# Automated Agent Improvement

## Overview

Adam v22.0 can autonomously improve its own agents over time. This is achieved through the `AgentImprovementPipeline`, a module that manages the process of improving an agent.

## Agent Improvement Lifecycle

The agent improvement lifecycle consists of the following stages:

1.  **Diagnosis:** The `MetaCognitiveAgent` monitors the performance of other agents. If an agent's performance degrades, the `MetaCognitiveAgent` triggers the `AgentImprovementPipeline`. The pipeline then determines the root cause of the performance degradation (e.g., outdated data, suboptimal prompts, model drift).
2.  **Remediation:** The pipeline automatically takes corrective action, such as retraining the agent's model, fine-tuning its prompts, or flagging a data source for review.
3.  **Validation:** The pipeline tests the improved agent to ensure its performance has increased.

## KPIs

The following Key Performance Indicators (KPIs) are used to monitor agent performance:

*   Task success rate
*   Execution time
*   User feedback scores

## Manual Trigger

The improvement pipeline can be manually triggered if necessary.