Component Design

Introduction

This document details the design of each major component of the Meta Agent Platform. It describes the purpose, functionality, interfaces, and interactions of each component, providing a comprehensive view of how they work together to create a cohesive system.

1. Frontend Components

1.1 Visual Workflow Builder

Purpose

To provide an intuitive, visual interface for designing AI agent workflows without requiring coding knowledge.

Features

Node Canvas: Interactive canvas for placing and arranging workflow nodes
Node Palette: Collection of available node types (agents, control flow, HITL)
Connection Tool: Interface for creating directed edges between nodes
Property Panel: UI for configuring selected node properties
Validation: Real-time validation of workflow structure and configuration
AI Assistance: Suggestions and auto-completion for workflow design

Technical Design

Built using React Flow for the graph visualization and editing
Custom node implementations for each agent type and control flow element
Zustand for state management of the builder UI
AI-assisted suggestions implemented through LLM integration

Interfaces

Data Import/Export: Load and save workflow definitions in JSON format
Validation API: Check workflow validity against defined rules
Template API: Save and load workflow templates

1.2 Workflow Monitoring Dashboard

Purpose

To provide real-time and historical visibility into workflow executions.

Features

Workflow List: Overview of all workflows with status and metrics
Run History: Detailed history of workflow executions
Real-Time Monitor: Live view of currently running workflows
Node Status: Visual representation of node execution status
Logs & Outputs: Access to execution logs and node outputs
Timeline View: Temporal representation of workflow execution
Multi-Modal Visualization: Specialized views for different data types

Technical Design

React components for dashboard elements
React Query for data fetching and caching
Websocket connection for real-time updates
D3.js for advanced data visualization
Three.js for 3D visualization of multi-modal data

Interfaces

Monitoring API: Fetch workflow execution data
Real-Time API: Subscribe to execution events
Export API: Download execution logs and outputs

1.3 HITL Interface

Purpose

To enable human involvement in workflow execution for approvals, decisions, and interventions.

Features

Task Queue: List of pending HITL tasks assigned to the user
Task Detail: Comprehensive view of task context and instructions
Decision Interface: UI for submitting decisions or inputs
Attachments: Support for viewing and uploading relevant files
Escalation: Interface for requesting additional assistance
Multi-Step Review: Support for sequential approval processes
Communication: Integration with notification channels (Slack, email)

Technical Design

React components for task management
Polling or WebSocket for real-time task updates
Form libraries for structured input collection
Markdown rendering for task instructions

Interfaces

Task API: Fetch and update HITL tasks
Decision API: Submit human decisions to workflows
Notification API: Connect to external communication channels

1.4 Marketplace UI

Purpose

To enable discovery, sharing, and management of agents, templates, and plugins.

Features

Agent Catalog: Browsable, searchable agent directory
Templates Gallery: Collection of workflow templates
Plugin Directory: Extensions for platform functionality
Submission Interface: Tools for publishing new items
Rating & Reviews: User feedback on marketplace items
Monetization: Interface for payments and subscriptions
Quality Metrics: Visibility into testing and compliance status

Technical Design

React components for marketplace browsing and management
Search functionality with filtering and sorting
Integration with payment processing systems
Quality assurance pipeline visualization

Interfaces

Catalog API: Browse and search marketplace items
Submission API: Publish new items to marketplace
Monetization API: Handle payments and subscriptions

1.5 Administration Interface

Purpose

To manage users, tenants, system settings, and platform operations.

Features

User Management: Create, edit, and deactivate user accounts
Role Management: Define and assign roles and permissions
Tenant Administration: Configure multi-tenant workspaces
System Settings: Configure global platform parameters
Audit Logs: Review system and user activity
Resource Monitoring: Track system resource utilization
Edge Device Management: Register and monitor edge deployments
Federated Organization Management: Configure cross-org collaboration

Technical Design

React components for administrative functions
Role-based UI rendering
Audit log visualization
Resource usage dashboards

Interfaces

Admin API: Manage users, roles, and settings
Audit API: Access system activity logs
Resource API: Monitor system resources

2. Backend API Components

2.1 API Gateway

Purpose

To provide a unified entry point for all API requests, handling authentication, routing, and cross-cutting concerns.

Features

Authentication: Verify user identity and credentials
Authorization: Check permissions for requested operations
Request Routing: Direct requests to appropriate handlers
Rate Limiting: Prevent API abuse
Request/Response Logging: Capture API interaction data
Error Handling: Standardize error responses
Cross-Origin Resource Sharing (CORS): Manage cross-origin requests
API Documentation: Interactive OpenAPI documentation

Technical Design

Built using FastAPI middleware architecture
JWT token validation for authentication
RBAC for authorization
Distributed rate limiting with Redis
Structured logging with correlation IDs

Interfaces

Auth API: Login, token management
OpenAPI Docs: Interactive API documentation
Health Check: System status endpoint

2.2 Workflow Management Service

Purpose

To create, update, and manage workflow definitions.

Features

Workflow CRUD: Create, read, update, delete workflow definitions
Version Control: Track changes to workflow definitions
Validation: Ensure workflow definitions meet system requirements
Template Management: Save and load workflow templates
Workflow Translation: Convert visual workflow to orchestrator format
Access Control: Manage permissions for workflow definitions
AI Assistance: LLM-powered suggestions and optimizations

Technical Design

FastAPI routes for workflow management
SQLAlchemy ORM for database operations
Pydantic models for validation
JSON schema validation for workflow structure
Translation engine for converting to Temporal workflows

Interfaces

Workflow API: CRUD operations for workflow definitions
Template API: Manage reusable workflow templates
Validation API: Check workflow validity

2.3 Run Management Service

Purpose

To track and control workflow executions.

Features

Run Triggering: Start workflow executions
Run Status Tracking: Monitor execution status
Run Control: Pause, resume, cancel executions
Run History: Maintain execution history
Input/Output Management: Track inputs and outputs
Log Aggregation: Collect execution logs
Performance Metrics: Measure execution performance
Multi-Modal Result Storage: Handle diverse output types

Technical Design

FastAPI routes for run management
Integration with Temporal API for execution control
Time-series data storage for performance metrics
Specialized storage for multi-modal outputs

Interfaces

Run API: Control and monitor workflow executions
Logs API: Access execution logs
Metrics API: Retrieve performance metrics

2.4 HITL Management Service

Purpose

To manage human-in-the-loop tasks and decisions.

Features

Task Creation: Generate tasks from workflow pauses
Task Assignment: Route tasks to appropriate users
Task Notification: Alert users of pending tasks
Task Status Tracking: Monitor task completion
Decision Capture: Record human decisions
Task Escalation: Route tasks to higher authorities
Integration: Connect with external communication tools

Technical Design

FastAPI routes for HITL management
Notification system with multiple channels
Integration with workflow orchestration for signaling
Escalation rules engine

Interfaces

Task API: Manage and respond to HITL tasks
Notification API: Configure and trigger notifications
Integration API: Connect with external tools

2.5 Agent Registry & Marketplace Service

Purpose

To manage the registry of agents and the marketplace for sharing and discovering agents.

Features

Agent Registration: Record agent configurations
Agent Discovery: Search and browse available agents
Agent Sharing: Publish agents to marketplace
Quality Assurance: Test and verify agent compliance
Monetization: Process payments and subscriptions
Rating System: Collect and display user feedback
Analytics: Track agent usage and performance

Technical Design

FastAPI routes for registry and marketplace
Advanced search functionality
Integration with payment processing systems
Automated testing pipeline for submissions
Analytics engine for usage tracking

Interfaces

Registry API: Manage agent configurations
Marketplace API: Browse and acquire agents
Monetization API: Handle financial transactions
Analytics API: Access usage statistics

2.6 Multi-Tenancy Service

Purpose

To manage workspaces, namespaces, and tenant isolation.

Features

Tenant Management: Create and configure tenants
Workspace Isolation: Ensure data separation between tenants
Resource Allocation: Manage resource quotas per tenant
Billing: Track usage for billing purposes
Cross-Tenant Collaboration: Enable secure sharing between tenants
Tenant Templates: Standardized tenant configurations

Technical Design

FastAPI routes for tenant management
Database schema design for multi-tenancy
Resource quota enforcement
Tenant-aware request processing

Interfaces

Tenant API: Manage tenant configurations
Quota API: Configure and monitor resource limits
Collaboration API: Set up cross-tenant sharing

3. Orchestration Engine Components

3.1 Workflow Engine

Purpose

To manage the execution of workflow definitions according to their structure and dependencies.

Features

Workflow Registration: Register workflow definitions with the engine
Workflow Versioning: Manage multiple versions of workflows
Workflow Execution: Run workflows based on their definitions
State Management: Maintain workflow state across restarts
Concurrency Control: Manage parallel execution of tasks
Error Handling: Manage failures and recovery
Pause/Resume: Support for workflow pausing and resumption
Distributed Execution: Run workflows across multiple nodes

Technical Design

Built on Temporal.io workflow engine
Python SDK for workflow implementation
Workflow registration and versioning via Temporal APIs
State persistence via Temporal's event sourcing

Interfaces

Engine API: Register, start, and control workflows
State API: Access workflow state information
Version API: Manage workflow versions

3.2 Task Scheduler

Purpose

To schedule and execute tasks defined in workflows.

Features

Task Queuing: Add tasks to execution queues
Worker Management: Distribute tasks to worker processes
Task Prioritization: Execute tasks based on priority
Resource Management: Allocate resources to tasks
Balancing: Distribute load across workers
Task Lifecycle: Track task status from scheduling to completion
Task Dependencies: Respect execution ordering requirements

Technical Design

Temporal.io task queue mechanism
Worker processes for task execution
Priority-based queue management
Resource-aware scheduling

Interfaces

Queue API: Manage task queues
Worker API: Control worker processes
Resource API: Configure resource allocation

3.3 HITL Coordinator

Purpose

To coordinate the integration of human-in-the-loop actions within workflows.

Features

Workflow Pausing: Halt workflow execution for human input
Context Preparation: Package workflow context for human review
Signal Waiting: Listen for human decisions
Timeout Management: Handle cases where human input is delayed
Escalation: Route to alternative decision-makers when needed
Decision Recording: Document human decisions in workflow history

Technical Design

Custom Temporal activities for HITL integration
Workflow-to-HITL communication via Temporal signals
State management during paused execution
Timeout handling with configurable policies

Interfaces

Pause API: Pause workflow for human input
Signal API: Send human decisions to workflows
Timeout API: Configure wait-time policies

3.4 Edge Orchestrator

Purpose

To enable workflow execution on edge devices with limited connectivity.

Features

Workflow Distribution: Deploy workflows to edge devices
Offline Execution: Run workflows without continuous connectivity
State Synchronization: Reconcile state when connectivity resumes
Resource Adaptation: Optimize for constrained environments
Mesh Coordination: Enable collaboration between edge nodes
Local Decision Making: Support autonomous operation at the edge

Technical Design

Lightweight Temporal worker for edge devices
Local state persistence with SQLite
Efficient synchronization protocol
Resource-aware execution policies

Interfaces

Deployment API: Distribute workflows to edge
Sync API: Synchronize state with central system
Mesh API: Coordinate between edge nodes

3.5 Federated Orchestrator

Purpose

To enable secure workflow execution across organizational boundaries.

Features

Cross-Org Workflows: Coordinate workflows spanning organizations
Privacy Controls: Ensure data remains within appropriate boundaries
Secure Computation: Enable computation without revealing sensitive data
Distributed Decision Making: Support consensus-based decisions
Compliance Tracking: Ensure adherence to data governance policies
Audit Trail: Maintain detailed records of cross-org interactions

Technical Design

Federated workflow coordination protocol
Secure multi-party computation integration
Zero-knowledge proof verification
Distributed consensus mechanisms

Interfaces

Federation API: Set up cross-organization workflows
Governance API: Define and enforce data policies
Audit API: Access records of cross-org activities

4. Agent Execution Components

4.1 Docker Runner

Purpose

To execute agents packaged as Docker containers.

Features

Image Management: Pull and cache agent images
Container Lifecycle: Create, start, monitor, and stop containers
Input Injection: Provide workflow data to containers
Output Extraction: Retrieve agent results from containers
Resource Control: Limit CPU, memory, and network usage
Security Isolation: Enforce container security boundaries
Log Capture: Collect and store container logs
Volume Mounting: Provide access to persistent storage

Technical Design

Docker Engine API integration via docker-py
Container configuration management
Input/output via environment variables and volume mounts
Resource limitation via Docker cgroups

Interfaces

Container API: Manage container lifecycle
Resource API: Configure container resources
Log API: Access container logs

4.2 API Caller

Purpose

To execute agents exposed as RESTful APIs.

Features

Request Construction: Build HTTP requests from workflow data
Authentication: Manage API credentials
Request Execution: Send requests to API endpoints
Response Handling: Process and validate API responses
Error Management: Handle API failures and retries
Rate Limiting: Respect API rate limits
Caching: Cache responses when appropriate
Timeout Handling: Manage long-running requests

Technical Design

HTTP client implementation (httpx)
Authentication middleware for various auth methods
Retry mechanisms with exponential backoff
Response validation against schemas

Interfaces

Request API: Configure and execute API calls
Credential API: Manage API authentication
Cache API: Control response caching

4.3 A2A/Open Agent Protocol Adapter

Purpose

To enable interoperability with agents implementing standardized protocols.

Features

Protocol Translation: Map between platform and A2A concepts
Agent Discovery: Find and connect to A2A-compatible agents
Message Exchange: Send and receive A2A protocol messages
Capability Negotiation: Determine agent capabilities
Schema Validation: Ensure message compliance with protocol
Version Management: Handle protocol versioning
Extensibility: Support for protocol extensions

Technical Design

A2A protocol client implementation
Protocol version negotiation
Dynamic capability discovery
Message transformation for compatibility

Interfaces

Protocol API: Interact with A2A-compatible agents
Discovery API: Find available agents
Capability API: Query agent capabilities

Purpose

To execute specialized agents for processing vision, audio, and sensor data.

Features

Data Preparation: Preprocess multi-modal inputs
Model Loading: Efficient loading of ML models
Inference Execution: Run ML inference on inputs
Result Processing: Post-process model outputs
Resource Optimization: Efficient use of GPU/specialized hardware
Format Conversion: Transform between data formats
Streaming: Support for continuous data processing
Fusion: Combine inputs from multiple modalities

Technical Design

Integration with ML frameworks (PyTorch, TensorFlow)
Hardware acceleration support (GPU, TPU)
Specialized libraries for different modalities (OpenCV, Whisper)
Streaming data processing architecture

Interfaces

Inference API: Execute ML models on multi-modal data
Model API: Manage ML model lifecycle
Resource API: Configure hardware utilization

4.5 Edge Runtime

Purpose

To enable agent execution on resource-constrained edge devices.

Features

Lightweight Execution: Efficient operation with limited resources
Offline Operation: Function without continuous connectivity
Local Storage: Persist necessary data locally
Synchronization: Update data when connectivity is available
Resource Awareness: Adapt to device capabilities
Power Management: Optimize for battery life
Mesh Communication: Interact with nearby edge devices
Failover: Graceful handling of device or connectivity failures

Technical Design

WebAssembly for cross-platform compatibility
SQLite for local data storage
Efficient sync protocol with delta updates
Dynamic resource allocation based on device state

Interfaces

Execution API: Run agents on edge devices
Storage API: Manage local data
Sync API: Control synchronization
Mesh API: Communicate with other edge devices

5. Database Components

5.1 Relational Database

Purpose

To store structured data for workflows, runs, users, and system configuration.

Features

Schema Management: Define and evolve data structures
CRUD Operations: Create, read, update, delete records
Transaction Support: Ensure data consistency
Indexing: Optimize query performance
Foreign Keys: Maintain data relationships
Migrations: Safely evolve schema over time
Multi-Tenant Isolation: Separate data by tenant
Audit Columns: Track creation and modification timestamps

Technical Design

PostgreSQL database (v15+)
SQLAlchemy ORM for database operations
Alembic for schema migrations
Row-level security for multi-tenancy

Interfaces

ORM API: Object-oriented data access
Migration API: Manage schema versions
Backup API: Data backup and recovery

5.2 Document Storage

Purpose

To store semi-structured data like agent configurations, workflow definitions, and outputs.

Features

JSON Storage: Store and query JSON documents
Schema Flexibility: Support evolving document structures
Indexing: Efficient querying on document fields
Versioning: Track changes to documents
Querying: Complex queries across document contents
Size Management: Handle large documents efficiently
Multi-Tenant Isolation: Separate documents by tenant

Technical Design

PostgreSQL JSONB columns
GIN indexing for JSON content
Query optimization for JSON data
Document compression for large payloads

Interfaces

Document API: Store and retrieve JSON documents
Query API: Search within document contents
Version API: Manage document versions

5.3 Edge Storage

Purpose

To provide data persistence on edge devices with synchronization capabilities.

Features

Local Storage: Persist data on edge devices
Efficient Synchronization: Minimize data transfer with central system
Conflict Resolution: Handle sync conflicts
Offline Operation: Function without connectivity
Space Management: Operate within storage constraints
Prioritization: Sync critical data first
Data Expiration: Automatically manage older data

Technical Design

SQLite for local storage
Custom sync protocol with change tracking
Conflict resolution strategies
Priority-based synchronization

Interfaces

Local API: Store and retrieve local data
Sync API: Control synchronization process
Conflict API: Resolve synchronization conflicts

5.4 Federated Storage

Purpose

To enable secure data sharing and collaborative storage across organizations.

Features

Distributed Storage: Span data across organizational boundaries
Access Controls: Fine-grained permissions on shared data
Encryption: Protect sensitive data at rest
Secure Queries: Privacy-preserving query execution
Audit Trail: Track access to shared data
Compliance: Enforce data governance policies
Location Control: Respect data sovereignty requirements

Technical Design

CockroachDB for distributed SQL
Homomorphic encryption for secure computation
Zero-knowledge proofs for verification
Comprehensive access logging

Interfaces

Federation API: Configure cross-organization sharing
Governance API: Define and enforce data policies
Audit API: Track data access and usage

6. Observability Components

6.1 LLM Tracing

Purpose

To provide deep visibility into the behavior and performance of LLM-based agents.

Features

Prompt Tracking: Record prompts sent to LLMs
Response Capture: Store LLM responses
Token Usage: Track token consumption
Latency Measurement: Monitor response times
Cost Tracking: Calculate API usage costs
Version Control: Track LLM model versions
Evaluation: Assess output quality and relevance
Feedback Loop: Incorporate user feedback for improvement

Technical Design

Integration with Langfuse, Trulens, Arize, PromptLayer
Standardized instrumentation for agent runtimes
Feedback collection mechanism
Cost calculation algorithms

Interfaces

Trace API: Access LLM interaction details
Evaluation API: Assess LLM performance
Feedback API: Collect and process user feedback

6.2 System Monitoring

Purpose

To track the health, performance, and resource utilization of the platform.

Features

Metric Collection: Gather system performance data
Resource Monitoring: Track CPU, memory, disk, network usage
Service Health Checks: Verify component availability
Alerting: Notify of issues or anomalies
Dashboard Visualization: Display system status
Trend Analysis: Identify patterns over time
Capacity Planning: Project future resource needs
SLA Tracking: Monitor service level agreements

Technical Design

Prometheus for metric collection
Custom exporters for platform-specific metrics
Alert manager for notification routing
Grafana for visualization

Interfaces

Metrics API: Access system performance data
Health API: Check component status
Alert API: Configure and manage alerts

6.3 Logging System

Purpose

To collect, store, and analyze logs from all platform components.

Features

Log Aggregation: Collect logs from distributed sources
Structured Logging: Consistent log format
Log Levels: Different verbosity for various scenarios
Context Enrichment: Add metadata to log entries
Search & Filter: Find relevant log entries
Retention Policies: Manage log storage duration
Log Rotation: Prevent unbounded log growth
Correlation: Link related logs across components

Technical Design

Structured logging with JSON format
Loki (or Elasticsearch) for log storage
Log shipping via Promtail or similar
Correlation via trace/span IDs

Interfaces

Log API: Configure logging behavior
Search API: Find and retrieve logs
Retention API: Manage log lifecycle

6.4 Distributed Tracing

Purpose

To track requests as they flow through the distributed system.

Features

Trace Creation: Generate unique identifiers for requests
Span Recording: Track operations within a trace
Timing Capture: Measure duration of operations
Causal Relations: Establish parent-child relationships
Tag Association: Add metadata to traces and spans
Sampling: Control tracing overhead
Visualization: Display request flow and bottlenecks
Anomaly Detection: Identify unusual patterns

Technical Design

OpenTelemetry instrumentation
Integration with trace collection backend
Context propagation across components
Sampling strategies for performance

Interfaces

Trace API: Access and query traces
Instrumentation API: Add tracing to components
Sampling API: Configure trace sampling

6.5 AI-Driven Analytics

Purpose

To leverage AI for advanced monitoring, optimization, and self-healing.

Features

Anomaly Detection: Identify unusual patterns in metrics and logs
Root Cause Analysis: Determine sources of issues
Performance Optimization: Suggest improvements
Predictive Scaling: Anticipate resource needs
Self-Healing: Automatically recover from failures
Pattern Recognition: Identify recurring issues
Workflow Optimization: Suggest workflow improvements
Resource Efficiency: Identify waste and optimization opportunities

Technical Design

Machine learning models for anomaly detection
Time series analysis for prediction
Reinforcement learning for optimization
Integration with orchestration for automated actions

Interfaces

Anomaly API: Detect and report anomalies
Prediction API: Forecast future state
Optimization API: Suggest improvements
Self-Healing API: Trigger automated recovery

7. Marketplace & Registry Components

7.1 Agent Registry

Purpose

To maintain a central repository of agent configurations and metadata.

Features

Agent Registration: Record agent definitions
Metadata Management: Store agent capabilities and requirements
Versioning: Track agent versions
Dependency Tracking: Manage agent dependencies
Search & Discovery: Find agents by various criteria
Access Control: Manage agent visibility
Quality Metrics: Track agent reliability and performance
Documentation: Store agent usage instructions

Technical Design

Registry database schema
Semantic versioning support
Full-text search capabilities
Multi-tenant visibility controls

Interfaces

Registry API: Register and discover agents
Version API: Manage agent versions
Search API: Find agents by criteria

7.2 Marketplace Platform

Purpose

To enable sharing, discovery, and monetization of agents and templates.

Features

Listing Management: Create and update marketplace listings
Category Organization: Organize items by category
Rating & Reviews: Collect and display user feedback
Search & Filter: Find marketplace items
Monetization: Support for paid items
Subscription Management: Handle recurring payments
Publisher Dashboard: Metrics for item creators
Quality Assurance: Test and verify marketplace items

Technical Design

Marketplace database schema
Integration with payment processors
Review and rating system
Automated testing pipeline

Interfaces

Listing API: Manage marketplace listings
Search API: Discover marketplace items
Monetization API: Handle payments and subscriptions
Analytics API: Access marketplace metrics

7.3 Quality Assurance System

Purpose

To ensure the quality, security, and compliance of marketplace items.

Features

Automated Testing: Run tests on submitted items
Security Scanning: Check for vulnerabilities
Performance Testing: Evaluate resource usage
Compliance Checking: Verify regulatory compliance
Documentation Validation: Ensure adequate documentation
Feedback Collection: Gather and process user reports
Version Compatibility: Check compatibility with platform versions
Certification: Apply quality certification to verified items

Technical Design

Test runner infrastructure
Security scanning tools integration
Compliance rule engine
Documentation analyzer

Interfaces

Testing API: Run quality checks
Security API: Perform security scans
Compliance API: Verify regulatory requirements
Certification API: Manage quality certifications

7.4 Monetization Framework

Purpose

To enable financial transactions for marketplace items.

Features

Payment Processing: Handle one-time and recurring payments
Pricing Models: Support various pricing structures
Revenue Sharing: Distribute earnings to creators
Tax Management: Handle tax implications
Subscription Lifecycle: Manage recurring billing
Financial Reporting: Generate revenue reports
Refund Processing: Handle payment disputes
Multi-Currency Support: Support global transactions

Technical Design

Integration with payment processors
Subscription management system
Revenue calculation and distribution
Financial reporting engine

Interfaces

Payment API: Process financial transactions
Subscription API: Manage recurring billing
Revenue API: Track and distribute earnings
Reporting API: Generate financial reports

8. Security & Compliance Components

8.1 Authentication System

Purpose

To verify the identity of users and systems accessing the platform.

Features

User Authentication: Verify user credentials
JWT Management: Issue and validate tokens
SSO Integration: Support enterprise identity providers
Multi-Factor Authentication: Add security layers
API Key Management: Handle service-to-service auth
Session Management: Track active sessions
Password Policies: Enforce strong passwords
Brute Force Protection: Prevent attack attempts

Technical Design

JWT implementation with secure algorithms
OAuth2 / OIDC flows
SAML support for enterprise SSO
MFA integration with standard providers

Interfaces

Auth API: Authenticate users and services
Token API: Issue and validate security tokens
MFA API: Manage multi-factor authentication
Session API: Track and manage user sessions

8.2 Authorization System

Purpose

To control access to resources based on user roles and permissions.

Features

Role Management: Define and assign roles
Permission Definition: Create granular permissions
Access Control: Enforce resource access rules
Policy Administration: Manage authorization policies
Context-Aware Rules: Consider execution context
Delegation: Allow temporary access grants
Audit Trail: Track authorization decisions
Least Privilege: Enforce minimal necessary access

Technical Design

Role-based access control (RBAC) implementation
Attribute-based access control (ABAC) for complex rules
Policy engine for evaluation
Fine-grained permission system

Interfaces

Role API: Manage user roles
Permission API: Define and assign permissions
Policy API: Create and manage access policies
Decision API: Evaluate access requests

8.3 Secure Multi-Party Computation

Purpose

To enable collaborative computation without revealing sensitive data.

Features

Privacy-Preserving Computation: Process data without exposure
Data Ownership Control: Maintain control of sensitive information
Cryptographic Protocols: Secure multi-party algorithms
Result Verification: Validate computation correctness
Federated Analytics: Analyze data across organizations
Zero-Knowledge Proofs: Verify without revealing
Secure Enclaves: Trusted execution environments
Compliance Enforcement: Adhere to data regulations

Technical Design

Homomorphic encryption implementation
Secure multi-party computation protocols
Zero-knowledge proof systems
Trusted execution environment integration

Interfaces

Computation API: Request secure computations
Verification API: Validate computation results
Governance API: Define data usage policies
Audit API: Track secure computation activities

8.4 Compliance Framework

Purpose

To ensure adherence to regulatory requirements and industry standards.

Features

Policy Definition: Create compliance policies
Audit Logging: Track all system activities
Data Governance: Manage data lifecycle
Privacy Controls: Implement privacy-by-design
Industry Compliance: Support GDPR, HIPAA, PCI-DSS, etc.
Risk Assessment: Identify compliance risks
Remediation Tracking: Manage compliance issues
Reporting: Generate compliance documentation

Technical Design

Comprehensive audit logging system
Data classification framework
Policy enforcement engine
Compliance reporting templates

Interfaces

Policy API: Define compliance policies
Audit API: Access activity logs
Governance API: Manage data lifecycle
Reporting API: Generate compliance reports

8.5 Secret Management

Purpose

To securely store and distribute sensitive credentials.

Features

Secret Storage: Encrypt and store credentials
Secret Distribution: Securely provide secrets to components
Rotation: Automatically update credentials
Access Control: Limit secret access
Audit Trail: Track secret usage
Integration: Connect with external vault systems
Encryption: Protect secrets at rest and in transit
Emergency Access: Break-glass procedures

Technical Design

Integration with HashiCorp Vault or similar
Encryption standards (AES-256)
Secure transport mechanisms
Rotation automation

Interfaces

Secret API: Store and retrieve secrets
Rotation API: Manage credential lifecycles
Audit API: Track secret access
Integration API: Connect with external systems

Conclusion

This component design document provides a comprehensive blueprint for implementing the Meta Agent Platform. Each component is designed with clear responsibilities, interfaces, and technical approaches, enabling a modular and extensible system architecture.

The design supports the platform's vision of empowering individuals and organizations to orchestrate, manage, and scale AI agent workflows with unmatched interoperability, observability, and extensibility. The components work together to provide a robust, secure, and user-friendly platform that can evolve from its initial core capabilities to advanced features for multi-modal agents, edge computing, federated collaboration, and AI-driven optimization.