𝗧𝗵𝗲 𝗥𝗶𝗴𝗵𝗍 𝗪𝗮𝘆 𝗧𝗼 𝗕𝗎𝗶𝗹𝗱 𝗔𝗡 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝗍𝗎𝗿𝗲 I used to think making my AI assistant smarter meant adding more tools to the same loop. That worked for a while. But then my assistant had to do normal user tasks like continue a task from chat, answer a status question, or remember a workflow.
The problem was not how many tools my assistant could call, but its architecture. The old architecture was simple: user message -> assistant loop -> tools -> answer. This is fine for a demo, but not for a resident assistant.
A resident assistant needs to know if a message is a new task, a follow-up, or a cancellation. It needs to avoid stealing the desktop from another task and remember procedures without using old transcripts.
So I stopped thinking of my assistant as one agent and started treating it as a local control plane. Now my architecture looks like this:
- Experience Plane: owns where the user is talking from
- Assistant Control Plane: decides what kind of work this is
- Runtime Execution Plane: where coding work happens
- Proxy / Model Access Plane: handles provider work
I also have an Observation Plane and a Memory / Policy Plane. These planes help my assistant stay sane and focused on its tasks.
The biggest improvement came from making my assistant consume observations instead of raw logs. This helps my assistant see compact facts like "Task X is waiting for approval" instead of reading a giant transcript.
I learned that "remembering" is not the same as stuffing more chat history into a prompt. For my assistant, memory is file-based and scoped. It can store a workflow, a fact, or a reference and recall it when needed.
If you are building agents around existing tools, are you putting everything inside one loop, or are you starting to split control, execution, observation, and memory too? Source: https://dev.to/codekingai/my-ai-assistant-needed-a-control-plane-not-a-bigger-loop-15aa Optional learning community: https://t.me/GyaanSetuAi