𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻

Building AI agents for production is different from building demos. Real systems face network issues, limited resources, and unpredictable users. You need a resilient architecture to prevent system failure.

Here are the main architectural patterns for production AI agents:

Stateless Architecture Each request is independent. No memory exists between calls. • Pros: Easy to scale, fast recovery, and low memory use. • Cons: High latency if you fetch context from a database. • Best for: Simple Q&A bots and classification tasks.

Stateful Architecture Agents keep internal memory of past interactions. • Pros: Natural conversations and better reasoning. • Cons: Hard to scale and complex to manage data. • Best for: Personal assistants and complex workflows.

Synchronous Architecture The agent waits for one task to finish before starting the next. • Pros: Easy to debug and predictable. • Cons: Slow performance and wasted resources. • Best for: Simple workflows with strict ordering.

Asynchronous Architecture The agent starts a task and moves to the next one immediately. • Pros: High throughput and better resource use. • Cons: Harder to debug and complex error handling. • Best for: Systems managing multiple external services.

Monolithic Architecture All agent functions live in one single unit. • Pros: Simple deployment and low overhead. • Cons: One error can crash the whole system. • Best for: Small teams and rapid prototyping.

Microservices Architecture Functions are split into independent services. • Pros: You can scale parts separately and isolate failures. • Cons: High operational complexity and network latency. • Best for: Large scale systems and big organizations.

How to choose your path:

Do not over-engineer early. Start simple. Move to complex patterns only when you hit specific bottlenecks.

Source: https://dev.to/dorjamie/resilient-ai-agents-comparing-architectural-approaches-for-production-1en6