Blevene

(Part 1 can be found here: https://gist.github.com/Blevene/0d5f74c70873ce2dbe356208f75c68fa)

A Practical Framework for Applying Agentic Archetypes

There's a pattern in technology that repeats itself. First, we build powerful, all-in-one monoliths. Then, as we try to scale them for the real world, we discover their limitations and break them apart into specialized, collaborating microservices.

Agents are to artificial intelligence as containers and microservices were to the cloud.

We've treated Large Language Models like monolithic "brains-in-a-box," trying to solve every problem with a single, complex prompt. This approach is brittle, expensive, and nearly impossible to debug in production. To build robust, scalable systems, we need to think less like prompters and more like architects. We need a new mental model.

Blevene

Orchestrating Intelligence: A PM’s guide to Agentic Archetypes

Introduction

As a product manager, I find the most valuable takeaways are often new mental models for thinking about a problem. For a while, the default model for AI has been the single, conversational agent. But as we task these systems with more complex, multi-step work, that model can feel limiting.

This shift toward building systems that can "reason" has been a surprising flashback for me. Before I joined the security industry, my education was in psychology, with a focus on cognition—the science of “thinking about thinking.” So when I started seeing the need for more structured AI reasoning, I found myself diving deep into what I call “agentic archetypes.” It’s a way to logically segment how these models "think" and act, allowing us to build more robust and predictable solutions while being mindful of our natural tendency to anthropomorphize everything.

Quick Level Set

Blevene

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Blevene

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Blevene

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Blevene

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Blevene

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