From AI to Agents to Agencies

Summary

Two years ago, I wrote about the transformative potential of AI Agents - autonomous systems that could break down complex tasks and execute them without constant human intervention. The core thesis was that while ChatGPT was reactive, Agents would be proactive, capable of independent problem-solving and task execution.Much of that vision has materialized. We now have Agents that can autonomously code websites, manage digital workflows, and execute multi-step processes. The economic dynamics I predicted are playing out: Agents are indeed starting to handle entry-level digital work, from copywriting to simple coding tasks.But as I've observed the ecosystem evolve, particularly through my work at NonBioS, a new architectural pattern seems to be emerging that represents a fundamental leap beyond what we call "Agents" today.What I'm witnessing is the birth of what I believe should be called Agencies - systems that tackle individual tasks by dynamically orchestrating different types of intelligence, each optimized for specific subtasks, all working toward completing a single overarching objective.An Agency is fundamentally different from an Agent. While an Agent is a single intelligence (an LLM) enhanced with tool-calling capabilities working on a task, an Agency is a coordination system that can access and deploy multiple specialized intelligences (LLMs) to complete different parts of the same task.Think of it this way: if an Agent is like a skilled generalist who uses various tools to complete a task, an Agency is like a master craftsperson who can tap into different types of expertise and capability as needed - strategic thinking for planning, procedural intelligence for routine steps, specialized knowledge for technical implementation - all flowing seamlessly within the execution of a single task.Traditional AI Agents, as we understood them in 2023, were essentially sophisticated wrappers around a single Large Language Model with tool-calling capabilities. When given ...