Innovation Lab / The Research Greenhouse

Questions I became curious enough to build something around.

Some questions are easier to understand once I can touch them.

The Research Greenhouse is where I test ideas in motion — building with AI agents, designing decision systems, challenging assumptions, and paying attention to the places where technology still needs judgment.

All of them teach me something.

We experiment. We learn. We evolve.Human-led. AI-enabled. Responsibly built.

On the bench

Three questions in motion.

Real experiments, abstracted only where privacy or responsibility requires it.

RG-01 / Agentic product buildingActive / On the bench

Project Relay

An AI-assisted product-building workflow that turns a structured idea into implementation, evaluates the result, and revises its own work before human review.

How far can an AI agent help move an idea from concept toward a functioning product — and where does human direction still matter?

RG-02 / Agentic decision systemsActive / Partially restricted

Project Signal

An AI-assisted decision agent that continuously evaluates a fast-moving environment, verifies candidates against fixed constraints, and escalates only the strongest possibilities for human review.

What happens when an AI agent has to make decisions inside a fast-moving, constraint-heavy environment?

RG-03 / Decision designLive

Density + Clarity

An interactive decision-design prototype that changes the amount of information presented at once to explore density, clarity, and modeled decision effort.

When does more information stop making a decision clearer?

Open live experiment

Featured live experiment

Density + Clarity

A design question made manipulable — not an empirical research claim.

When does more information stop helping?

Move the slider to see how modeled clarity changes as more information is added.

RG-03 / Live specimenDeterministic model
4 / 10Strongest clarity

Enough context to support the decision without unnecessary overload.

Modeled decision effort34s

Strongest modeled zone: 3–5. The range where the model balances useful context with lower overload.

About the model. A deliberately constructed design experiment, not participant research.

Lab notes

What the experiments left behind.

Project Relay

“More capability does not make direction less important. It makes ambiguity more expensive.”

Project Signal

“A useful agent needs permission to return nothing.”

Project Signal

“Not every source that can answer a question should be allowed to authorize a decision.”

Public / private principle

Some real experiments can be discussed at the level of the question and learning while the underlying project details remain private.

Archive — Nothing has been put away yet.

What I’m testing next

The next question is not simply whether the agent can make a recommendation. It is whether the system can clearly explain why the decision boundary was or was not crossed.

I want to keep exploring ways to preserve product intent across long-running agentic work without turning every new interaction into another giant prompt.

Start a project

Bring me a question worth testing.

If your work needs clearer direction, a tangible experiment, or thoughtful human judgment around emerging technology, we can start there.

Start a project