I have been noticing something quite interesting from last couple of months. Early AI applications were mostly about prompt engineering and getting the models generate the useful outputs, But when the teams start building real products, the problem is completely changed. The kind of issues we deal with are things like- coordinating with multiple agents, managing context between tools & workflows, memory layer management, agent misbehave cases, and mainly ensuring that the whole system is debuggable. The real challenge at that point becomes system architecture. It reminds me a bit of how distributed systems evolved, the complexity wasn’t the individual services, it was the orchestration between them. While finding resources for this, I came across a book that focuses specifically on designing multi-agent AI systems using MCP and A2A patterns, which I thought was interesting because most resources still focus heavily on prompts or single-agent setups. Curious what you guys think- Are we moving from prompt engineering to AI systems architecture as the main challenge? submitted by /u/FoundSomeLogic
Originally posted by u/FoundSomeLogic on r/ArtificialInteligence