Herdr: AI's Real Problem Isn't the Model, It's Persistence
Herdr is not another wrapper for coding agents. It is a signal that we are entering the era of AI infrastructure, not just models.
Every time a new tool for working with AI agents shows up, the first question is usually the same:
How many models does it support?
The conversation almost always circles around integrations, commands, and which CLI it works with.
But I think Herdr points at something far more important.
Not because it supports 19 different agents.
Not because it has thousands of installs.
But because it exposes a problem almost nobody was naming out loud.
Agents no longer fit inside a chat session
Barely a year ago, “using AI to code” meant opening a window, writing a prompt, and waiting for a reply.
Today an agent can take twenty minutes, an hour, or an entire night to work through a ticket.
It can sit there waiting for a decision from you halfway through.
It can need to keep running even after you close your laptop, switch networks, or go to sleep.
That’s exactly the problem Herdr goes after: keeping real terminals open, alive, and resumable from any device, without depending on your machine staying on.
It’s not a pretty interface. It’s not an assistant. It’s the infrastructure that makes it possible for an agent to keep working after you’ve stopped watching.
The real bottleneck is no longer the model’s intelligence
For a long time, the focus was on finding “the best model.”
I’m no longer sure that’s the right question.
What good is a brilliant agent if it shuts off the moment you lose connection?
Model intelligence stopped being the limit. The limit now is infrastructure: sessions that survive restarts, state that can be recovered, agents that coordinate with each other without a human simulating every keystroke.
Herdr solves that with something deliberately simple: a single binary, no Electron, no account, no telemetry, that does exactly one thing well — keep the terminal alive and tell you, at a glance, which agent is working, which one is blocked waiting on your input, and which one is already done.
What this means for engineering teams
The engineer’s role is shifting again.
Knowing how to write a good prompt is no longer enough.
Increasingly, we’re going to need to:
- Design workflows where several agents run in parallel, not one at a time.
- Treat persistence and state recovery as part of the design, not a technical afterthought.
- Treat agents as background processes, not as conversations you open and close.
- Build on lightweight, open tools instead of depending on a single closed app.
The competitive edge no longer comes only from how well you prompt.
It comes from how well you design the infrastructure that keeps your agents running while they work.
My take
Herdr won’t be the last tool of this kind.
In a few months another one will show up, with more features, more integrations, more marketing.
But what matters isn’t Herdr itself. It’s what it reveals: AI is stopping being a feature inside a chat app and becoming an infrastructure layer that runs in the background, all the time, like any other production service.
That shift in mindset — from “conversation with a model” to “system that operates autonomously” — is, to me, the real leap.
Final thoughts
The most interesting thing about Herdr isn’t how many agents it supports.
It isn’t its feature list either.
It’s that it confirms something those of us working in quality and software development have been feeling for months: AI agents are stopping being tools we use and becoming processes we manage.
The future isn’t having the smartest chat open in a tab.
It’s building the infrastructure that lets multiple agents work reliably, persistently, and in coordination, even when no one is watching them.
What do you think?
Do you think in the coming months we’ll stop talking about “the best model” and start talking about “the best infrastructure for agents” instead?
I’d love to hear your take.