Hello
I am exploring ways to make Retool interfaces more adaptive by using LLMs (like OpenAI or Anthropic) to interpret user inputs and dynamically adjust components such as hiding irrelevant fields; pre-selecting dropdowns / offering suggested queries. 
While it’s possible to call LLMs via APIs and feed their responses into Retool state, managing this in real time across multiple components without hard-coding logic is tricky. 
Has anyone implemented a more generalized / reusable approach? 
One idea I am testing is building a JSON-based instruction layer where the LLM returns a structured payload (e.g., { showField: "email", suggestValue: "marketing@..." }) & using JavaScript in Retool to respond to those outputs.
It works in limited scenarios; but gets messy fast when dealing with multiple field types, complex validation & nested conditions. I would love to hear how others are structuring this logic / if there's a better way to manage LLM-driven UI flows. Checked Retool AI actions | Retool Docs documentation related to this and found it quite informative.
In my research, I stumbled upon the concept of what is Agentic AI ; which refers to AI systems that operate more autonomously to make decisions and initiate actions exactly the kind of adaptive behavior I’m aiming for.
Has anyone here experimented with agentic-style patterns in Retool to go beyond static AI prompts and build more dynamic, context-aware tooling?
Thank you !! 