<The Agent Expert />

From Amnesia to Expertise.
A visual exploration of persistent mental models in AI agents.

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1. The Problem: Amnesia

Generic agents are brilliant but forgetful. They burn tokens re-learning context every session.

Try it: Tell the agent your project name is "Orion". Then restart the session and ask "What is my project?"

TERMINAL: GENERIC AGENT
Agent: Hello. I am ready.
SESSION MEMORY: {}

2. The Solution: Mental Models

To create an expert, we externalize memory into a structured file (YAML/Markdown). The agent consults this file before answering.

Lab: Edit the YAML file directly. Change users to clients. Then ask the agent: "What table stores people?"

AGENT INTERFACE
I have read the mental model.
expertise.yaml

3. The Learning Loop

True experts validate their models. If the code (Source of Truth) changes, the expert detects the drift and updates its Mental Model automatically.

CODEBASE (SQL)
CREATE TABLE users (
 id INT,
 email TEXT
);
AGENT LOGS
Waiting for trigger...
expertise.yaml
table_name: users

4. Meta-Agentics

We don't write experts manually. We use a Meta-Agent to spawn specialized experts and generate their initial mental models.

Lab: Define a domain (e.g., "WebSocket", "Payment") to generate a new expert.

META-AGENT FACTORY
Meta-Agent
[Waiting for Input]

5. Summary: Compounding Knowledge

The Agent Expert accumulates wisdom. While generic agents stay flat, the Expert's utility grows over time.

Knowledge / Context Execution Time (Sessions) Generic Agent (Flat) Agent Expert

STATUS: EXPERTISE ARCHIVED.