# COGNITION

![Cognition AI](./designs/cognition-ai.jpg)

## Entry Layer

The architecture begins with an entry point that routes all requests through Portkey, which serves as our LLM management layer. This design choice provides several advantages:

The Portkey integration allows for sophisticated LLM routing and management, ensuring that requests are directed to the most appropriate model based on complexity and requirements. This layer acts as our first line of defense and optimization point for all AI interactions.

## Core Agent Container (Cloud Run)

The main agent runs in a Cloud Run container, which provides excellent scalability and managed infrastructure. Within this container, we have several key components:

### Configuration System
The configuration system consists of three main parts:
1. YAML Loader - Reads configuration files
2. Hot Reload - Monitors for configuration changes
3. Environment Manager - Handles environment variables and settings

This system allows for dynamic updates to the agent's behavior without requiring redeployment, which is crucial for maintaining flexibility in production.

### Core Crew
The heart of our agent contains three essential components:
1. Planner Agent - Handles high-level task planning
2. Task Router - Directs tasks to appropriate handlers
3. Executor Agent - Manages task execution

This structure leverages CrewAI's native capabilities while adding our own orchestration layer for better control and monitoring.

### Memory System
The memory system is designed with flexibility in mind:
1. Local Cache - Fast, in-memory storage
2. Short Term (Redis) - Distributed cache for scalability
3. Long Term (Firestore) - Persistent storage for important data

The system can switch between local and cloud storage based on deployment needs, providing both development simplicity and production scalability.

### Tool System
The tool system is designed for extensibility:
1. Tool Registry - Manages available tools
2. API Tool Wrapper - Standardizes tool interfaces
3. Response Validator - Ensures tool output quality

All tools are implemented as external APIs (Cloud Functions), allowing for independent scaling and maintenance.

## External Integration

### External APIs (Cloud Functions)
Tools are implemented as Cloud Functions, including:
- GSuite integration
- Trading functionality
- Finance operations

This approach allows each tool to:
- Scale independently
- Be maintained separately
- Be versioned individually
- Have its own resource allocation

### Persistence Layer
The persistence layer includes:
1. Redis - For distributed caching
2. Firestore - For long-term storage
3. Secret Manager - For secure credential storage

## Key Design Decisions

### Simplicity
1. Minimal custom code
2. Heavy reliance on CrewAI's native features
3. Clear separation of concerns

### Scalability
1. Cloud-native design
2. Independently scalable components
3. Flexible memory configuration

### Maintainability
1. Hot reload for configurations
2. External tools as APIs
3. Clear component boundaries

## Implementation Notes

### CrewAI Integration
The design leverages CrewAI's built-in features:
1. Memory management
2. Task planning
3. Tool integration
4. Error handling

### Tool Management
Tools are managed through:
1. External API endpoints
2. Standard interface definitions
3. Central registry
4. Response validation

### Configuration Management
Configurations are handled through:
1. YAML files for readability
2. Hot reload for updates
3. Environment variables for secrets

## Future Considerations

### Planned Improvements
1. Webhook endpoints for tool status updates
2. Enhanced monitoring capabilities
3. Expanded tool ecosystem

### Scaling Strategy
The architecture can scale in several ways:
1. Horizontal scaling of Cloud Run instances
2. Independent scaling of tool functions
3. Distributed memory system when needed

## Development Workflow

The architecture supports a clean development workflow:
1. Local development with local storage
2. Testing with cloud resources
3. Production deployment with full cloud integration

This breakdown shows how each component serves a specific purpose while maintaining simplicity and scalability. The architecture successfully balances the use of CrewAI's native capabilities with custom extensions where needed.