Agentic AI

It's built on the same foundation: retrieval plus generation, but the control flow is genuinely different. Traditional RAG runs a fixed sequence every time. Agentic RAG has a decision-maker in the loop that changes what happens next based on what it finds. So it's less "extra steps" and more "the steps aren't fixed anymore."

Different problems entirely. Fine-tuning changes what the model itself knows or how it writes. Agentic RAG doesn't touch the model; it changes how information gets found and assembled before the model answers. You can actually use both together: a fine-tuned model that's also wired into an agentic RAG pipeline.

It depends on how many steps the agent decides it needs, but expect it to take noticeably longer, often several times as long, because each additional retrieval, tool call, or reasoning step adds its own round trip. For a question that needs one lookup, this is a bad tradeoff. For one who genuinely needs three or four sources cross-checked, the extra time is usually worth it.

It can, in a specific way, because it's making more decisions (which source to use, when to stop, how to combine results); there are more places for it to go wrong, and each of those decisions can compound. A bad retrieval, followed by a bad follow-up decision based on that bad retrieval, gives you an error that's harder to spot than a plain wrong answer.

You don't need a vector database at all if your data doesn't call for semantic search. A well-built agent can just as easily query a SQL database, hit an API, or search a knowledge graph; the "agentic" part is about deciding which source fits the question, not about always using vector search.

Usually, it's not the AI reasoning itself; it's a lack of visibility. Teams build the pipeline, it works in testing, then in production, nobody can tell why a specific answer came out wrong because there's no clear trace of what the agent decided to do at each step. Logging and observability aren't optional extras here; skip them, and you're debugging blind.

Usually not yet. It adds real engineering overhead, orchestration, monitoring, and more moving parts to maintain for a payoff that only shows up once your queries are genuinely complex or multi-source. Most small teams are better served starting with plain RAG and only reaching for agentic once they hit a clear wall.

Retrieval answers "what information is relevant to this specific question right now." Memory answers "What do I already know about this user or this ongoing task from before?" They sound similar but solve different problems, and a lot of systems that claim to "remember" you are really just re-running retrieval over a log of past chats, which isn't the same as an agent that actually carries context forward with intent.