---
title: "Why Do Parallel AI Coding Agents Fry Your Focus, and What Helps?"
url: "https://goremy.ai/blog/articles/ai-context-switching-fried-focus-software-factory"
author: Luis Chavez-Mattos
editor: Luis Chavez-Mattos
editor_title: Director, Product
published: "2026-10-23T02:40:06.393Z"
updated: "2026-10-23T02:40:06.393Z"
topic: AI at Work & Governance
description: Parallel coding agents raise output but fragment attention. Here is why context switching drains you and how a more autonomous harness cuts the babysitting.
---

# Why Do Parallel AI Coding Agents Fry Your Focus, and What Helps?

> Parallel coding agents raise output but fragment attention. Here is why context switching drains you and how a more autonomous harness cuts the babysitting.

Edited by Luis Chavez-Mattos, Director, Product · Published 2026-10-23

## Why does running parallel AI agents wreck your focus?

Parallel agents wreck focus because current agents still need supervision. You send a request, chat briefly, let the agent work, then jump to another session. Each task gets a small slice of attention, and you never settle into one problem long enough to reach flow. Human cognitive bandwidth does not parallelize. The more sessions you run, the more often you switch, and the more drained you feel by the end of the day.

## TL;DR

- Running many agents at once multiplies **context switching**, and the cost shows up as fatigue, not just slower work.
- Agents still need **babysitting** because models and harnesses are not yet reliable enough to run unattended for most tasks.
- Making an agent more capable tends to **tempt you into adding another session**, which brings the switching back.
- The proposed fix is to invest in the harness so agents **plan, implement, and validate in a loop** and bring decisions back in batches.
- A custom "software factory" approach means **defining intent up front**, letting agents run for long stretches, and reviewing the results together.
- Built-in autonomy commands help, but they do not know how *you* validate work, so you still end up cleaning up at the end.

## What is the "fried brain effect"?

The phrase came from a widely shared post by a developer named Tibo. He described spinning up many agents in parallel through the day and jumping from conversation to conversation. He said he is probably context switching a hundred times more than he did five years ago. By evening, he said, his brain was fried, with nothing left over. He added that nearly every founder he talked to described the same thing: superpowers arrived, and the pressure went up anyway.

The tweet may exaggerate for effect, but the pattern is familiar to anyone who works this way. The tool made you faster, so the expectation became that you ship more. That expectation turns idle time into guilt.

## Why is context switching so costly?

Attention is the scarce resource. Cal Newport has argued that the ability to focus is becoming the scarcest commodity of the 21st century, and he said it before coding agents existed. Agents add a new source of interruption: a notification that a session needs input.

Compare the two workdays:

| | Before agents | With parallel agents |
|---|---|---|
| Active problems | One or a few | Many at once |
| Typical block of attention | Hours | Minutes |
| Flow state | Common | Rare |
| Interruptions | Self-chosen | Triggered by agent output |
| End-of-day feeling | Tired, but focused | Scattered and exhausted |

One creator who builds with agents daily described missing the old pattern: music on, four hours straight on a single problem. He said he rarely does that now because it doesn't feel practical when agents could be producing work elsewhere. He also noted that he procrastinates by playing short chess games, which he reads as a craving for the deep focus his workday no longer gives him.

## Why do we keep adding sessions anyway?

Two forces push in the same direction.

First, the productivity gain is real. Few people want to go back to working without agents, and the creator was explicit that he wouldn't. The goal is to keep the gains and lose the fragmentation.

Second, every improvement to your setup invites more parallelism. If your rules, skills, or second brain let an agent run a bit longer, the natural move is to fill the gap with another session. You gain throughput and lose focus again. The waiting time never turns into rest or deep work, because there is always something else to start.

This is the "in-between" state. Agents are capable enough to be worth running in bulk, but not capable enough to leave alone. So we sit in the middle, supervising.

## Will smarter models fix this on their own?

Probably over time, but not yet. The argument is that as model capability improves, agents will take on a full day of work for most tasks, much like an employee with real autonomy. Then you would babysit less and could focus on one thing while background agents handle the rest.

Until then, waiting for better models means living with the fragmentation. The alternative is to improve the system around the model now, which is where harness engineering comes in.

## How does a more autonomous harness reduce babysitting?

The proposal is a trade. You give up some short-term output (fewer sessions producing work right now) and spend that time on the larger harness. The aim is an agent that works the way you work and validates the way you validate, so you can trust it with longer tasks.

The pieces named in the source:

1. **Rules and skills.** The standing instructions and reusable procedures your agent follows.
2. **A plan, implement, validate loop.** For larger tasks, the agent plans the work, builds it, checks it, and repeats without you in the middle.
3. **Intent defined up front.** You spend real time having the agent question you until you share the same understanding of the goal. Then it runs.
4. **Batched decisions.** Approvals and questions still come back to you, but grouped, so you switch context a few times instead of constantly.

The payoff is a different shape of day. Instead of dozens of small check-ins, you front-load the thinking, let background processes run for hours or longer, and review the output together.

## What is a software factory?

"Software factory" is the name the creator uses for his version of this system. In his description, he plans a larger release with an agent. The agent then splits the plan into issues and works through them in the loop, while a manager agent oversees the whole thing. At the end, you get a pile of completed work to review in batch and test in a staging environment.

He expects this to become the standard way of building software within a year or two, with planning done up front and agents running for a day or several days. That is his prediction, not an established fact. Treat it as one practitioner's bet.

The key point is customization. The creator argued that a built-in command like Codex's `/goal` is not enough by itself. It doesn't know how you work or how you verify results, so you end up babysitting at the end when the agent gets something wrong. Encoding your own validation habits into the harness is what lets you trust longer runs.

## Do you need to build your own?

Not necessarily. The source says building one isn't for everyone. If you like building and want results sooner, constructing your own gets you there faster and teaches you how these systems behave. If not, the expectation is that ready-made options will arrive that are easy to tailor to your work.

A practical way to start without building a full factory:

- Pick one recurring, well-understood task type.
- Write down how you personally check that it is done correctly.
- Put those checks in the agent's rules or skills so it validates before it reports back.
- Ask the agent to interview you about intent before it starts.
- Review finished work at set times instead of responding to every notification.

## Is the trade worth it?

The cost is real: for a while you ship less per hour because you are investing in the harness instead of launching more sessions. The argument for it is that the investment compounds. Each task the agent can finish without you removes a future interruption.

Whether it fully restores the old deep-focus days is unproven. The source frames this as a way to get past an awkward transition, not a guaranteed cure.

## Frequently Asked Questions

### Why do I feel more tired with AI even though I do less manual work?

The work shifted from sustained production to constant supervision. You switch between sessions, read output, and give input, and each switch carries a cognitive cost. That fragmentation can be more tiring than a long stretch on one problem.

### Should I just run fewer agents in parallel?

That helps, and nothing in the source argues against it. The deeper issue is that agents need frequent input. Reducing that need through better rules, validation, and planning addresses the cause rather than the symptom.

### Is `/goal` in Codex enough to solve this?

According to the source, no. Generic autonomy features don't know your specific way of validating work. You still end up supervising and fixing problems at the end unless you encode your own process into the harness.

### What does batching reviews actually mean?

It means agents work through a larger body of tasks on their own and surface the results together. You then review and test them in one sitting, such as in a staging environment, instead of reacting to each agent as it finishes.

### Will this problem go away as models improve?

The expectation is that it will ease as agents become reliable enough to run a full day on most tasks. We are not there yet, so improving your harness now is the practical way to reduce the babysitting in the meantime.