The Dispatcher Model: How to Build AI Workflows That Don't Break

Most people build AI workflows the same way they’d assemble IKEA furniture without instructions — by feel, trial and error, and a lot of frustrated backtracking.

You build a prompt. It works, kind of. You reuse it. You find yourself doing the same cleanup over and over. So you build something more permanent — a custom GPT, a Project, a Gem. Now you have new problems: it’s rigid where you needed flexibility, and updating it is a chore that never quite gets done.

This is the AI Karmic Trap. The solution you built created new problems to solve.

The Dispatcher Model is an architectural approach to AI workflows that breaks this cycle — by treating AI less like a chat box and more like a functioning team.

Why Most AI Workflows Eventually Break

The failure pattern is predictable. You end up with one of two bad options:

Option A: One giant agent that understands everything. It inevitably forgets key details, hallucinates, or starts making things up when the context gets complex. You can’t trust it consistently.

Option B: A bunch of small, granular agents. Now you’re the glue — copy-pasting output from Agent A into Agent B like a human API. You’ve automated the task but not the workflow. You’re still doing the work, just differently.

Neither scales. Neither is reliable. And both leave you exhausted in ways that are hard to explain to people who haven’t felt it.

The root problem isn’t the tools. It’s the architecture.

The Four Components of the Dispatcher Model

Think of this like a professional kitchen. Every order that comes in gets routed correctly, executed with the right ingredients, checked against a quality standard, and sent out consistently — without the head chef personally managing every plate.

1. The Dispatcher (The Head of the Kitchen)

The Dispatcher is a lightweight entry-point document — not a prompt, an index. It tells the AI what kind of task is coming in and where to go for the right instructions.

Instead of dumping your entire context into every prompt, the Dispatcher lets the AI “choose its own adventure.” It reads the index, identifies the task type, and pulls only the context it actually needs. No bloated prompts. No token waste. No lost focus.

This is the file that says: “If you need X, see Y.”

2. Primitives (Your Ingredients)

Primitives are your raw materials — the things that don’t change much and get used across many different tasks:

  • Your brand voice guide
  • Client background documents
  • Project context files
  • Raw transcripts or source material

Stop pasting these into every prompt. Store them separately. Update them once when they change, and they automatically reflect across everything that uses them. The recipe for roasting chicken belongs at the prep station — not rewritten into every dish it appears in.

3. Skills (Your Recipes)

Skills are where most people start and stop. A Skill teaches the AI how to do a specific type of task in the way you like it done:

“Interview me and create a brief.”
“Turn this transcript into a blog post.”
“Diagnose this workflow bottleneck.”

Skills are powerful, but incomplete on their own. A recipe without measured ingredients and a clear picture of the finished dish gives you something approximately right — which is how you get 70% quality, 70% of the time. Sometimes.

4. Contracts (Your Quality Standard)

This is the component most people are missing — and it’s responsible for closing most of the remaining quality gap.

A Skill says: “Here’s generally how I like things done.”
A Contract says: “Here’s exactly what done looks like — and here’s what failure looks like.”

Specifically, a Contract tells the AI:

  • I will give you: inputs X and Y
  • You must produce: outputs A and B
  • You must NOT include: C, D, or E
  • Checkpoint: At milestone F, stop and get my approval before continuing

This forces the AI to check its own work. If an input is missing, the Contract makes it ask rather than guess. If the output doesn’t match the spec, it catches the drift before it compounds. Most importantly, the AI now has a clear way to know whether it did a good job — before showing you the result.

How It All Flows Together

When you set up a Project in Claude or ChatGPT with this architecture in place:

  1. A request comes in
  2. The Dispatcher identifies the task type and routes accordingly
  3. It pulls the right Skill for the job
  4. It grabs the relevant Primitives — only what’s needed
  5. It checks the result against the Contract before handing it to you

You stop being the glue. The system routes its own traffic.

Why This Matters Beyond Productivity

The Dispatcher Model isn’t just about saving time. It’s about building AI workflows that other people can use — workflows that don’t depend on you being the expert in the room.

For teams, this is the difference between “we have AI tools” and “we have AI systems.” Tools require expertise to operate. Systems encode expertise so anyone can get consistent output.

For solo operators, it’s the difference between AI that works when you’re at your best and AI that works reliably — even when you’re tired, distracted, or handing something off.

Reliability doesn’t come from better prompts. It comes from better structure.

Getting Started Without Going Insane

Don’t try to build all four components from scratch. That would be the Karmic Trap reasserting itself.

The smartest entry point is a single “skill-making skill” — a prompt that helps the AI create its own Skills and Contracts based on what you tell it about your work. Get that piece right, and you can derive the rest of the system through conversation.

Start with one workflow that’s costing you the most repeated cleanup. Build a Skill and a Contract for it. See what changes. Then expand.

FAQ

Is this only useful for technical users?
No. The architecture is conceptual — the actual implementation can be as simple as a few text documents stored in a Claude Project or ChatGPT Project. No coding required.

How is this different from just writing better prompts?
Better prompts improve individual outputs. The Dispatcher Model improves the system — so outputs are consistently good across different tasks, users, and contexts.

Does this work with any AI tool?
The principles apply anywhere. The most practical implementation today is with Claude Projects or ChatGPT Projects, which support file storage and persistent instructions.

How long does it take to set up?
A basic version — one Dispatcher document, two or three Primitives, and one Contract — can be operational in a few hours. The system gets more powerful as you add to it over time.

Mitch Schwartz is the founder of Ops Machine, a Montreal-based AI integration and workflow consultancy. He works with nonprofits and organizations mid-transformation to find where AI fits, build the right systems, and make sure teams actually use them. Book a free discovery call →