The Architecture of AI Personalities: Roles, RAG, and Orchestration
🤖 The Architecture of AI Personalities Hello everyone, and welcome! This post covers the talk I gave at the Vegas Tech Alley AI Meetup in June 2024, where we explored...
🤖 The Architecture of AI Personalities
Hello everyone, and welcome! This post covers the talk I gave at the Vegas Tech Alley AI Meetup in June 2024, where we explored the concept of AI personalities: what they are, what value they provide, and how they are constructed to orchestrate complex actions.
The goal of our work at Kusog AI is to build frameworks that provide value beyond just the chat interface—moving from simple Q&A to coordinated, action-oriented systems.
Video: Tech Alley Vegas | Las Vegas AI Meetup - June 2024
What is an AI Personality?
An AI personality is a layered construct designed to guide the model’s behavior, voice, and even its access to data, allowing for predictable and focused interactions.
Defining the Personality Boundaries
A personality is built from a collection of attributes that influence the AI’s output, including:
- Role: Defining the specific job (e.g., Chief Marketing Officer, Software Engineer, Counselor, Pat Animal CEO). This dictates the boundaries of the conversation and the type of information deemed relevant.
- Origin & Background: Specifying cultural background, location (UK, India, Louisiana), and educational history (e.g., Harvard MBA). This controls the voicing and style of the text, helping to avoid the generic “tapestry” language often seen in large language models (LLMs).
Custom Content Ratings
A key feature in our system is Content Ratings, which sets the boundaries of what the personality is willing to discuss. This goes beyond typical AI moderation:
| Rating | Audience | Purpose & Example |
|---|---|---|
| AI Y-All | Youngest/All Audiences | Standard safe content. |
| AI PG | Parental Guidance | Topics requiring sensitivity. |
| AI MA | Mature Audience | Medical conversations (e.g., surgery) or complex psychology, but not sexual content. |
| AI MA+ | Mature Audience Plus | Designed for sensitive, unmoderated conversations, such as discussing past trauma, where standard LLMs often shut down the dialogue. |
These ratings also drive the backend, determining which underlying model is used, as some models have strict built-in limitations that conflict with higher rating levels.
🏗️ The 3D Memory Architecture (RAG)
The core technical innovation that makes these personalities powerful is the 3D Memory Structure—an extension of the Retrieval-Augmented Generation (RAG) system.
Layered Knowledge
Instead of a single pool of documents, knowledge is organized into vertical layers (Z-index), using Elastic Search as the vector store.
- Z=0 (Base Layer): The traditional RAG layer. This contains the raw, long-term, static data: uploaded documents (Confluence, Jira tickets), web-scraped content, and old emails (personal archives).
- Z=1, 2, 3… (Higher Layers): Each subsequent layer represents a conversation or a summary report.
- Conversations reference chunks from the layer immediately below it, or from the base layer.
- This allows you to build organized summaries (e.g., a report on a 10-year company history) in a single chunk, which can then be referenced by future conversations without needing to re-read the 50 source documents every time.
Query Optimization
When querying the system, the architecture prefers results from higher Z-index conversations if the cosine similarity is close. This means the system prioritizes organized, summarized knowledge (higher Z-index) over raw, disorganized source data (Z=0).
🎬 Action and Orchestration
Personalities are not just decorative; they are tied to tasks and offline jobs that drive real-world actions.
Multi-Mode Prompts
Instead of making separate API calls for text, audio, and images, the system uses a multi-mode prompt. A single prompt sends the request, and the system coordinates all necessary backend jobs:
- Text/LLM Response
- Streaming Audio Response (with appropriate voice/accent based on the personality’s defined location)
- Image Generation (using Stable Diffusion/Control Nets)
- Semantic Network Relationships
This coordination is vital to ensure that the voice and image output are received by the user at the same time as the text, creating a seamless experience.
Future Capabilities
The future direction involves deeper integration to provide ultimate utility:
- Group Conversations: Allowing four or more personalities plus humans to interact simultaneously, feeding tasks into their respective offline job chains.
- Direct Application Control: Moving away from traditional tabbed interfaces. The AI agent will drive a minimal UI, displaying only the specific elements needed for the task at hand (e.g., form fields pop up when the personality starts a story creation task).
- Code Generation: Using conversations to generate executable code by training the AI on a structured architecture, ultimately leading to machine code generation without the intermediate step of source code.
This framework moves AI from being a conversational tool to a functional operating system that accelerates human abilities.
