A film producer I work with has a process for developing ideas. He runs every concept through four AI models.
ChatGPT first, for initial ideation. Then Claude, for a technical perspective. Then Gemini, for creative angles. Then Grok, for unfiltered feedback. Every model gives him different insights that the others miss.
When he first described this to me, I asked why he didn't just use one. His answer: “They each see things differently. I get four distinct viewpoints.”
He'd independently arrived at the same principle I keep coming back to when building AI systems: one generalist beats five specialists at nothing.
The One-Agent Trap
The natural instinct when building your first AI agent is to make it capable of everything. Handle my email, do research when I need it, draft content, answer client questions, help with scheduling. One powerful assistant.
This is the same impulse that produces chaotic job descriptions for humans. “Must be a strategic thinker, detail-oriented executor, creative problem-solver, and strong communicator” — which usually means the job is four roles bundled into one.
AI agents have the same problem. When you give one agent multiple jobs, you force it to context-switch constantly. The instructions get long and complicated. The agent has to decide at each moment which “mode” it's in. Errors compound.
And when something goes wrong, you don't know which job caused the failure.
The Librarian Analogy
Here's the mental model I use instead.
Think about a library. You have books, and you have librarians. Now imagine you only hired one librarian, and they're responsible for every section — fiction, science, law, history, reference, periodicals, all of it.
That librarian will give you okay answers on everything. But you'll never get the depth you'd get from a specialist.
Now imagine you have five librarians, each assigned to their section. The fiction librarian has read everything in their stacks. They know which books cross-reference each other. They know which authors are relevant to your question. They read their section differently because it's all they do.
Same books. Different librarians. Way better answers.
Your AI agents are the librarians. The books are your data, your workflows, your content — whatever they're working with.
When I was working with Blake Eastman on his content system, he'd built one agent to do everything. It read all his content and was supposed to generate Instagram posts, find interesting facts, create carousels, and write threads. It did all of it at a mediocre level.
I stopped him and rebuilt it as three separate agents: an idea finder (reads the content, surfaces the most interesting angles), a hook generator (turns those angles into strong opening lines), and a carousel formatter (takes approved hooks and formats them for the specific platform). Same content library. Three specialized librarians.
More files to manage. More agent configurations to maintain. But each one reliably does its job.
The Same Principle Applies to Models
This isn't just about building agents — it's about how you use AI models day to day.
Most people pick one model and use it for everything. But each model has genuine strengths. ChatGPT is strong for general work, brainstorming, and daily tasks. Claude is better for technical work and coding. Gemini handles visual analysis and works better on large documents. Grok is faster for real-time research.
I've started selecting models per task the way you'd select the right tool from a toolbox. When I build multi-step agents, I even pick different models per step: a cheaper, faster model for research steps, a stronger model for final synthesis.
The best practitioners don't ask “which AI should I use?” They ask “which AI is right for this specific job?”
What an Agent That Knows Its Job Looks Like
There's a test I use before building any agent: can you write its job in one sentence?
Not a paragraph. One sentence.
“This agent reads incoming emails and categorizes each one as archive, draft, or snooze.” Done. That's a tight, scoped agent.
“This agent handles my entire workflow and helps me with whatever I need” — that's not an agent. That's a hope.
The cleaner the job definition, the better the agent performs. When an agent can't explain its own role and boundaries clearly, it's not ready. You'll spend more time fixing its mistakes than you save from having it.
Building Your First Specialized Stack
The place to start is your current catch-all agent, if you have one.
What does it actually do most of the time? Find the one or two tasks it handles most often. Pull those out into their own agent. Give each one a tight prompt, a clear job, and only the data it needs to do that job.
You'll end up with more pieces. But each piece will work better.
The library gets better as you add more specialist librarians. The books don't need to change — just who's reading them.
The 4-Day AI Sprint covers how to build a multi-agent setup from scratch — including how to scope agents, connect them, and build the kind of stack where each piece does one job reliably.
