Three tiers of AI systems: Level 1 is basic LLM wrappers for simple retrieval. Level 2 adds multi-step agents but lacks proper measurement and reliability. Level 3 is advanced orchestration where the system plans ahead, reasons critically, reflects on its own logs, and self-corrects. Most teams don't know which level they're building.
Stop behaving like you know AI. There are 3 levels of AI development, and most of you are stuck on level 1. Level 1 is LLM wrappers. This is where you wrap a foundation model and it's just a little RAG application. The job is to process text. You ask a question, spits out an answer. They're great at that. And honestly, it might be good enough for a very basic use case to just retrieve something or a couple things, but it's not going to be very proactive. For general user queries. Level 2: AI agents. This is where things get more real. These systems do not just process text and give you a response. They have a bit more brainpower than that, and it might have some logic, but this is where it fails, where you might have multi-step agentic workflows, but you haven't properly instrumented everything with analytics and evals with the right benchmarks and heuristics to make sure that is it retrieving the thing? Is it evaluating things right? Is it grading its response well? Does this satisfy the user query? And then level 3 is actual agentic orchestration, the current sort of endgame in this landscape. Level 3 doesn't follow your workflow, it creates its own. The systems then plan steps ahead, uses critical reasoning like a human. It reflects on its own trace logs and self-corrects if a deployment fails. So there's like auto-healing and things of that nature. More autonomy, more agency. So which level are you operating on? Comment level and I'll show you how to get to level 3.
