agentorg.design

Write the intent.
The agent optimizes overnight.

You describe your business in a few paragraphs — what you sell, what it costs, what you can’t control.

An AI agent runs hundreds of experiments against your data.

By morning, you know how much you’re leaving on the table.

$98,569/yr found
90.7% of theoretical max
639 experiments · one night

Case Study — Inventory Optimization

$98,569

additional profit per year

639 Experiments,
One Flower Shop

A Boston flower shop. Five perishable products. Daily ordering. The sales data doesn’t record what people wanted — it records the minimum of what they wanted and what you had. Every stock-out makes your history lie.

The agent discovered that orchids need longer memory than roses, that the Monday after Mother’s Day is worth 3.4× normal orders, and that safety stock — the textbook answer — makes things worse.

Read the full case study →
639 experiments: profit climbing from $392K to $491K through iterative policy improvements
Start: $392,534 (72.5%)End: $491,103 (90.7%)

Case Study — Multi-Agent Organization Design

“Same model.
Same input.
Same API calls.

Only variable:
org design.”

12 API calls · $0.08

Can Org Design Make AI Smarter?

Optimization isn’t always a number.

Six identical AI agents analyze a startup pitch. Then five specialists — each forbidden from covering the others’ territory — analyze the same pitch. A synthesizer collides their perspectives into a final memo.

The flat team finds the same risks in the same order. The designed firm surfaces visceral user rejection, competitive kill shots, and a pivot the flat team never considered.

Structure isn’t overhead. It’s the optimization itself.

Read the full case study →

How Every Engagement Works

The Architecture of Intent

01

You Write the Brief

Describe your business: what you sell, what it costs, how demand moves, what you can’t control. Not a spec. A few paragraphs. The kind of thing you’d explain over coffee. This becomes the agent’s compass.

02

We Build the Judge

A scoring function that measures how close the agent is to the theoretical best. It doesn’t need to be perfect — it needs to be honest. If the agent can measure the gap, it can close the gap.

03

The Agent Optimizes Overnight

It proposes changes, measures results, keeps what works, discards what doesn’t. Hundreds of experiments. Most fail. The ones that survive compound. By morning, you have a policy tuned to your business — not a generic model, not a dashboard, an actual answer.

The human draws the map.
The agent walks every path on it.

Every business that orders inventory, schedules staff, sets prices, or allocates budget faces the same structure: a decision made under uncertainty, a cost for being wrong in either direction, and historical data biased by your own past decisions.

The hard part was never the AI.

The hard part is knowing your own business well enough to write it down.

Let’s talk about your problem

hasan@agentorg.design