5 Architecture Patterns for Production AI Agents (That Actually Work)

Most AI agent demos look great in a tweet. Then you deploy them and everything breaks. I have built six AI agent systems in the last three weeks. Code review agents, research automation, form navigators, interview coaches, and developer briefing tools. Some worked. Some failed badly. Here is what I learned about making agents reliable in production. 1. The Fallback Chain (Never Trust One Model) Your agent will hit rate limits. The model will go down. Your credits will expire at 2am on a Sunday. The fix is a fallback chain. Not "retry the same model" but "try a completely different provider." PROVIDERS = [ {"name": "primary", "client": anthropic_client, "model": "claude-sonnet-4-6"}, {"name": "secondary", "client": openai_client, "model": "gpt-4o"}, {"name": "tertiary", "client": google_client, "model": "gemini-2.5-flash"}, ] async def generate(prompt: str) -> str: for provider in PROVIDERS: try: return await provider["client"].generate(prompt) except (RateLimitError, APIError) as e: