Common AI Coding Agent Problems: The 5 Issues + a Weekly Fix Routine

The first week we let a coding agent run unsupervised on a real project, it quietly did three things we didn't notice until later: it filled a chunk of our SSD with log files, left a dev server running on a port we needed two days later, and pushed our token bill up faster than we expected. Nothing exploded. That's the trap. AI coding agents fail slowly and in the background, so the problems pile up before anyone connects the dots. This is the hub that ties together the five recurring problems we keep hitting, with the quickest fix for each and a maintenance routine that takes a few minutes a week.

Quick Answer

The most common AI coding agent problems fall into five buckets: disk and SSD wear from heavy local logging, destructive file or command actions, token costs that climb because your whole context is billed every turn, rate limits or sudden quality dips, and background processes the agent leaves running. None of these mean you should stop using agents like OpenAI Codex, Claude Code, or Cursor. They mean you need a light maintenance routine: commit before you let an agent loose, run in the most restrictive permission mode that still works, glance at your usage daily, and once a week prune logs, kill stray processes, and check the bill. This guide is for solo creators, product managers, and small teams running agents locally, not large platform teams with dedicated infra. Each problem links to a deep-dive sibling. As of June 2026, tool defaults change between versions, so always confirm against the official docs.

What This Problem Is

"AI coding agent problems" is the catch-all for the operational side effects of letting a model run shell commands, edit files, and call an LLM on your machine. The agent itself can be excellent at the coding; the issues show up around it: disk filling, files at risk, costs creeping, throttling, and a messy environment. In our testing the single best framing is this: an AI coding agent is a powerful but careless intern. It will do the task you asked and also leave the kitchen a mess unless you set up guardrails and a cleanup habit. The five problems below are the mess, and a short routine is the cleanup.

Who Should Care

• Best for: solo developers, bloggers, and product managers running OpenAI Codex, Claude Code, or Cursor locally on a personal or small-team machine, including non-engineers who "vibe-code" small tools and sites.
• Also useful for: small teams sharing a workflow who want a baseline checklist everyone follows before and after agent sessions.
• Not a concern for: teams whose agents run only in fully managed, ephemeral cloud sandboxes with platform-level isolation, billing caps, and log rotation already handled by infra. You still benefit from the safety section, but the disk and stray-process items matter less.

What You Need

The Fix at a Glance

Here is the one-screen map: each problem, the symptom you'll actually notice, the quickest fix, and the deep-dive guide for the full playbook. Skim this, then bookmark the routine at the bottom.

Step-by-Step

If you only do one thing after reading this, set up the routine. Here's how to roll it out from scratch:

1. Before any agent session, commit. Make sure your working tree is clean and committed (or stashed) so every change the agent makes is revertible with one command. A dedicated branch or git worktree gives you a disposable copy to throw away if a run goes wrong.
2. Set the permission mode deliberately. Start in the most restrictive mode that still lets the task get done — read-only or plan first, require approval for commands. Loosen only when you trust the direction. As of June 2026, mode names and defaults differ between tools and change between versions; check each tool's official docs.
3. Isolate the environment where you can. Run the agent in a container or VM with only the project mounted, and with scoped, short-lived credentials — never your production database creds or broad cloud keys. This caps the blast radius of any mistake and contains stray processes.
4. Glance at usage daily. Open the agent's usage view (for example /usage in Claude Code) once a day to see token spend and whether you're near a rate limit. A 10-second look catches a runaway session before the bill does.
5. Clear or compact between unrelated tasks. Reset the conversation so you stop paying to resend a giant history every turn, and so context from the last task doesn't confuse the next.
6. Weekly cleanup. Prune the agent's local log store (Codex's logging is the big one), kill any dev servers or containers left running, and check your bill or usage trend against the week before.
7. Keep a backup assistant configured. So a rate limit or a temporary quality incident on one provider doesn't block your whole day.

Copy-and-Paste Commands

This is the maintenance routine as a copy-paste checklist plus the diagnostic commands behind it. Run the diagnostics, then keep the checklist somewhere you'll see it. Treat any path as illustrative — confirm the exact path on your machine before deleting anything.

# ===== AI CODING AGENT MAINTENANCE ROUTINE =====
# BEFORE WORK (every session)
# [ ] git status is clean / committed (or on a throwaway branch or worktree)
# [ ] permission mode set to the most restrictive that works (read-only / plan; approval on)
# [ ] agent running in an isolated env with scoped, short-lived credentials

# DAILY
# [ ] glance at the usage view (e.g. /usage in Claude Code) for spend + rate-limit status
# [ ] /clear or /compact between unrelated tasks

# WEEKLY
# [ ] prune the agent's local log store (Codex logging is the big one)
# [ ] kill stray dev servers / containers left running
# [ ] check the bill / usage trend vs last week

# ----- DIAGNOSTIC: how big is the Codex log store? -----
# macOS / Linux:
du -sh ~/.codex

# Windows PowerShell:
Get-ChildItem "$env:USERPROFILE\.codex" -Recurse -File | Measure-Object Length -Sum

# ----- DIAGNOSTIC: what dev servers / processes is the agent leaving behind? -----
# macOS / Linux — see what is listening on a common dev port (example: 3000):
lsof -i :3000

# Windows PowerShell — see what is listening on a port (example: 3000):
Get-NetTCPConnection -LocalPort 3000 -State Listen

# ----- DIAGNOSTIC: any containers the agent started still running? -----
docker ps

# NOTE: close the agent before pruning its logs, and confirm the exact path first.
# Delete only the specific log/sqlite/WAL files the deep-dive guide identifies,
# never a broad rm -rf on a guessed path. See the file-safety guide.

Example: What You'll See

A typical "something's off" week looks like this. Free disk space has dropped a few gigabytes with no new projects, and a size check on the Codex home directory comes back surprisingly large:

$ du -sh ~/.codex
 47G    /Users/you/.codex

$ lsof -i :3000
COMMAND   PID   USER   FD   TYPE  NODE NAME
node    48213    you   23u  IPv4   TCP *:3000 (LISTEN)   # left over from a run two days ago

$ docker ps
CONTAINER ID   IMAGE        STATUS          PORTS
9f2c1a7b4e8d   node:20      Up 2 days       0.0.0.0:5173->5173/tcp   # still running

Meanwhile the usage view shows you're 80% through a rolling window earlier in the day than usual, and output on a tricky task felt a little worse this afternoon. Individually each is minor. Together they're the five problems showing up at once.

Example: After the Fix

After one pass through the weekly cleanup — close the agent, prune the log store per the deep-dive, kill the stray node process and the leftover container, then a quick usage check — the same diagnostics look calm:

$ du -sh ~/.codex
 312M   /Users/you/.codex          # back to a sane size

$ lsof -i :3000
                                   # nothing listening — port is free

$ docker ps
CONTAINER ID   IMAGE   STATUS   PORTS    # no stray containers

Disk space is reclaimed, the port is free for today's work, and because you committed before the last agent run, nothing you cared about was at risk. The routine took about five minutes.

Tested Notes

• Input type: a real multi-day stretch of running coding agents locally on a small-team project, then auditing what they left behind (disk, processes, usage).
• Tool used: Claude Code and OpenAI Codex CLI, with Cursor referenced for comparison.
• Best result: the weekly cleanup plus a "commit before you run" habit caught every issue early; the disk reclaim and stray-process kill were the highest-impact, lowest-effort wins.
• What failed: relying on memory instead of a written checklist — without the routine pinned somewhere visible, the weekly steps slipped and the problems crept back.
• Manual edits still needed: confirming the exact log paths before deleting, and tailoring permission modes per tool — defaults differ and change between versions, so we verified each against the official docs.

Pitfalls We've Actually Hit

The biggest one: assuming "I told the agent not to" is protection. In our experience natural-language instructions are not a security boundary — real isolation is. We've had an agent do something we'd explicitly asked it not to, simply because the permissions allowed it. Telling it "don't touch X" is a hint, not a fence; the fence is the permission mode, the deny-list, and the container.

The second: deleting a log store and expecting space back instantly. One user reported that deleting the store didn't always immediately reclaim space, and we've seen similar lag. Close the agent fully, confirm the path, and re-check size afterward rather than assuming it worked.

The third: panic-rewriting prompts when output dips. As of June 2026, a sudden broad quality drop is more often a temporary, known incident than your prompt going bad — check the provider's status or engineering blog before tearing your setup apart.

Common Mistakes

1. Running in the loosest permission mode by default because the prompts are annoying. Convenience now, a deleted file later. Start restrictive and loosen deliberately.
2. Never looking at usage until the bill or a hard pause arrives. A daily 10-second glance at the usage view prevents both surprises.
3. Letting log stores and dev servers accumulate across weeks. Disk fills and ports stay busy; a weekly prune-and-kill pass keeps it boring.
4. Treating "it got dumber" as a permanent downgrade and overhauling everything, when it's usually a temporary bug that gets fixed.
5. Skipping the pre-run commit. Without a clean, committed starting point you have no easy undo when an agent run goes sideways.

Tool Alternatives

The same five problems show up differently across the popular agents. As of June 2026, behavior and defaults change between versions — confirm against each tool's official docs.

FAQ

What are the most common AI coding agent problems?

In our experience they cluster into five: disk and SSD wear from heavy local logging, destructive file or command actions, token costs that climb because context is billed every turn, rate limits or temporary quality dips, and background processes the agent leaves running. The agent's coding can be great while these operational side effects pile up quietly. The fix isn't to stop using agents — it's a light maintenance routine. Specifics and defaults change between versions as of June 2026, so confirm against each tool's official docs.

Should I run my AI coding agent in a restrictive permission mode or just trust it?

Start restrictive. In our testing the safest default is the most limited mode that still lets the task finish — read-only or plan first, with approval required for commands — then loosen deliberately when you trust the direction. Natural-language instructions like "don't delete X" are not a security boundary; the permission mode, deny-list, and container are. The cost of a restrictive start is a few extra prompts; the cost of a loose one can be a lost file. As of June 2026, check your tool's docs for exact mode names.

Why did my AI coding agent suddenly get slower or seem dumber?

Usually one of two things, and it's worth telling them apart. Either you hit a usage limit and got paused, or output quality genuinely dipped. A sudden, broad quality drop is more often a temporary, known bug than an intentional downgrade — Anthropic has published engineering postmortems for such bugs, and OpenAI leadership has acknowledged routing issues. Check the usage view first, then the provider's status or engineering blog, before rewriting your prompts. As of June 2026, verify with the official usage docs.

How do I stop my coding agent from leaving servers and processes running?

Make it part of the weekly routine and isolate the environment. After sessions, list what's listening on your usual dev ports and what containers are up, then kill anything stray — the diagnostic commands above show how on macOS, Linux, and Windows. Better still, run the agent in a container or VM so leftover processes die with the container instead of polluting your host. Keeping the agent isolated is the cleanest long-term fix; the kill step is the catch-all in the meantime.

Will an AI coding agent really wear out my SSD?

Worth managing, not worth panicking over. SSDs have finite write endurance, and sustained background writes use some of it up. One Codex user estimated cumulative writes on the order of hundreds of terabytes per year in a GitHub issue — that's a user estimate, not an official figure, and modern consumer SSDs carry high endurance ratings. So treat the heavy local logging as something to prune periodically rather than a reason to expect imminent failure. The deep-dive guide covers the exact files to clear safely.

Final Recommendation

You don't need a platform team to run AI coding agents responsibly — you need a routine. Commit before you run, work in the most restrictive permission mode that still gets the job done, isolate the environment when you can, glance at usage daily, and spend five minutes a week pruning logs, killing stray processes, and checking the bill. Each of the five problems has a deep-dive sibling with the full playbook; this hub is the map and the checklist that ties them together.

👉 Bookmark this page and copy the maintenance routine from the Commands section into wherever you keep your dev notes, then work through the four deep-dive guides below as each problem comes up. Start with the one that's biting you today.

Related Guides

• Is Codex eating your disk space? Stop the SSD wear — the deep dive on problem 1.
• Stop your AI coding agent from deleting files — the deep dive on problem 2.
• How to cut AI coding token costs — the deep dive on problem 3.
• Claude and ChatGPT rate limits explained — the deep dive on problem 4.
• ChatGPT vs Claude vs Gemini for coding web apps — pick the right agent in the first place.
• An AI subscription audit workflow — keep the whole stack's spend honest.