Architecture Overview

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Overview

RunAgent is built on a modern, scalable architecture that enables Python-based AI agents to be accessed from any programming language. The system is designed for high performance, reliability, and ease of use.

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High-Level Architecture

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Core Components

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1. Client SDKs

RunAgent provides native SDKs for multiple programming languages:

• Python SDK: Full-featured with sync/async support
• JavaScript SDK: TypeScript support with async/await
• Go SDK: Idiomatic Go with context-aware operations
• Rust SDK: High-performance with zero-cost abstractions

Each SDK provides:

• Authentication: API key management
• Request/Response: Synchronous and asynchronous calls
• Streaming: Real-time response streaming
• Error Handling: Language-appropriate error types
• Retry Logic: Automatic retry with backoff

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2. API Gateway

The API Gateway handles:

• Request Routing: Routes requests to appropriate agents
• Authentication: Validates API keys and permissions
• Rate Limiting: Enforces rate limits per client
• Load Balancing: Distributes load across agent instances
• Protocol Translation: Converts between HTTP and WebSocket

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3. Agent Manager

The Agent Manager is responsible for:

• Agent Lifecycle: Starting, stopping, and monitoring agents
• Configuration Management: Loading and validating configurations
• Health Monitoring: Checking agent health and availability
• Scaling: Automatically scaling agents based on demand
• Resource Management: Allocating and managing resources

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4. Firecracker MicroVMs

RunAgent uses Firecracker for secure, isolated agent execution:

• Security: Each agent runs in its own microVM
• Isolation: Complete process and resource isolation
• Performance: Near-native performance with minimal overhead
• Scalability: Fast boot times and efficient resource usage
• Sandboxing: Secure execution environment

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Data Flow

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1. Request Processing

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2. Streaming Response

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Security Architecture

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1. Multi-Layer Security

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2. Firecracker Security Model

• MicroVM Isolation: Each agent runs in its own microVM
• Resource Limits: CPU, memory, and network constraints
• File System: Restricted file system access
• Network: Controlled network access
• Process: Complete process isolation

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Scalability Architecture

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1. Horizontal Scaling

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2. Auto-scaling Triggers

• CPU Usage: Scale up when CPU usage exceeds threshold
• Memory Usage: Scale up when memory usage is high
• Request Rate: Scale up when request rate increases
• Response Time: Scale up when response times are slow
• Queue Length: Scale up when request queue is long

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Performance Architecture

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1. Caching Strategy

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2. Performance Optimizations

• Connection Pooling: Reuse connections for efficiency
• Request Batching: Batch multiple requests together
• Response Compression: Compress responses to reduce bandwidth
• CDN Integration: Use CDN for static content
• Database Optimization: Optimize database queries and indexes

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Monitoring and Observability

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1. Monitoring Stack

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2. Key Metrics

• Request Rate: Requests per second
• Response Time: Average and P95 response times
• Error Rate: Percentage of failed requests
• Resource Usage: CPU, memory, and network usage
• Agent Health: Agent availability and performance

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Deployment Architecture

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1. Cloud Deployment

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2. Local Development

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Error Handling Architecture

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1. Error Propagation

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2. Error Types

• Client Errors: Authentication, validation, rate limiting
• Server Errors: Internal server errors, agent failures
• Network Errors: Connection timeouts, network issues
• Agent Errors: Python exceptions, function errors

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Future Architecture

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1. Planned Enhancements

• Multi-Region Deployment: Global agent deployment
• Edge Computing: Deploy agents closer to users
• GPU Support: GPU-accelerated agent execution
• Custom Runtimes: Support for additional languages
• Advanced Caching: Intelligent response caching

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2. Scalability Improvements

• Kubernetes Integration: Native Kubernetes support
• Service Mesh: Istio integration for advanced networking
• Event-Driven Architecture: Event-based agent communication
• Stream Processing: Real-time data processing capabilities

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Best Practices

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1. Agent Design

• Stateless: Design agents to be stateless
• Idempotent: Make operations idempotent
• Error Handling: Implement proper error handling
• Resource Management: Manage resources efficiently
• Monitoring: Add appropriate logging and metrics

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2. Performance Optimization

• Caching: Use caching for expensive operations
• Streaming: Use streaming for long-running operations
• Batch Processing: Batch multiple operations together
• Resource Limits: Set appropriate resource limits
• Connection Pooling: Use connection pooling

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Next Steps