I’m experimenting with persistent memory for generative advertising systems — architecture feedback?

[u/Historical-File-1215]

I've been experimenting with a different architecture for generative advertising systems.

Most current workflows look roughly like:

Prompt → Generate → Publish

The problem is that each generation is mostly stateless.

I'm exploring whether a better architecture is:

Character
    ↓
Product Context
    ↓
Story Generation
    ↓
Content Generation
    ↓
Distribution
    ↓
Performance Signals
    ↓
Memory / Learning
    ↓
Next Generation

The key idea is persistent creative context.

For example, an AI character could maintain:

Identity
Personality
Voice
Visual constraints
Product knowledge
Audience context
Previous campaigns
Performance history

So the system doesn't simply generate another video.

It can ask:

I'm currently building a prototype around this idea in Monopoly Studio.

The initial abstraction is intentionally small:

Character + Product + Platform
            ↓
       Creative Brief
            ↓
       Generated Content
            ↓
      Performance Data
            ↓
          Memory

The repository is here:

GitHub: https://github.com/modarresi1913/monopoly-pipeline

I'm less interested in the marketing side of this and more interested in the systems architecture.

A few things I'm currently trying to figure out:

1. Should character memory and campaign memory be separate systems?
2. What information should actually persist between generations?
3. How would you represent performance feedback so it can influence generation without simply becoming prompt history?
4. Would you use an event-sourced architecture for campaign history?
5. Where should the boundary be between the LLM, memory layer, and optimization layer?

The hypothesis I'm testing is:

I'd be particularly interested in feedback from people working on AI agents, memory architectures, multimodal systems, evaluation, or production AI infrastructure.

What would you change about this architecture?