There’s a pattern I keep seeing in the AI space right now, and it’s driving me a little crazy. Everyone’s racing to bolt more tools onto GPT — Salesforce integrations, database queries, third-party APIs — as if the problem is that the AI doesn’t have enough capabilities. But that’s not the problem. The problem is that a chat AI has no idea WHEN to use those tools, or WHY.
I’ve been working on something that I think gets closer to the real architecture we need. And it starts with a concept I’m calling the Intermediate Observer Layer.
The Problem With Giving AI More Tools
Here’s the core issue. ChatGPT — or any conversational AI — is designed to do one thing: respond to what you say. It’s reactive. You ask, it answers. That’s the loop.
But when you want AI to DO things in the real world — update a CRM, pull data from an external system, or even just summon a human into the conversation when it’s out of its depth — you need something that sits OUTSIDE that conversational loop and watches it. Something that can observe the conversation between a human and GPT and infer when it should take action, not because it was explicitly asked to, but because the context demands it.
That’s the Intermediate Observer Layer. It monitors the conversation and makes decisions about when to invoke tools, when to escalate, when to step back. The chat AI itself can’t do this. It’s too deep in the weeds of generating the next response.
Sam Altman actually touched on this recently — he said everyone’s trying to make a plugin when what they really need is the API. And he’s right, but I think it goes even further than that. The application should use GPT, not the other way around. You prompt GPT on how to format replies in a way your application layer can parse, and then your application decides what to do with that structured output. Error codes, success codes, next-step prompts — all flowing through a layer that GPT doesn’t even know exists.
From Observer to Orchestrator
But the IOL is just the starting point. Once you’ve got an observer that can detect intent and trigger actions, you need to give it a framework for HOW to act. And this is where the architecture gets pretty interesting.
Think about it in layers:
Planner — takes a high-level goal and breaks it into discrete steps. Not generating text, but generating a workflow.
Worker — executes individual steps. This is where your tool integrations actually live. Salesforce API calls, database writes, web searches — each one is a worker capability.
Solver — handles exceptions. When a worker fails or returns unexpected results, the solver figures out an alternative path.
And the IOL sits above all of this, reading the human conversation and deciding when to kick the whole machinery into gear.
If you can accomplish that — IOL plus Planner, Worker, Solver, with third-party tools linked into the process — you essentially get a more efficient AutoGPT that can accomplish real-world digital tasks for humans. I’ve written before about giving AutoGPT actual jobs and watching it struggle with autonomy. This architecture is the answer to WHY it struggled. AutoGPT tried to be all of these layers at once, inside a single conversational loop. It was the planner AND the worker AND the solver AND the observer, all mashed together. No wonder it kept asking for my credit card and going off the rails.
The Memory Problem Is Real — And Connected
There’s a second piece to this puzzle that I’ve been wrestling with. One of the reasons personalized AI engines struggle right now — even on GPT-4 — is that they can’t work effectively with long-term memory stored in external databases. It’s slow. And you need nearly perfect prompts for GPT-4 to pull the correct data with each request.
I was talking about this months ago when I talked about vector databases and Pinecone — the idea that infinite memory is the path to AI that actually knows you. The theory was sound. The practice has been.. rough. The retrieval step is where everything breaks down. GPT-4 doesn’t inherently know how to query its own memory store efficiently, and the prompts required to make it work reliably are brittle.
The solution, I’m increasingly convinced, ties back to the same architectural insight. You don’t ask GPT to manage its own memory. You build a retrieval layer — another component in the orchestration stack — that handles memory lookup based on context, and feeds relevant information INTO the prompt before GPT ever sees it. The AI doesn’t search. The system searches FOR it.
Why This Matters Right Now
We’re at an inflection point. The plugin ecosystem is exploding. Every SaaS company wants to be the next ChatGPT integration. But most of them are building the wrong thing. They’re building direct tool connections when what the ecosystem actually needs is better orchestration.
The companies that figure out the observer-planner-worker-solver pattern — the ones that build robust intermediate layers — are going to eat the ones that just shipped a plugin and called it a day.
I’ve been testing our own personalized AI alpha, and the difference between “GPT with tools” and “GPT with an orchestration layer that manages tools” is night and day. One feels like a chatbot that occasionally does tricks. The other feels like it’s actually working FOR you.
The takeaway is pretty simple: stop giving AI more tools. Start giving it a manager.
