You’re in a PM interview at an AI-first company (Open AI / Anthropic / Google)

Everything is going well.

Then the interviewer asks:

↳ “Design an AI system that can autonomously learn and execute new tasks.”

Suddenly the room feels quieter.

Because this isn’t a typical system design question.

and, It’s definitely not:

→ A feature design problem

→ A standard ML pipeline

→ Or another “add ChatGPT to an app” solution

Yet many candidates approach it exactly that way.

And that’s where they struggle.

Most people try to design "traditional system architecture".

But what the interviewer actually wants is autonomous Agentic AI architecture.

...

𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐝𝐞𝐬𝐢𝐠𝐧 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦?

You’re no longer building:

→ A product feature

→ A prediction model

→ A simple workflow

You’re designing a self-directed decision loop.

A strong architecture should include:

1️⃣ 𝐆𝐨𝐚𝐥 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧

How the system interprets intent and defines objectives

2️⃣ 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 & 𝐭𝐚𝐬𝐤 𝐝𝐞𝐜𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧

Breaking complex goals into executable steps

3️⃣ 𝐌𝐞𝐦𝐨𝐫𝐲 & 𝐜𝐨𝐧𝐭𝐞𝐱𝐭

Maintaining state across tasks and interactions

4️⃣ 𝐓𝐨𝐨𝐥 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧

Choosing APIs, tools, or actions to complete tasks

5️⃣ 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬

Evaluating outcomes and correcting failures

6️⃣ 𝐒𝐚𝐟𝐞𝐭𝐲 & 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬

Preventing harmful or unintended behavior

In short.

You’re designing a control system, not just an application.

...

These questions are becoming increasingly common as companies build Agentic AI products.

PMs who understand Agentic AI will have a strong edge in the next generation of AI products.

...

I spent the last few weeks breaking down how top AI companies expect PMs to answer this question.

I wrote a full walkthrough here: