AI Framework
Agent Structure
AIAF's AI agents will be built using a modular architecture that enables flexibility and composition:
Agent Components
Agent Core: Central orchestration module
Configuration: Parameter settings and preferences
Capabilities: Functional modules providing specific abilities
Interfaces: Methods for interacting with the agent
Runtime: Execution environment and operational logic
Modular Design Benefits
Flexibility: Customize agents for specific use cases
Reusability: Combine existing modules in new ways
Maintainability: Update components independently
Scalability: Optimize resource usage for each component
Implementation Details
Component Registry: Central catalog of available modules
Dependency Management: Handling relationships between components
Version Control: Managing component compatibility
Configuration System: Standardized parameter management
Composition Capabilities
AIAF will support agent composition, allowing multiple agents to be combined for complex tasks:
Composition Types
Sequential: Agents process in series, output of one feeds into another
Parallel: Multiple agents process simultaneously and combine results
Hierarchical: Higher-level agents coordinate lower-level agents
Dynamic: Composition changes based on context or requirements
Orchestration Layer
Flow Control: Manages the sequence of agent interactions
Data Transformation: Converts outputs to required input formats
Error Handling: Manages failures and retries
Performance Optimization: Reduces latency and resource usage
Composition Benefits
Specialization: Each agent can focus on its core strength
Complexity Management: Break down complex tasks
Resource Efficiency: Allocate resources based on specific needs
Reusability: Combine existing agents in new ways
Federation Features
AIAF will support federated learning and agent federation for privacy-preserving, distributed AI:
Federation Architecture
Federation Controller: Manages the federation process
Aggregation Module: Combines updates from participants
Encryption Layer: Ensures privacy of participant data
Participant Nodes: Contribute to federated learning
Privacy-Preserving Techniques
Differential Privacy: Add noise to protect individual data
Secure Multi-party Computation: Compute on encrypted data
Homomorphic Encryption: Process data while encrypted
Zero-Knowledge Proofs: Validate without revealing details
Federation Benefits
Data Privacy: Local processing preserves sensitive data
Regulatory Compliance: Addresses data sovereignty requirements
Reduced Data Transfer: Only model updates are shared
Broader Training Data: Access to diverse data sources
Technical Specifications
AIAF agents will adhere to standardized technical specifications:
Technical Standards
Interface Standards: REST, GraphQL, WebSocket specifications
Data Formats: JSON, Protocol Buffers, Avro schemas
Performance Metrics: Latency, throughput, accuracy benchmarks
Security Requirements: Encryption, authentication, authorization
Agent Capabilities
Text Generation
Creates human-like text
Medium CPU, Low Memory
Language Translation
Converts between languages
Medium CPU, Medium Memory
Image Recognition
Identifies objects in images
High GPU, Medium Memory
Sentiment Analysis
Detects emotional tone
Low CPU, Low Memory
Conversational
Interactive dialogue
Medium CPU, High Memory
Data Analytics
Processes and analyzes data
High CPU, High Memory