# Autonomous Self-Improvement (SEAL)

## Overview
The v23.0 architecture implements the **SEAL (Self-improving Embodied Agents Learning)** framework to enable the system to evolve without human code changes. This is the "Outer Loop" of the adaptive system.

## Architecture

### 1. Controller (`autonomous_self_improvement.py`)
The central brain that monitors system health and triggers the adaptation workflow. It maintains a failure log and triggers training when a threshold (e.g., 3 failures) is met.

### 2. Agent Forge
A synthetic data generation service. When a domain failure is detected (e.g., "Market Risk"), the Forge uses a powerful LLM to generate thousands of diverse test cases for that specific domain.

### 3. Code Alchemist
The finetuning engine. It takes the "Self-Edits" (successful corrections generated by the agents in the sandbox) and runs a LoRA (Low-Rank Adaptation) training job to update the failing agent's model. It then hot-swaps the new model version into production.

## Workflow
1. **Monitor:** Detects recurring failure in `RiskAssessmentAgent`.
2. **Forge:** Generates 1000 synthetic risk scenarios.
3. **Sandbox:** Runs current agent on scenarios; Agent self-corrects.
4. **Reward:** Validates corrections.
5. **Alchemist:** Finetunes `RiskAssessmentAgent-v23.1`.
6. **Deploy:** Updates the graph to use the new adapter.