Agent Architecture

Dive into the core building blocks of Oxyde agents — including memory recall, emotion modeling, goal prioritization, and inference routing — and how these systems interact to create emergent behavior.

🧠 Overview: How Agents Work

Oxyde agents are LLM-powered actors that:

1. Maintain internal state (emotion, memory, goals)

2. Perceive external input (chat, events, player actions)

3. Dynamically construct prompts from their state

4. Query a selected LLM for a response

5. Emit structured actions, emotions, or replies

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🔁 The Agent Loop

Each frame or turn, agents:

[Input Received]
     ↓
[Update Emotion State]
     ↓
[Recall Relevant Memories]
     ↓
[Reweight & Reprioritize Goals]
     ↓
[Construct Prompt]
     ↓
[Route to LLM]
     ↓
[Parse & Act on Response]

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🧠 Emotional State

Agents maintain a 6D emotional vector:

Emotion	Type
Curiosity	Positive
Trust	Positive
Anger	Negative
Fear	Negative
Happiness	Positive
Energy	Neutral/modulatory

This vector:

• Decays naturally over time

• Is influenced by inputs or LLM responses

• Shapes tone and memory recall bias

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📘 Memory System

Each memory has:

• content: what happened

• tags: emotional tags (e.g., "trust", "fear")

• importance: how strong its weight is

• recency: timestamp for freshness

At runtime, top-K memories are recalled and injected into prompts. Memory can be:

• Short-term (session)

• Long-term (persistent, WIP)

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🎯 Goal Engine

Agents have a list of goal objects with:

• description

• priority (float)

• optional trigger (event or condition)

Goals can be reprioritized dynamically based on:

• New memories

• Player interaction

• Emotional state

Agents behave differently when pursuing different goals — like “explore” vs. “protect”.

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🔄 State Update Frequency

Agents can run:

• Every frame (WASM, native)

• Every dialogue event (Unity, Unreal)

• Interval-based (idle simulation loops)

Runtime config controls this behavior per agent.