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

Capability	Description	Resource Requirements
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