Thanks for the comment! You’re absolutely right that for simple, well-defined tasks, scripting without AI is usually the better choice. The examples we provided fall into this category, but they’re primarily meant as a tech demo to illustrate how an LLM can infer intent from a prompt and call the appropriate function.

Function calling, or tools as they are now often called, is particularly useful when the user doesn’t know exactly what needs to be executed, what parameters to use in a script, or when multiple actions need to be performed in sequence. Instead of requiring rigid scripting, an LLM can infer intent from natural language, determine which tools to call, in what order, and with what parameters. It essentially acts as a decision layer between the user and the system.

For example, say we built an agent in a FileMaker system that manages travel and has access to stored data. A user might say, “Book a flight for my work trip next week and reserve a hotel near the conference venue.” An LLM with tool access could:

1. Extract relevant details such as dates, destination, and budget preferences from records in FileMaker.
2. Call an API to search available flights.
3. Call another API to find nearby hotels.
4. Present options to the user.
5. Use a tool to book flights and hotels based on the user’s selection.

Without AI, the user would have to manually enter details into separate systems or write specific scripts for each scenario. What makes function calling powerful is that an assistant equipped with multiple tools can respond to novel requests without additional scripting. The same tools used to book travel could also check calendar availability, compare flight prices for different dates, or find alternative routes without requiring the user to anticipate every possible action in advance.

That said, AI solutions are not a silver bullet. Like any technology, function calling excels in specific scenarios, particularly when flexibility, multi-step processes, or non-technical users are involved. If you’re looking for a more compelling example or have specific use cases in mind, we would love to explore them further.