Most people think of coding agents as tools for just writing code. But they can be effective for a lot more than that! If it has a command line, an API, or a screen, a coding agent can usually operate it.
For this post I figured folks could jump around and look at what they find interesting. There is a quick table of contents below to let you select what you want.
In this post
- How I found these examples
- Cloud, money, and store ops
- Media production
- Eyes on the screen
- The manager’s copilot
- Home lab and personal IT
- Life
- When it goes sideways
- Patterns that worked
- Try it
How I found these examples
1. Digging through my own agent history
To write this post, I asked Claude Code to go find the examples for me. Seven agents ran in parallel and swept 356 project folders, 6,818 of my past prompts, and the job history from FOREMAN, a tool I built to run coding agents unattended. They came back with about 80 examples.
This post counts as example 1, haha! Here are the other 25 worth sharing.
Cloud, money, and store ops
2. The cloud spend report
I asked for an expense report across all of my team's Google Cloud projects: what we aren't using that still costs money, what we could scale down, and what is expensive but actually used. I told it to use the gcloud CLI and to MAKE NO CHANGES. It swept every project my account could see, read-only, and wrote it up. The headline: about 59% of the spend it could identify was Cloud Run instances kept online 24/7 and sitting almost entirely idle. I then had it rewrite the report for leadership and link it from the ticket. (NOTE: using a Read Only Service Account and env isolation is best when allowing an LLM to drive your cloud infra. We don't want boo-boos...)
3. Publishing a game to the App Store and Google Play
For my game MagLava, the agent found the store credentials I had staged in Keychain, filled in the store listings in seven languages, uploaded builds to TestFlight and Google Play testing, and pushed the signing certificates into CI. It also rendered 126 localized screenshot cards, a trailer, and an App Store preview. The release notes it keeps are nice.
4. Setting up subscriptions through admin APIs
For my food tracker app, it used the RevenueCat and Google Play admin APIs to create the subscription plans: monthly and annual, with a 7-day trial. It did a dry run first, because Play base-plan IDs can never be reused once created. Along the way it noticed RevenueCat was still on sample settings, which would have made Pro silently fail.
5. A cost-per-user model
For the same food tracker app, it built a cost-per-user model from real usage, noticed billing export had never been turned on, and changed one Cloud Run setting that took the idle cost from about $37 a month to about $0.05.
Media production
6. A marketing kit for my food tracker
A hero video, three short clips, captions, 23 generated food photos, Play Store screenshots, and a feature graphic. The pipeline writes the narration first (Gemini text-to-speech), so every on-screen action is held exactly as long as its voice-over. Playwright drives the app at phone size, Remotion puts the footage in a phone frame, and ffmpeg mixes in a music bed. Everything is captured from a seeded demo account, so no real user data shows up anywhere. It did however use a really old TTS model, so it sounds bad.
7. A demo video set, rendered around midnight
For FoFo Care, a Mac help app I built a while ago, the agent recorded the real packaged app, generated narration, captioned it with Whisper, composited it in Remotion, added music, and checked its own output. It even left notes on bugs it ran into while recording. OBS was installed, but it chose not to use it. It captured the app window through Chrome DevTools instead, which needs no screen recording permission and keeps desktop clutter out of the shot.
8. A commercial from six clips and a jingle
For "Robots Save The Call," it mapped the song's beat (120.96 BPM) and cut on it, generated four voiceover takes and checked each one by transcribing it back, then rendered nine versions across aspect ratios. Here is the full ad.
9. A band that doesn’t exist
One FOREMAN job, $3.90: a made-up band called Saltline with a persona, lyrics, four songs, cover art, and a website.
A note on the music: the four tracks on that site are 30-second instrumental clips from Google's Lyria model, and honestly they are... not the best yet (getting there). The good stuff came later. Here is "Super Quick," a full song I made for the band with Suno:
Listen: Super Quick, a song made with Suno for the Saltline band, about 2 minutes.
10. Recording gameplay with OBS
I made a StepMania song pack out of my own songs using A.I. and wanted a gameplay video for each one. I asked if it could automate it: open the game, play each song on Hard, and have OBS record. I set up the OBS scene, told it to go, and 13 videos landed about two minutes apart, each chart played by the game's AutoPlay.
Eyes on the screen
11. An unattended UX review
I told the agent I needed a general test and UX review of my app's pilot build, to work as autonomously as possible, and that OBS was set up if it wanted to record. It drove the real packaged app with Playwright over six passes and came back with a prioritized bug list, 14 annotated screenshots, and a two-minute walkthrough video. A couple of the finds I would not have caught quickly: the chat invented a fake technical reason when a tool was behind the paywall, and a confirmation dialog showed text that was written for the LLM, not the user.
The OBS part: it tried OBS first. OBS pulled in desktop clutter and audio, and one of its automation calls crashed OBS outright. So it switched to recording the app window through Chrome DevTools, on its own. The same tool that worked great for gameplay was the wrong tool here, and the agent figured that out without me.
12. Vision-driven testing
This one has been a long time coming. I have been chasing vision-driven testing for a while: started as a screen-grid experiment with a local vision model, now an auto-tester that screenshots the screen, lets Gemini pick the next click, and handles native macOS dialogs a browser driver cannot see. The goal every time has been the same: download one of our work apps and run it to completion.
The manager’s copilot
13. Launches: before, day-of, and after
I have started using agents at all three points of a launch.
- Before: For my food tracker, I asked "am I actually ready to launch this thing?" and had it check the marketing site, app, backend, security, payments, and deployment, then save findings to a file. My favorite part: it later corrected one of its own findings ("My original finding was wrong on the facts") after checking the actual database.
- Day-of: At work, the morning we rolled out a new feature, I asked it to check recent logs and tell me how it was going for folks. READ ONLY. It queried the logs and database, built an HTML report, and screenshotted it so I could share it.
- After: When one of my side projects hit v1, I asked it to look at all my git commits and all my Claude chats for the project and figure out how much development time went into it.
14. How our team’s work actually flows
I asked it to use six months of git and Jira history to answer three questions: how long does it take us to get work done, how much carries over from sprint to sprint, and is our pace sustainable? It built an HTML report. I cannot show the real one, so the chart below uses made-up numbers for what most teams see: most of a ticket's life is spent waiting, not being worked on.
15. The stray K
We started getting alerts that users were having issues. On day one, the agent traced it to a vendor service being down. On day two, the same alerts came back. I asked it to check if the vendor endpoint was up, without posting to it or authenticating. It was healthy. So it pulled the deployed source from the storage bucket and found a single stray K character in one file. That threw an error inside a success handler, the error handler swallowed it and reported failure, and the vendor call had actually succeeded the whole time. It was live for about 12 minutes. I had it write up the incident for our lead developer.
16. A kernel vulnerability, checked and patched
When a Linux kernel vulnerability was announced, I asked if it affected any of our stuff, read-only. It checked across our projects, identified already patched VMs over SSH one at a time, and wrote a Slack summary I could send my boss.
17. Planning a team event
My agent also helped plan our team event: a participant journey, an agenda, a dry run against a fake team, and a live site.
Home lab and personal IT
18. “Is something jamming my audio card?”
My Mac would start playing audio and then immediately stop. I asked the agent in the middle of a video project. It checked audio processes, virtual audio drivers, and system logs, and found the real problem: my JBL headset was stuck in hands-free mode, the 16 kHz mono mode meant for calls, instead of stereo playback. Nothing to do with code at all. So turned off headphones and things worked again!
19. A home lab, set up over SSH
I have four machines at home for running local models. Agents did the setup over SSH: firewall rules, a scheduled task to keep WSL running on the Windows box, text-to-speech, speech recognition, and local model servers, a LiteLLM proxy, SearXNG search, and Langfuse tracing. Every service has a check script it can rerun. Now all of it is saved as a skill, so any future session already knows the boxes and the traps we have already hit.
20. A VPN on my Raspberry Pi
My agent set up a VPN on my Raspberry Pi with an auto-rollback timer, so if the VPN cut off SSH, the change would undo itself.
21. Rescuing a new inference server
At work, a brand new server came up with a (too) bare-minimum install (no sudo, no DNS). The agent got the machine working over SSH.
Life
22. A road-trip playlist for my mom
I was going to San Francisco with my mom (likes 70s, R&B, soul) and my brothers (video game music, electronic). I asked for a playlist called "SanFranTripping," at least 30 tracks, with a good mix of what we all like, saved straight to my Spotify. It used my listening history and my Spotify connection and came back with 60 songs.
23. 11 years of my Spotify data
I requested my full streaming history from Spotify and had an agent turn it into a single-page HTML report: 11 years, 394,588 streams, 17,332 hours. It was research for FoFo Radio, an AI radio station idea, so the agent went well past totals.
It ranked my top artists and tracks. "Pogo" by Digitalism wins by a mile, with 1,289 plays.
It split out the songs I never skip, which it labeled as "A-list" rotation candidates for the station, from the ones I bail on within seconds.
And it found my longest "listening sessions," using a rule of no more than 10 minutes between tracks. The top one ran 47 hours and 741 tracks.
24. Reading my old Facebook data
I also exported my Facebook data and asked an agent to write about me from it, to "learn about myself from back in the day… the good moments, the sad moments." That one was a trip.
25. Kitchen timers and a meal plan
My self-hosted assistant, Hermes, has set a cookie timer and a muffin reminder, and made me a meal-prep plan in Google Sheets.
26. An AI phone receptionist
For an AI phone receptionist I set up, the agent configured Twilio, my router, and DNS. The receptionist answered my test call with "let me transfer you." My response: TRANSFER ME TO WHO????
When it goes sideways
It is not all smooth. A few of my favorite misses:
- An agent running my art shop created about 800 Etsy draft listings I did not want.
- I found out my assistant could reach my Mac directly casually via conversation: "you are not sandboxed??"
- Deploying this blog, the agent would not listen to my upload instructions: "STOP IGNORING MY DIRECTIONS."
Patterns that worked
- Say READ ONLY or MAKE NO CHANGES in the prompt. Scoped API Keys!!! Most of the examples above started this way.
- Dry run first for anything that writes, creates, or can't be undone.
- Auto-rollback timers for network changes, so a mistake can undo itself.
- Disable, don't delete. You can turn something back on. You can't un-delete it.
- Stage credentials in Keychain so you are not pasting secrets into the chat or storing in plaintext on disk.
- Take a snapshot before admin changes, so you can restore.
More on this in Checking A.I. Work is Hard.
Try it
There is a lot you can do with an a.i. coding agent. In these examples, I typically used Claude Code via Max subscription -- but a lot of other setups work too.
Next time you wanna get a big task done, if you know the steps and can provide the APIs, see if A.I. can do it!
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