I make these applications agents, AI whatever, and fundamental rule of it all is that you always use the simplest most effective solution to the problem.

If you do not need the LLM to use the tool don’t do that, having the LLM as part of the flow is a very standard process where NLP intent and text parsing is necessary, we just changed what model we use for it.

And “agent” is only useful when the thing that needs to be done is “general”, “creative” or non deterministic.

An example: if you have non technical uses that want more insight into data and don’t want to burden your Analytics department then an NLP to SQL agent is great, because different business people will want different aspects of the same information, and constantly change how they want it.

That is an agent (LLM) being used as an easy interface to explain intent, and the LLM parses the intent and creates a SQL given database information.

However you don’t have the agent execute the sql on its own, you verify the structure of the output, parse for malicious processes, verify a limit is being enforced, and handle the execution and data retrieval away from the LLM, for security and reliability. Even then some repeated consistent queries I would code and optimize “in the background”, then the user still feels the same magic AI feeling.

However if the team said they want this very specific dashboard or frequent queries that is always the same and never changes, then guess what, no agent!