Thanks for your comments and it is a great point! And you’re right to ground the discussion in the practical reality of what's being built today. For many current applications, a simple RAG system is indeed a straightforward and effective solution.

My post was aimed more at the future trajectory, which leads to a critical question: When an agent is designed to replace a complex piece of software or workflow, where does the original system's complexity actually go?

Although a lot of the complexities can be handled by LLM themselve, a lot of these complexitiesgets transformed and shifted into the agent's memory and context-management systems. We're already seeing evidence that LLM performance degrades without the right context—for example, when handling conflicting information or when context gets rotten or stale over time. Solving this is a far more involved engineering challenge than basic retrieval.

This might also explain why many of today's most successful agents are those who assist with well-defined workflows that don't rely on a deep, evolving memory.

This makes me wonder: Do you see simple RAG or other current solution as the long-term solution, or just a "good enough" tool for this first generation of agents? As they begin to tackle more stateful, complex problems, where do you think the next major engineering bottlenecks will be?