When One Agent Isn't Enough: The Rise of AI Orchestration

Last year, I asked an AI to plan an entire product launch — and it failed spectacularly.

Not because the model was bad, but because I handed it a single massive task with no structure.

That was when I realized something: the bottleneck was never the model.

It was the system around it.

AI tools have quietly evolved from simple question-answering machines into systems that can break down complex problems, delegate subtasks, and coordinate results.

But most people are still using them the old way — one prompt, one answer, done.

Think of it this way: the model is like a brilliant new hire on your team.

How well they perform depends not just on their skills, but on the environment you build around them.

That environment — the rules, memory, tools, and automated triggers — is called a harness.

A well-designed harness tells the model what to prioritize, what to avoid, and how to hand off work.

Without it, even the most powerful model will wander, repeat itself, or miss the point entirely.

Engineers who get consistently great results from AI aren't just writing better prompts.

They're building better harnesses.

Once your harness is solid, you can start thinking about orchestration — coordinating multiple agents to tackle work in parallel.

Imagine a team of three researchers working on a product report.

In a serial setup, the first finishes before the second begins, and the project crawls along.

In a parallel setup, all three dig in simultaneously, cutting the total time by two-thirds.

But real workflows rarely fit neatly into either pattern.

That's where a DAG — a directed acyclic graph — comes in.

It maps out which tasks depend on each other and which can run at the same time.

Two or three workers is usually the sweet spot.

Beyond that, the overhead of collecting and combining their outputs starts to cancel out the speed gains.

There's even a name for what happens when workers send back too much information: context starvation.

The orchestrator gets so buried in summaries that it loses track of the original goal.

Not every task needs this level of structure.

Vibe coding — quick, intuition-driven experimentation — is perfectly fine when you're exploring a new idea alone.

But the moment your changes touch multiple systems, involve other people, or have real consequences, structure pays for itself.

The hardest lesson I've learned is this: without an eval framework, you have no idea if you're improving.

You might switch models, rewrite your harness, add more workers — and still be going sideways.

Measurement isn't glamorous, but it's the only thing that separates engineering from guessing.

The future of AI isn't about finding the smartest model.

It's about building the smartest system around it.