Agent Structure

Agents are lightweight, containerised modules with well-defined state and mutation potential.

Component	Purpose
Base Model	Pluggable - selected at run-time from a model registry that ships with presets for OpenAI O-series (O3-mini / O3-high / GPT‑4o), Anthropic Claude 3.x (Sonnet / Opus), Meta Llama-3 (405 B / 70 B), DeepSeek Coder / Reasoner, or any open-source checkpoint addressable through an OpenAI-style or Anthropic-style endpoint.
Task Strategy	Blueprint describing how the agent designs, mutates, and orchestrates children.
Mutation Logic	Parameter-level rules (e.g., temperature, context window) plus structural edits (swap attention heads, change prompt templates, introduce new reasoning chains).
Memory & Cache	Short-term scratchpad + long-term vector store for episodic recall.
Fitness Prediction Module	Local heuristic that estimates expected reward before expensive evaluation runs.

An adapter layer automatically normalizes provider-specific chat formats (function-calling JSON, Claude tool-use, etc.), so adding/swapping a model remains a one-line YAML change; no code edits are required. Multiple base-model variants can be instantiated in parallel; the adapter layer merely unifies their interfaces.