Building AI agents that work reliably in production requires more than just connecting APIs. Here's how to create robust, scalable agentic systems using n8n workflows with proper error handling and monitoring.
Architecture Overview
Our production AI agent architecture consists of three main components:
- Decision Engine: LLM-powered reasoning and task planning
- Tool Layer: Modular functions for external integrations
- Orchestration Layer: n8n workflows managing execution flow
Error Handling Strategy
Production agents must handle failures gracefully. We implement a multi-layered approach:
Retry Logic
- Exponential backoff for API rate limits
- Circuit breakers for failing services
- Fallback strategies for critical operations
Monitoring and Observability
We track key metrics across the agent lifecycle:
- Task completion rates and execution times
- LLM token usage and costs
- Error rates by component and failure type
- User satisfaction scores
Scaling Considerations
For enterprise deployment, consider:
- Horizontal scaling with queue-based task distribution
- Resource pooling for expensive operations
- Caching strategies for repeated computations
- Load balancing across multiple n8n instances
With proper architecture and monitoring, n8n-based AI agents can handle thousands of concurrent tasks while maintaining reliability and performance. The key is treating them as distributed systems from day one.