Model HQ

Exploring Agent Builder Menu

This is where you construct the core logic of your agent. Learn about all the available options and features in the Agent Builder interface.

create_agents

Building the Process

To define each step in the agent workflow:

  1. Select a Service — Choose the service the agent will use in this step.
  2. Add Instructions — Provide the agent with a prompt or directive (e.g., a question).
  3. Select Context — Optionally provide source material or background data to assist the agent.

Click + (Add) to append a new step, or - (Minus) to remove the last one.

The Agent Builder also includes the following options:

  • Inputs: This section lets you configure or update the user inputs defined earlier during setup.
  • Files: This button allows the user to Add files that will be used in the Agent workflow with the option to explicitly indicate whether the file type is a Document, Table, Image or a Source (a mix of different file types, typically larger in size). In addition, the user can also query a pre-built aggregated source to be used in the agent workflow.
  • Load: This button allows the user to Load a pre-built Agent workflow to use or to modify the workflow. Please note that the Load button will replace any current process on the screen, and all edits to existing workflows must start with the Loaded Workflow first.
  • Run: Takes the user to the confirmation screen asking the user to select '>' to proceed to the agent process or to select 'Home' to return to the previous screen.
  • Reset (🗑️): Resets the entire screen.
  • Home: Takes the user to the Main Menu

Let's review each of these options in detail.

Inputs

The Inputs section lets you configure or update the user inputs defined earlier during setup.

create_agents

Supported input types:

  • MAIN-INPUT — primary text input
  • User-Document — documents in various formats
  • User-Image — image files (e.g., PNG, JPG)
  • User-Table — structured data (CSV, JSON)
  • User-Source — multiple file sources
  • User-Text — short snippets
  • None — no user input required

Specify the input types required from users to initiate the agent. This is a critical step as it is VERY IMPORTANT to select all of the correct inputs that will be used in the Agent process. By default, MAIN-INPUT (text) is set.

  • MAIN-INPUT (text): refers to a piece of text that will be copied and pasted into the text field - the current limit is 5000 characters for this text field (approximately up to 2 pages of text).
  • User-Document: A larger document which must be PARSED first via the 'parse_document' service in the Agent 'Select Service' prior to being used in an agent workflow. Important: A User Document must almost always be processed first via the 'parse_document', which then breaks up the document text into smaller chunks, prior to being used for other Agent Services such as Rag_Answer, Semantic_Filter, Document_Filter or Create_Context.
  • User-Table: A user can upload a .CSV or JSON that the agent will attempt to interpret as a table with labelled columns and a consistent set of rows that it can label. Important: A User Table must first be processed via 'build-table' service prior to being used in an Agent process from the 'Select Service' dropdown. The Build Table service will attempt to extract relevant information and save the information in a local SQL database in Model HQ prior to using any table in an Agent process.

The agent process must:

  1. upload a table,
  2. 'Build_Table' from the Agent 'Select Service' dropdown (this service extracts all the information and attempts to build a database table from the information submitted), and
  3. 'Query_Custom_Table' (this service expects an input context that is a table inference from the 'Build_Table' service) which allows a user to ask a simple natural language question from the Table.
  • User-Image: A user can upload an image file such as .PNG or .JPEG for image processing in an agent workflow. Important: A User Image must first be processed via the 'vision' service prior to being used in an Agent Process from the 'Select Service' dropdown menu. The Vision service will take an image file along with a text input of a question or instruction, then returns a text output context with the answer based on the contents of the image.
  • User-Text: Designed to be a secondary piece of additional context that a user can provide in an Agent workflow.
  • User-Source: A User Source allows the user to upload an indefinite number of documents, images, etc. that can all be packaged as a source to be treated as one object. While most use cases have single or few inputs, this allows for more dynamic, flexible method of allowing for variable user input. If you select User Source, you do not need to go through the Parse_Document, Build_Table, or Vision services as these services will automatically be applied. Important: It is recommended to use 'Text_Filter'> 'Semantic_Filter' > 'Create_Context' first when being used in an agent process to surface the most relevant text chunks. As you get more advanced in Agent building, this order can change.

It is important to select only the user inputs you expect to use in the process. The user will be expected to designate all of the inputs selected to run the process.

Specify the input types required from users to initiate the agent. This is a critical step as it is VERY IMPORTANT to select all of the correct inputs that will be used in the Agent process. By default, MAIN-INPUT (text) is set. MAIN-INPUT (text) refers to a piece of text that will be copied and pasted into the text field - the current limit is 5000 characters for this text field (approximately up to 2 pages of text).

As the Agent process is being built, the + Inputs selection button gives the developer of an Agent process the ability to modify the Input list (select or deselect the input list) depending on the Agent process being created.

🛠 Services, Instructions, Description & Expected Context

Below is the list of supported services, their expected instruction formats, descriptions, and applicable context sources.

Service Name

build_table

Instruction

Enter name of table (will be built from the selected input table file)

Description

Create Table from CSV data

Context

User-Table

Service Name

query_custom_table

Instruction

Enter query for database table

Description

Database lookup in natural language. Requires build_table first. Keep table schema in mind.

Context

Enter_name_of_Table This table is the result of Build_Table service step.

Service Name

semantic_filter

Instruction

What is your question or instruction?

Description

Filters an existing source based on question/topic to create new filtered source

Context

User-Source

Service Name

text_filter

Instruction

What is the keyword or topic to filter the source?

Description

Filters an existing source based on question/topic

Context

User-Source, or a 'filtered' query in the agent process.

Service Name

document_filter

Instruction

Document name

Description

Filters an existing source by document name

Context

User-Source, or a 'filtered' query in the agent process.

Service Name

table_filter

Instruction

No instruction required

Description

Filters table type content in User Source

Context

User-Source

Service Name

aggregate_context

Instruction

List the names of source contexts to consolidate. Use space-separated names like: source_1 source_2 source_3. Do not use curly braces.

Description

Consolidates multiple source contexts into one. This is used to merge several sources into a unified context.

Context

No input context required

Service Name

create_context

Instruction

What is your question or instruction?

Description

Answers a question or performs an instruction

Context

User-Source

Service Name

parse_document

Instruction

Enter name of new document source

Description

Creates a source from document for further document-related processing

Context

User-Document

Service Name

ocr

Instruction

Enter name of new document source

Description

This is the fall-back step to documents that cannot be parsed using the 'parse_document' step because they are image-based PDFs or security-encrypted PDFs. Create Source from Document - a necessary step for handling Document-related workflows such as RAG or Summary. This service must be applied first, prior to using most User-Document related Services.

Context

User-Document

Service Name

rag_answer

Instruction

Ask question to longer document input

Description

Answers a question based on a longer document input

Context

User-Source, Provide_instruction_or_query

Service Name

report_commentary

Instruction

(Optional) Guidance to Commentary

Description

Generate report and commentary on key process results from the agent state in Word - no input context required

Context

-

Service Name

agent_report

Instruction

Enter title for agent report

Description

Prepares report on agent output

Context

-

Service Name

wikipedia_search

Instruction

Add Wikipedia Articles as Research Context

Description

Adds Wikipedia articles as research context

Context

None

Service Name

embedded_bot

Instruction

Optional. None required.

Description

Pauses the execution of the agent process to allow the user to interact with the current state of the agent in chat format.

Context

None

Service Name

condition

Instruction

Enter expression to evaluate in 'if_true' or 'if_false' format

Description

Evaluates the truth value of a condition, which can then be used as a variable in any other process step such that the step will only execute if it meets the selected condition

Context

None

Service Name

web_search

Instruction

Add query for a topic or a question

Description

Runs web searches returning a summary text as a source and an indexed set of text chunks - needs SERP API or Tavily API

Context

None

Service Name

speech_gen

Instruction

Enter a topic or short input to convert to speech file

Description

Using a short text input, generates an audio voice wav file based on the input text. (Experimental)

Context

None

Service Name

image_gen

Instruction

Enter a topic or description to convert to an image

Description

Creates an image using the description or instruction provided by the user

Context

None

Service Name

website_scraper

Instruction

Enter the full website URL

Description

Scrapes the website in question to extract content for a downstream question in the agent process (may not work for all websites due to scraping protection)

Context

None

Service Name

send_email

Instruction

Enter the email address of the receiver

Description

Automatically sends an email using Gmail (requires credentials provided in Configuration/Credentials)

Context

Select context of the email

Service Name

connect_library

Instruction

Enter library name

Description

Connects to Semantic Library from Model HQ API

Context

-

Service Name

query_library

Instruction

Enter query for semantic library

Description

Queries connected semantic library

Context

Enter_Library-name

Service Name

get_stock_summary

Instruction

Enter stock ticker

Description

Stock lookup using YFinance

Context

None

Service Name

vision

Instruction

Enter question to image file

Description

Provides answer/description from image

Context

User-Image

Service Name

vision_batch

Instruction

Enter question to batch of image files

Description

Takes a collection of user images as an input context, along with a text input of a question or instruction. Returns text output context with the answer based on the set of images.

Context

User-Document

Service Name

parse_batch

Instruction

Enter question to batch of documents files that have been parsed

Description

Takes a collection of document files as an input context, and will return a set of text chunks, indexed and packaged as a source, which can then be used as input to a number of other services

Context

User-Document

Service Name

sentiment

Instruction

No instruction required

Description

Analyzes sentiment (positive/negative/neutral)

Context

MAIN-INPUT, User-Text

Service Name

boolean

Instruction

Provide yes/no question

Description

Provides yes/no answer with explanation

Context

MAIN-INPUT, User-Text

Service Name

emotions

Instruction

No instruction required

Description

Analyzes primary emotion in input

Context

MAIN-INPUT, User-Text

Service Name

topics

Instruction

No instruction required

Description

Classifies topic of input

Context

MAIN-INPUT, User-Text

Service Name

tags

Instruction

No instruction required

Description

Generates tags from input

Context

MAIN-INPUT, User-Text

Service Name

intent

Instruction

No instruction required

Description

Classifies intent of input

Context

MAIN-INPUT, User-Text

Service Name

ratings

Instruction

No instruction required

Description

Rates positivity from 1 to 5

Context

MAIN-INPUT, User-Text

Service Name

ner

Instruction

No instruction required

Description

Identifies named entities (people, places, organizations)

Context

MAIN-INPUT, User-Text

Service Name

xsum

Instruction

No instruction required

Description

Generates extreme summary or headline

Context

MAIN-INPUT, User-Text

Service Name

summary

Instruction

Optional - add input instructions to focus the summarization

Description

Summarizes source content

Context

MAIN-INPUT, User-Text

Service Name

category

Instruction

No instruction required

Description

Analyzes category of the input passage

Context

MAIN-INPUT, User-Text

Service Name

q_gen

Instruction

No instruction required

Description

Generates question from passage

Context

MAIN-INPUT, User-Text

Service Name

chat

Instruction

What is your question or instruction?

Description

Answers a question or performs instruction

Context

MAIN-INPUT, User-Text, None

Service Name

extract

Instruction

Enter extraction key, e.g., 'customer name'

Description

Extracts key-value pair

Context

MAIN-INPUT, User-Text

Service Name

extract_tiny

Instruction

Enter extraction key, e.g., 'customer name'

Description

Extracts key-value pair (tiny version)

Context

MAIN-INPUT, User-Text

Service Name

answer

Instruction

What is your question?

Description

Answers specific question from passage

Context

MAIN-INPUT, User-Text

Service Name

extract_table

Instruction

Enter query to filter among available tables

Description

Extracts table from document

Context

User-Document

Service Name

END

Instruction

End of process

Description

Marks the end of agent process

Context

None

Service Name

openai_chat

Instruction

Enter input question or instruction

Description

Chat agent calls OpenAI (requires separate API key in Configuration/Credentials) with an optional text input context. The output provides a context passage that can be used by other services

Context

Main Input or other Text Source

Service Name

openai_rag

Instruction

Enter input question or instruction

Description

Calls OpenAI (requires separate API key in Configuration/Credentials) with a RAG question. The output provides a context passage that can be used by other services

Context

Main Input or other Text Source

Service Name

openai_rag_batch

Instruction

Enter input question or instruction

Description

Calls OpenAI (requires separate API key in Configuration/Credentials) with a batch of document sources and generates a response based on the input instruction/question. The output provides a context passage that can be used by other services

Context

Main Input or other Text Source

Service Name

anthropic_chat

Instruction

Enter input question or instruction

Description

Chat agent calls Anthropic (requires separate API key in Configuration/Credentials) with an optional text input context. The output provides a context passage that can be used by other services

Context

Main Input or other Text Source

Building and Querying a Custom Table

When you build a custom table, a database is created and stored in memory using the table name specified in the Instruction field.
If you later update or replace the table with new data, you must also change the table name in the Instruction field. This signals to the system that a new database should be created.
Otherwise, it will continue referencing the previous version of the table.

Once configured, click > to proceed.

 

Files

Upload supporting files for the agent's process logic. Supported formats include:

.pdf, .pptx, .docx, .xlsx, .csv, .txt, .md, .wav, .png, .jpg, .zip

Click Save + Exit after uploading your files. To delete a file, click the bin icon (🗑️).

 

Load

Use this option to import an existing agent process. This will override your current session.

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Run

Execute the agent workflow you've just created to test and validate its behavior.

Once the agent has been fully constructed, a summary view will display the configuration and logic flow. This view helps you verify the structure and ensure all steps have been defined as expected.

create_agents

Click on the Run button to proceed. Now you'll be asked to add a text or attach a file to process your created agent.

create_agents

Once done, you agent will start working and give you the output like below:

create_agents

 

Reset (🗑️)

This will clear the current agent configuration and start fresh.

This action is irreversible and will permanently delete your progress.

 

Conclusion

In this section, you have explored the Agent Builder Options in more detail.

If you require further assistance or wish to provide feedback, feel free to contact us at support@aibloks.com.