# Agentic LLMs, Functions and Developer Tools

Functions allow you to run sandboxed Javascript functions & API calls inside your Gooey.AI workflows.

{% @mermaid/diagram content="---
title: POSSIBILITIES FOR FUNCTIONS
----------------------------------

graph TD

```
subgraph AI Agent
A[User asks a Query] ==> B(AI Agent's RESPONDS)
B ==> C[Response sent to User]
end
D(BEFORE Request to Gooey Workflow)-..->B 
G[(Database)] <--> D
B -..->E(AFTER Request to Gooey Workflow)
E <-->H[(Database)]
```

style A fill:#f9f
style C fill:#f9f
style G fill:#39f
style H fill:#39f" %}

### Example of AFTER Function:&#x20;

{% @mermaid/diagram content="---
title: AFTER FUNCTION FOR ANALYSIS SCRIPT
-----------------------------------------

flowchart TD
A\[User asks a Query] --> B\[AI Agent RESPONDS]
B --> D\[Response sent to User]
D --> |response collected|E{Analysis Script}
A --> |query collected|E
E --> |user needs human handoff|F(AFTER FUNCTION ACTIVATED)
E --> |user was satifised with answer|G\[CHAT LOOP CLOSED]
F --> |user query and contact pushed to CRM|H\[CRM]

" %}

## How do LLM-enabled Functions work?

When the user sends a query in Natural Language, the LLM determines the following:&#x20;

1. does the query require a function?
2. which part of the text should be passed as an argument in the function?

{% @mermaid/diagram content="graph TD
A\[User asks a query] --> B{LLM assess if functions are needed}
C\[LLM responds with function arguments] -->D\[Function is called with arguments]
B --> |Functions needed|C
B --> |Functions not needed|J\[LLM Responds with answer]
D --> E\[Function executes]
E --> F\[Function returns result]
F --> G\[LLM processes function result]
G --> H\[LLM formulates final response]
G --> B
H --> I\[Response sent to user]
J --> I

" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://academy.gooey.ai/ai-for-impact/module-9.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.