⛓️‍💥 AI Agents broke my Dev setup

Ariel Sh — Tue, 24 Mar 2026 17:02:26 +0000

I had a setup that worked perfectly for me,
3 screens (Actually 2 screen + Macbook Pro)
one with IDE (WebStorm, Vscode and from time to time just sublime).
Second with Terminal (started with iTerm2 and moved to Warp with Oh My Zsh and bunch of plugins)
and last with Browser (Web or DB).

Add to that the fact that I’ve always been a sucker for good GUIs,
so GitKraken for Git and Lens for Kubernetes completed most of my stack. It worked extremely well for years.

But when I started writing less code myself and letting agents write more of it for me, I realized this setup no longer worked.

Mixing AI into this flow (chatgpt, gemini & claude) started a butterfly effect.

My appetite has increased, I wanted to scale myself, run in parallel on many features and repositories but I was missing a way that felt natural and comfortable for me.

The moment I started jumping between multiple sessions/projects (Hey Tmux/Cmux), I got lost. More than once, I sent unrelated prompts to the wrong project or the wrong session. Creating worktrees and tracking them became messy, running multiple local envs and finding which of the port is running which branch got me dizzy.

It became obvious: this needed a proper methodology and a control plane.

Now Spec-Driven Development gave a strong theoretical answer on how you can have a system to plan and let agents execute, plan many specs let agents run and… 🎩🐇.

On paper, it felt exactly right for this new world. But when I tried the tools that exist today, they felt too clunky and too slow for me.

I had already seen how quickly I could get to clear definitions through chat or plan mode, and with the right skills, tests and loops in place, the results were genuinely impressive.

I ended up having many conversations about this with colleagues, how to take what we already know works in structured software development and make it comfortable first and foremost for us.

That’s exactly how Shep was born. A shepherd for a flock of agents.

The idea is simple: if agents are becoming part of how we build software, we need better ways to organize them, guide them, and keep context from falling apart.

According to Steve Yegge, people building tools like this are already in a later stage of the AI-assisted development journey. Maybe these tools are only for people at that stage. Maybe not.

But for me, this came from a very practical pain: I didn’t want more agent power. I wanted a better way to work with it.

Shep runs locally, doesn’t require a complicated setup, and tries to fit naturally into how different developers already like to work. It supports Claude and Cursor, Gemini & Codex, works on Mac, Linux, and Windows, open source under MIT no token, api key or register needed, no heavy platform lock-in.

There’s a lot more in the repo for anyone who wants to go deeper, but if you just want to try it:

npx @shepai/cli

shep-ai / cli

Shep AI CLI & UI - Multi-Session SDLC Control Center for AI Coding Agents

Shep AI

One command. Full lifecycle. Merged PR.

Describe a feature in plain English — Shep researches, plans, codes, tests, and opens a PR. You approve when you want to, or let it run hands-free.

Features · Quick Start · CLI Reference · Architecture · Contributing

Quick Start

# Try it instantly — no install needed
npx @shepai/cli

# Or install globally
npm i -g @shepai/cli
shep

# Browser opens at http://localhost:4050 — you're in

Features

shep feat new "add stripe payments" --allow-all --push --pr
# ↳ PRD → research → plan → code → tests → PR → CI watch — done.

Full lifecycle in one shot — From idea to merged PR: requirements, technical research, implementation plan, code with tests, PR creation, and CI fix loop
Approve or go hands-free — Three review gates (PRD, Plan, Merge) you can enable, disable, or skip entirely with --allow-all
…

View on GitHub

Would genuinely love feedback especially honest criticism from people trying to make AI-assisted development actually usable in day-to-day work.

Balagan Agent 🌪️ Chaos Engineering for AI Agents

Ariel Sh — Mon, 02 Feb 2026 18:17:12 +0000

AI agents don’t usually fail because the model is “bad”.

They fail because the real world is chaotic.

APIs time out
Tools return partial or malformed responses
Context degrades after a few iterations
Budgets get exhausted
Latency spikes at the worst moment

Most agents look great in local demos and unit tests — and then quietly break in production.

That’s the gap we built Balagan Agent to address.

👉 Product Hunt launch: https://www.producthunt.com/products/balagan-agent

What is Balagan Agent?

Balagan Agent is an open-source chaos-engineering tool for AI agents.

Instead of testing the happy path, it intentionally breaks your agent in controlled ways and measures how well it recovers.

You wrap your existing agent, define failure scenarios, and Balagan injects things like:

tool timeouts
rate limits
corrupted context
malformed outputs
latency spikes
budget exhaustion

Then it measures:

recovery behavior
MTTR
reliability score

Reports are generated in terminal, JSON, Markdown, or HTML, so you can plug them into CI or review them manually.

Why chaos engineering for agents?

Traditional software already learned this lesson:

systems don’t fail because of code alone — they fail because of interactions.

AI agents multiply that problem:

non-deterministic behavior
long-running loops
external tools everywhere
hidden failure modes

If you don’t actively stress them, you’re shipping unknown risk.

Balagan makes those risks visible before users find them.

Current integrations

Balagan Agent works as a lightweight wrapper around popular agent frameworks, including:

CrewAI
Claude Agent SDK
Examples for AutoGen and LangChain

Install via:

pip install balagan-agent

It’s framework-agnostic by design — you bring your agent, Balagan brings the chaos.

Who is this for?

If you are:

building AI agents for real users
running agents in CI/CD or production
caring about reliability, not just demos

This tool is for you.

It’s free, open source, and meant to evolve with the agent ecosystem.

Launching on Product Hunt

We launched today on Product Hunt and would love feedback from people actually building agents:

👉 https://www.producthunt.com/products/balagan-agent

Questions, criticism, ideas for new failure modes — all welcome.

Forem: Ariel Sh