Architecture Design

Introduction

This document details the architecture design for the Meta Agent Platform, an AI Agent Orchestration Platform. The architecture is designed to be scalable, reliable, secure, and extensible, with support for multi-modal agents, edge computing, federated collaboration, and a vibrant agent ecosystem/marketplace.

System Architecture Overview

The Meta Agent Platform follows a modular architecture comprising several key components:

Architecture Overview

Note: This is a placeholder for an architecture diagram. The actual diagram should be created and added to the project.

Core Components

1. Frontend (Client Tier)

Visual Workflow Builder: React Flow-based interface for designing agent workflows
Workflow Monitoring: Real-time and historical workflow execution tracking
HITL Interface: User interface for human-in-the-loop interactions
Marketplace UI: Interface for discovering and managing agents
Administration: User, tenant, and system management

Technologies: React, React Flow, Material UI, Zustand, React Query, TypeScript

2. Backend API (Service Tier)

API Gateway: Routing, authentication, and request handling
Workflow Management: Creation, updating, and management of workflow definitions
Run Management: Tracking and controlling workflow executions
HITL Management: Handling human-in-the-loop tasks and decisions
Marketplace Services: Agent discovery, sharing, and monetization
Multi-Tenancy: Workspace and namespace management

Technologies: Python, FastAPI, SQLAlchemy, JWT/OAuth2/SAML

3. Orchestration Engine (Workflow Tier)

Workflow Execution: Managing the lifecycle of workflow runs
Task Scheduling: Scheduling and executing workflow tasks
State Management: Maintaining workflow and task state
Retry Logic: Handling failures and retries
HITL Coordination: Pausing workflows for human input

Technologies: Temporal.io, Temporal Python SDK

4. Agent Execution (Execution Tier)

Docker Runner: Executing agents in Docker containers
API Caller: Triggering agents via RESTful APIs
A2A/Open Agent Protocol: Supporting standardized agent interfaces
Multi-Modal Runtimes: Executing vision, audio, and sensor agents
Edge Runtime: Lightweight execution for resource-constrained devices

Technologies: Docker Engine, httpx, A2A protocol libraries

5. Database (Data Tier)

Relational Storage: Structured data for workflows, runs, users, etc.
Document Storage: Semi-structured data for agent configurations
Edge Storage: Optimized storage for edge deployments
Federated Storage: Distributed storage for cross-organization collaboration

Technologies: PostgreSQL, SQLite (edge), CockroachDB (federated)

6. Observability Stack (Monitoring Tier)

LLM Tracing: Deep visibility into LLM-based agents
System Metrics: Hardware and system performance monitoring
Logs Management: Centralized log collection and analysis
Tracing: Distributed tracing for request flows
Alerts: Proactive notification of issues
AI Analytics: Anomaly detection and performance optimization

Technologies: Langfuse, Trulens, Arize, PromptLayer, Prometheus, Grafana, Loki, OpenTelemetry

Expanded Architecture Components

Vision Agents: Processing image and video data
Audio Agents: Processing speech and sound
Sensor Data Agents: Processing IoT telemetry
AR/VR Agents: Interacting with augmented and virtual reality
Cross-Modal Orchestration: Coordinating agents across modalities

Technologies: OpenCV, PyTorch, TensorFlow, Whisper, Three.js

8. Edge Computing Framework

Edge Deployment: Distributing workflows to edge devices
Offline Operation: Running without constant connectivity
Resource Optimization: Efficient execution on constrained devices
Mesh Networking: Enabling agent collaboration across nodes
Edge Telemetry: Lightweight monitoring with buffering

Technologies: WebAssembly, lightweight containers, SQLite

9. Federated Collaboration Framework

Cross-Organization Workflows: Secure workflow spanning organizations
Secure Multi-Party Computation: Privacy-preserving data processing
Federated Learning: Distributed model training
Zero-Knowledge Verification: Proof without revealing data
Governance: Policy management for collaboration

Technologies: Secure enclaves, homomorphic encryption, zero-knowledge proofs

10. Marketplace & Registry

Agent Registry: Central repository for agent configurations
Marketplace: Discovery and sharing platform
Monetization: Payment processing and subscription management
Quality Assurance: Automated testing and compliance
Community Governance: Decentralized management of policies

Technologies: Payment gateways, automated testing frameworks

11. AI-Driven Platform Optimization

Workflow Optimization: AI-driven improvement suggestions
Anomaly Detection: Identifying unusual patterns
Predictive Scaling: Anticipating resource needs
Self-Healing: Automated recovery from failures
Performance Analytics: In-depth analysis of platform efficiency

Technologies: Machine learning models, anomaly detection algorithms

Architectural Patterns

The Meta Agent Platform employs several architectural patterns:

1. Layered Architecture

The system is structured in layers (presentation, service, business logic, data access) with clear separation of concerns.

2. Microservices/Modular Monolith

The backend is designed as a modular monolith initially (for v1.0 simplicity), with the option to evolve toward microservices as the platform matures.

3. Event-Driven Architecture

Components communicate through events for loose coupling and scalability: - Workflow state changes trigger notifications - HITL decisions generate events for workflow resumption - Agent completion events update workflow state

4. API Gateway Pattern

A centralized API gateway handles authentication, routing, and cross-cutting concerns.

5. Command Query Responsibility Segregation (CQRS)

Separate paths for read operations (queries) and write operations (commands) to optimize performance.

6. Circuit Breaker Pattern

Protect the system from cascading failures by detecting and preventing calls to failing services.

7. Bulkhead Pattern

Isolate components to prevent failures in one part from affecting others.

Deployment Architecture

Cloud Deployment (Primary)

The platform is designed for deployment in cloud environments:

[Load Balancer] --> [API Gateway/Backend API Instances]
                      |
                      |--> [Temporal Workers (Agent Execution)]
                      |
                      |--> [Database Cluster]
                      |
                      |--> [Observability Stack]

Edge Deployment

For edge scenarios, a lightweight version can be deployed:

[Edge Device] --> [Lightweight API] --> [Edge Runtime]
                   |
                   |--> [Local Database (SQLite)]
                   |
                   |--> [Telemetry Buffer]

Hybrid Deployment

A hybrid model connects cloud and edge deployments:

[Cloud Platform] <--> [Edge Gateways] <--> [Edge Devices]

Communication Flows

1. Frontend <-> Backend

RESTful API calls over HTTPS using JSON payloads
Authentication via JWT Bearer tokens
React Query for data fetching and caching
WebSocket/SSE for real-time updates

2. Backend <-> Orchestrator

Temporal Client (Python SDK) for workflow management
Starting, querying, and signaling workflows
Dynamic deployment of workflow definitions

3. Orchestrator <-> Agent Execution

Temporal Activities encapsulate agent execution
Docker SDK for container management
HTTP clients for API calls
Protocol adapters for A2A/Open Agent Protocol

4. HITL Flow

Workflow reaches HITL step
Activity notifies Backend API
Workflow pauses via wait_for_signal
Frontend displays task in user's queue
User makes decision
Backend signals Temporal workflow
Workflow resumes execution

Specialized protocols for different modalities
Data transformation between modalities
Fusion of multi-modal inputs and outputs

6. Edge Communication

Intermittent synchronization with cloud
Local messaging between edge nodes
Prioritized message delivery for constrained networks

7. Federated Communication

Secure API calls between organizations
Cryptographic protocols for privacy-preserving computation
Decentralized consensus for shared decisions

Scalability Design

The architecture supports horizontal scaling at multiple levels:

Frontend: Stateless React application can be scaled behind a load balancer
Backend API: Stateless services scale horizontally
Orchestration: Temporal supports distributed worker pools
Database: Sharding and read replicas for PostgreSQL
Edge: Designed for thousands of edge devices in a mesh topology
Federated: Built for cross-organization scaling with dozens of participating entities

Fault Tolerance

The architecture includes multiple fault tolerance mechanisms:

Workflow Persistence: Temporal maintains workflow state through failures
Activity Retries: Configurable retry policies for tasks
Circuit Breakers: Prevent cascading failures
Data Replication: Redundant storage for critical data
Self-Healing: AI-driven recovery from failures
Edge Resilience: Offline operation capabilities

Security Architecture

The platform employs a defense-in-depth approach:

Authentication: JWT, OAuth2, OIDC, SAML, MFA
Authorization: RBAC with fine-grained permissions
Data Protection: Encryption at rest and in transit
Secrets Management: Integration with HashiCorp Vault
Container Security: Image scanning, runtime protection
API Security: Rate limiting, input validation
Audit Logging: Comprehensive activity tracking
Federated Security: Zero-trust model for cross-organization interaction
Privacy: Secure multi-party computation, homomorphic encryption

Technical Debt Considerations

The architecture acknowledges potential areas of technical debt:

Initial Monolithic Design: May require refactoring to microservices
Technology Selection: Some choices may need reassessment as the platform evolves
Schema Evolution: Database schema changes will require careful migration
Protocol Adaptation: A2A/Open Agent Protocol is evolving and may require updates

Architectural Decision Records (ADRs)

Key architectural decisions:

ADR-001: Choice of Temporal.io for orchestration
ADR-002: Selection of FastAPI for backend development
ADR-003: Database selection (PostgreSQL)
ADR-004: Authentication strategy
ADR-005: Agent execution isolation approach
ADR-006: Multi-tenancy implementation
ADR-007: Observability stack integration
ADR-008: Edge computing architecture
ADR-009: Federated collaboration approach
ADR-010: AI-driven optimization strategy

Evolution Path

The architecture is designed to evolve through well-defined phases:

Phase 1 (Core): Establish foundation with monolithic backend, Temporal orchestration, Docker execution
Phase 2 (Multi-Modal): Add specialized components for vision, audio, and sensor data
Phase 3 (Edge): Develop lightweight runtime and offline capabilities
Phase 4 (Federated): Implement secure multi-party computation and cross-organization workflows
Phase 5 (AI-Driven): Integrate self-optimization and anomaly detection

Conclusion

The architecture design presented in this document provides a comprehensive blueprint for the Meta Agent Platform. It balances immediate needs with future extensibility, enabling the platform to deliver on its vision of empowering individuals and organizations to orchestrate AI agent workflows with unmatched interoperability, observability, and extensibility.

The modular nature of the architecture allows for phased implementation, starting with the core platform and progressively adding advanced capabilities for multi-modal agents, edge computing, federated collaboration, and AI-driven optimization.