# theory - Functional Specification

**Version**: v1.0.0 | **Status**: Active | **Last Updated**: February 2026

## Purpose

The `theory` submodule provides theoretical foundations for agentic systems. It includes agent architecture patterns (reactive, deliberative, hybrid) and reasoning models (symbolic, neural, hybrid).

## Design Principles

### Modularity

- **Architecture Patterns**: Separate implementations for different architectures
- **Reasoning Models**: Separate implementations for different reasoning approaches
- **Extensibility**: New architectures and reasoning models can be added

### Functionality

- **Reactive Architecture**: Stimulus-response pattern
- **Deliberative Architecture**: Planning-based pattern
- **Hybrid Architecture**: Combines reactive and deliberative
- **Symbolic Reasoning**: Rule-based, logic-based reasoning
- **Neural Reasoning**: Pattern-based, learned reasoning
- **Hybrid Reasoning**: Combines symbolic and neural

## Navigation

- **Human Documentation**: [README.md](README.md)
- **Technical Documentation**: [AGENTS.md](AGENTS.md)
- **Parent SPEC**: [../SPEC.md](../SPEC.md)

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## Detailed Architecture and Implementation

### Design Principles

1. **Strict Modularity**: Each component is isolated and communicates via well-defined APIs.
2. **Performance Optimization**: Implementation leverages lazy loading and intelligent caching to minimize resource overhead.
3. **Error Resilience**: Robust exception handling ensures system stability even under unexpected conditions.
4. **Extensibility**: The architecture is designed to accommodate future enhancements without breaking existing contracts.

### Technical Implementation

The codebase utilizes modern Python features (version 3.10+) to provide a clean, type-safe API. Interaction patterns are documented in the corresponding `AGENTS.md` and `SPEC.md` files, ensuring that both human developers and automated agents can effectively utilize these capabilities.

