AI agent workflow that doesn't fry your brain

An AI agent workflow is the path a task takes once you stop doing it in a chat: you name the outcome, specialised agents do a bounded step, and the result waits for you to review. The bottleneck is attention, not agent count. Starting another agent does not give you another pair of eyes on the work. It gives you another conversation to remember.

The fried brain effect

On 14 October 2026, Tibo wrote that most days he has "up to 10 agents running at the same time. one fixing a bug, one writing pages, one refactoring something I already forgot I asked for", and three others he had no idea what they were doing. "I jump from conversation to conversation all day." His words: "by the end of the day my brain is fried. completely toasted." He calls it the fried brain effect. The post opens with "AI was supposed to make us work less" and "I've never been this tired in my life." He also wrote, "compared to 5 years ago, I probably context switch 100x more", and "if you can ship 10x more, you feel like you have to ship 10x more." Public counts on that post, read on 15 October 2026, were 434,074 impressions and 7,987 likes.

On 15 October 2026, Patrick McKenzie wrote about "Getting my first real exposure to multi-agent development workflows". "This is clearly the future." Then: "Barely feels like software engineering but I end the day intellectually spent." He also wrote, "Oh we're going to buy so much inference."

X's news summary of the discussion is AI Agents Leave Founders with Fried Brains from Constant Juggling.

OpenAI's page on how agents are transforming work (June 25, 2026) says: "Agentic AI changes the unit of knowledge work from single interactions to delegated, long-horizon tasks." Among daily active users at OpenAI, the page reports that by June 2026 "users at the 99th percentile regularly generated more than 60 hours of Codex agent turns per day, distributed across multiple, parallel agents." Parallel agents are already how the heaviest users inside OpenAI work. The fried-brain posts are what that feels like when you are still the one jumping between conversations.

What belongs in an AI agent workflow

A multi-agent workflow fails when each agent is a tab. Tibo's day is that failure: the agents are running, and he is the one context-switching. McKenzie called the same kind of work the future, and he still ended the day intellectually spent.

Running fewer agents forever is not the fix. It only shrinks the pile. The limit that holds is how many interrupts you can keep in your head. An AI agent workflow moves that state onto a board, so you review a card instead of reconstructing a chat.

Three pieces. Drop one and you are back in the tabs.

• A shared task board. The work is a card with a status, not a thread you have to find.
• Named roles. Research, a draft, and a check are different jobs. One general chat makes you re-explain the brief between them.
• Review. You accept the work, or you send it back. You are not the interrupt handler who has to notice that an agent finished.

That hand-off is how to use AI agents. An AI assistant for work still ends in a message you copy out. A personal AI agent can take the task. The workflow is several of those agents on one list, with you reading the result once.

Managing AI agents as your own squad

xSquads for Work gives each knowledge worker their own squad of specialised agents to delegate to and review, so they become 10x more productive. The squad works on your own files and folders. Cards move on a shared task board. You delegate. You review. You do not keep ten conversations alive to know what is in progress.

The app is Mac now, with Windows coming soon. It is free via the waitlist. The roles, by job, are on squads.

When the work is a production line for code

A knowledge-worker squad and a software line are different workflows.

A managed software factory is a production line for code, operated for you. On this site that line is the code factory. Agents open pull requests. A person reviews before anything merges. Nothing merges without approval. You add or remove agents as the workload changes. The same description is on what a managed AI software factory does.

You still read the change. You are not sitting in a coding chat for each agent while it types.

Keep the agents. Stop being the router.

Tibo wrote, "I don't have a solution." The gap in that post is a place for the work to wait that is not his attention. That place is the AI agent workflow.

• If a task only exists inside a chat, it is not on the board yet.
• Give the card one role. "Someone look at this" is how three agents do the same job and you reconcile them at the end of the day.
• Read Review on a schedule you choose. The card waits. You do not have to answer the moment an agent finishes.

Frequently Asked Questions

What is an AI agent workflow?

An AI agent workflow is the path a task takes after you leave the chat: you name the outcome, specialised agents do a bounded step, and the result waits for review. The bottleneck is your attention, not how many agents you can start.

What is a multi-agent workflow?

A multi-agent workflow is several specialised agents on one piece of work, each with a named role, sharing one task list. You review what they bring back. You do not jump between their chats to remember what you asked each one to do.

Why does managing AI agents leave people exhausted?

The person becomes the router. On 14 October 2026 Tibo described up to 10 agents at once and jumping from conversation to conversation until his brain was fried. On 15 October 2026 Patrick McKenzie, writing about multi-agent development workflows, said this is clearly the future, and that he ends the day intellectually spent.

Does a better AI agent workflow mean fewer agents?

No. Fewer agents only shrinks the pile. The fix is a shared task board, named roles, and Review, so you are not the interrupt handler for ten chats. You can still run several agents. You stop holding their state in your head.

How does xSquads for Work fit managing AI agents?

Each knowledge worker gets a squad of specialised agents to delegate to and review, on a shared task board, working on their own files. Finished work waits in Review. The app is Mac now, with Windows coming soon, and it is free via the waitlist.

How is a managed software factory different from a personal squad?

A personal squad is one knowledge worker's files and tasks. A managed software factory is a production line for code, operated for you: agents open pull requests, a person reviews before anything merges, and you add or remove agents as the workload changes. Nothing merges without approval.

Related Reading

• How to use AI agents: the hand-off, then the review.
• AI assistant for work: the chat that waits, and the coworker that returns a file.
• Personal AI agent: from one assistant to teammates you review.
• Managed software factory and the code factory.
• Squads and xSquads for Work.

Raina Rufus, Founder of xSquads

xSquads for Work is free. Join the waitlist and we'll send the download link.