Agentic AI
RAG vs Fine-Tuning: Choosing the Right Approach for Your AI Agent
Fine-tuning feels like the "serious" option and RAG feels like a shortcut. In production, the calculus is almost always the opposite.
The word "agent" gets used for everything from a chatbot with a system prompt to a fully autonomous pipeline. Here is the definition we actually build against.
Ask five vendors what an "AI agent" is and you will get five different answers, three of which describe a chatbot with a longer system prompt. That vagueness costs teams real money — they buy a workflow tool expecting autonomy, or they build an autonomous loop when a simple retrieval-and-respond feature would have shipped in a week. The distinction matters enough that we define it explicitly before any scoping conversation.
An AI agent is a system that observes state, decides on an action from a set of tools, executes that action, and evaluates the result — in a loop, with the loop itself deciding when to stop. A single LLM call that answers a question is not an agent, no matter how good the prompt. The moment you add "and now decide what to do next based on what just happened," you have crossed into agent territory, along with its costs: nondeterminism, longer latency, and a much bigger testing surface.
Most teams that come to us asking for "an agent" actually need tier one. It is faster to build, far easier to evaluate, and it fails in ways you can predict. We push clients toward the smallest tier that solves the problem, then let usage data justify the next tier — not the other way around.
Before writing a line of orchestration code, ask: does this task require more than one round of "look something up, then decide"? If the honest answer is no, you do not need a loop, a planner, or a memory store. You need a well-written prompt and a good retrieval pipeline. Save the agent architecture for the cases that actually branch.
More from the same category.
Fine-tuning feels like the "serious" option and RAG feels like a shortcut. In production, the calculus is almost always the opposite.
Guardrails are not a system prompt that says "do not make things up." They are a set of engineered checkpoints the agent cannot talk its way past.
Turning a Q&A chatbot into an agent that takes actions is not a bigger prompt — it is a different system, with a different failure mode at every layer.
Thirty minutes with the people who would actually do the work — no discovery deck, no account manager.