The Problem with One Big Agent
Why “just use ChatGPT” isn’t a strategy — and how AOL changes the game
Hiya all — Mohit here.
One of my earlier posts introduced a new concept called AOL (Agentic Orchestration Layer) — you can read it here: Agentic Orchestration Layer – Rethinking How We Run AI Agents
But I realised something. There are still several assumptions, myths, and half-baked strategies circulating that muddy the waters, and in doing so, obscure the need for something like AOL. So I’m starting a series to break these down. One post at a time. One myth at a time.
Let’s begin with the first: the overhyped idea of the One Big Agent.
If you’ve been following the AI wave lately, you’ve probably noticed a recurring pattern: everyone wants one big agent to do everything. Whether it’s customer service, internal support, or marketing ops, the default mindset seems to be:
“Why not just plug all our tools into GPT-4 and let it figure it out?”
“Can’t we just craft a really ‘smart’ prompt and pour everything into it?”
“Why not just ask it to ‘do this’ — and let it work out the rest?”
Tempting, sure. But deeply flawed.
The Illusion of the Super-Agent
This “super-agent” myth is built on three shaky assumptions
That a single LLM can reason across infinite contexts equally well.
Integrating it with more tools makes it more capable.
That chaining tasks = orchestrating intelligence.
In practice, you end up with
Agents are becoming overwhelmed by the number of tools available.
Lost memory between tasks.
Confusing outputs and expensive errors.
Zero observability or accountability in decisions.
It’s like asking one intern to manage your IT, accounting, marketing, HR, and lunch orders — with no clear boundaries or support structure.
Why Does This Keep Happening?
Because the architecture is missing. Most teams are chasing behaviour without thinking about structure. They want the agent to act smart without setting up the system for it to succeed.
What’s needed isn’t a smarter prompt — it’s a smarter system.
Enter AOL — The Agentic Orchestration Layer
This is where the Agentic Orchestration Layer (AOL) comes in. Instead of relying on one overburdened agent, AOL gives you
A coordination layer that routes intents, not just inputs.
A context assembler that brings in only what’s needed — just-in-time.
A modular system where each agent or tool has a role and scope.
Observability — so you know what’s happening, and why.
Think of it like an operating system for agents — not just a chatbot, but a structured, intelligent environment that enables them to work together.
The Shift That Matters
We’re not moving toward bigger models, we’re moving toward better orchestration.
In this series, I’ll break down each AOL component: intent routing, memory architecture, tool abstraction, and governance. Not because it's cool tech, but because this is how AI becomes usable, scalable, and safe in the real world.
Because the truth is: the era of “one big agent” is already behind us. The future is agentic — but only if we learn to orchestrate.
More soon — and as always, happy to hear your thoughts.