills. Over time I've taught it procedures — how to do a portfolio audit, how to write an implementation plan, how to structure a PR. Those procedures persist as reusable skills, so the next time I ask for a similar task, it follows the established pattern without me re-explaining.

What a typical day looks like

Morning: I get a Telegram ping. "3 project URLs returned non-200 status overnight." One is a temporary DNS issue, one is a real problem. I fix the real one during lunch.

Afternoon: I'm working on a new feature for Warkas. I message the agent: "add stock alert thresholds to the inventory module, follow the existing pattern." It reads the codebase, identifies the pattern, writes the implementation, commits, and pushes. I review the diff on GitHub.

Evening: I'm thinking about a blog post (this one, actually). I ask it to draft something about AI agents in developer workflows. It pulls from its memory of what tools I use, what I've complained about, and what's actually worked. I edit the draft and publish.

None of this is magic. All of it is faster than doing it manually.

The weird parts

It's not all smooth. There are genuine oddities:

The memory thing is uncanny. When an agent says "based on what you told me in March about your deploy pipeline," it's jarring in a way that Siri remembering your name isn't. Probably because it's remembering substance, not trivia.

It makes mistakes with confidence. Like a junior dev who read the docs but hasn't been burned yet. It'll suggest a perfectly reasonable approach that happens to break something subtle. You still need to review.

You develop a delegation instinct. After a while you start categorizing tasks: "this I'll do myself, this I'll hand off." It's the same mental model as managing a team, except the team member never sleeps and works at the speed of API calls.

It changes what you attempt. Tasks I'd have deferred — "I should audit all those project URLs," "I should write more blog posts," "I should check if the SEO metadata is consistent" — I just ask the agent to do them. The activation energy drops to near zero.

What it doesn't replace

Let me be clear about what this isn't:

• It doesn't replace understanding. If you don't know how your deploy pipeline works, you can't evaluate whether the agent's suggestion is good.
• It doesn't replace judgment. It'll happily implement whatever you ask, even if what you ask is wrong.
• It doesn't replace taste. The blog post it drafts needs a human edit. The architecture it suggests needs a human review.

What it replaces is the mechanical overhead. The "clone the repo, grep for the pattern, read five files, write the change, commit, push, open PR" dance. That stuff was never the hard part — but it was always the time-consuming part.

Should you use an AI agent?

If you're a solo developer or on a small team, and you find yourself spending more time on process than on product — maybe. The tooling is still early, and there's a real setup cost. You have to configure it, teach it your preferences, build up the skill library.

But if you're the kind of person who has 15 side projects and finishes 2 of them, an agent that reduces the activation energy on the boring parts might be the difference between "I should write that blog post" and actually writing it.

I wrote this post. But the agent drafted the first version, checked my grammar, and will publish it when I say go. That's the workflow now. It's weird, and it works.