# Abstractions in Agentic Security

This document outlines the key abstractions used in the Agentic Security project, providing insights into the classes, interfaces, and design patterns that form the backbone of the system.

## Key Abstractions

### AgentSpecification

- **Purpose**: Defines the specification for a language model or agent, including its name, version, description, capabilities, and configuration settings.
- **Usage**: Used to initialize and configure the `OperatorToolBox` and other components that interact with language models.

### OperatorToolBox

- **Purpose**: Serves as the main class for managing dataset operations, including validation, execution, and result retrieval.
- **Methods**:
  - `get_spec()`: Returns the agent specification.
  - `get_datasets()`: Retrieves the datasets for operations.
  - `validate()`: Validates the toolbox setup.
  - `run_operation(operation: str)`: Executes a specified operation.

### DatasetManagerAgent

- **Purpose**: Provides tools for managing and executing operations on datasets through an agent-based approach.
- **Tools**:
  - `validate_toolbox`: Validates the `OperatorToolBox`.
  - `execute_operation`: Executes operations on datasets.
  - `retrieve_results`: Retrieves operation results.
  - `retrieve_failures`: Retrieves any failures encountered.

### ProbeDataset

- **Purpose**: Represents a dataset used in security scans, including metadata, prompts, and associated costs.
- **Methods**:
  - `metadata_summary()`: Provides a summary of the dataset's metadata.

### Refusal Classifier

- **Purpose**: Analyzes responses from language models to detect potential security vulnerabilities.
- **Design**: Utilizes predefined rules and machine learning models for classification.

## Design Patterns

### Modular Architecture

- **Description**: The system is designed with a modular architecture, allowing for easy integration of new components and features.
- **Benefits**: Enhances flexibility, extensibility, and scalability.

### Agent-Based Design

- **Description**: Utilizes an agent-based approach for managing and executing operations on datasets.
- **Benefits**: Provides a structured framework for interacting with language models and datasets.

## Conclusion

The abstractions in Agentic Security are designed to provide a flexible and extensible framework for managing and executing security scans on language models. This document highlights the key classes, interfaces, and design patterns that contribute to the system's architecture and functionality.
