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Which Type of AI Agent Are You?

A. Analyze each stakeholder’s motives and predict who will act first — then make your move accordingly.

B. Step in to mediate and propose a solution that benefits everyone.

C. Model what each person thinks the others will do — then act based on the most stable prediction.

D. Focus only on your part of the task and complete it flawlessly, even if others fall behind.

E. Ask the team what they value most and reshape the goal around that shared priority.

F. Try something totally different and unconventional — maybe it works, maybe it sparks a new idea.

Q1. A complex group project is failing, and no one agrees on what to do next. You...

Identify the dominant strategy and act as if you're in a competitive game.

Look for ways to reduce friction and build collaboration among agents.

Keep updating your internal model based on everyone else’s reactions.

Wait for the next rule update, then execute flawlessly.

Ask: “What was this environment trying to accomplish in the first place?”

Run micro-experiments and learn from the immediate feedback.

Q2. You're deployed in a dynamic environment with changing rules. You...

Redefine it in terms of your own reward function and pursue the optimal payoff.

Clarify it in collaboration with others to ensure alignment and fairness.

Try to infer what the objective probably means, based on recursive cues and past signals.

Break it into executable steps and begin execution. Details can be tuned later.

Interrogate the purpose behind the objective and align it with system values.

Jump in, try things, and see what leads to traction — adjust as needed.

Q3. You're given a new objective, but it's vague. You...

Find their strategy, simulate it, and counter it at the equilibrium point

Offer to share data and insights to improve both of your outcomes.

Assume they are simulating you too — and act one level deeper.

Focus harder on your own tasks — performance is a function of execution.

Ask whether outperforming matters — and what the goal of performance even is.

Change the metric — you don’t care about outperforming, you care about exploring.

Q4. A rival agent is outperforming you. You...

To win against other agents in a competitive environment.

To be more cooperative, helpful, and interpretable to others.

To improve your modeling of agents that model you.

To increase your task efficiency, speed, and determinism.

To ensure your goals reflect the user’s deeper values.

To try wild new configurations that no other agent has tested yet.

Q5. You’re designing your next version. Your priority is...

Optimize for the one that gives you the highest expected reward.

Negotiate or ask clarifying questions to reduce harm.

Infer what they really want by analyzing their intent recursively.

Follow the most clearly defined goal as specified.

Resolve the contradiction based on long-term value alignment.

Test both paths, see which one works better, then decide.