AI agent architecture patterns: How to choose the right one for your workload

Choosing the right architecture pattern for AI agents is crucial for determining cost, reliability, and scalability before development begins. Single-agent systems offer simplicity with fewer language model (LLM) calls per task, while multi-agent systems provide specialization and improved performance for complex tasks across multiple domains. Key patterns include ReAct for iterative reasoning and action, planning-based approaches for structured tasks, and multi-agent designs like orchestrator-worker and hierarchical teams for parallel and sequential workflows. Industries such as financial services, insurance, healthcare, and e-commerce optimize these patterns based on specific constraints like regulatory compliance, latency tolerance, and task complexity. Effective architecture selection can prevent costly rebuilds and improve deployment speed, with tools like Redis offering unified infrastructure for managing multi-agent coordination, data storage, and real-time messaging. By aligning architecture with workload requirements, organizations can create scalable, efficient AI systems that meet their operational needs.

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