Studio: First Steps - Basic Settings – Blip | Blip Help

Index

Introduction
Creating and configuring agents in the builder
2.1 How to create an Agent block
2.2 How to configure the Agent block
2.2.1 Renaming the Agent
2.2.2 Configuring the Instructions tab
2.2.3 Configuring the Knowledge base
2.2.4 Configuring the Exit Conditions tab
2.2.5 Configuring the Tools tab
Full Orchestrator example
FAQ AI Agents example

Introduction

Studio is designed to enable the creation and customization of Artificial Intelligence agents in a visual and intuitive way. With it, you can personalize your agent's behavior, define its knowledge base, set exit conditions for the flow, and integrate external tools.

This tool centralizes all the resources needed for you to build and manage intelligent agents in an intuitive manner.

Creating and configuring agents in the builder

Note: At the end of this article, you will find the configuration JSON file. You can copy it and import it directly into the builder to make any adjustments you want.

How to create an Agent block

On the Studio screen, click Add block and choose the Agent option.

A new Agent block is added to Studio.

How to configure the Agent block

By clicking on the created Agent, you can configure it through the side menu.

Renaming the Agent

First, you can rename the block to make it easier to identify. In the following example, the Smart Contact will be a FAQ about AI Agents.

Configuring the Instructions tab

In this section, you can customize your Agent to meet your needs. Choose the language model (LLM), set the temperature and maximum number of tokens, enter the agent’s initial instructions, among other options.

Accurate configuration is essential to optimize your agent’s performance.

To configure a model, click on the Configure agent button, next to the model indication in the Instructions tab. The Configure Instructions screen will be displayed.

In the Model tab, you can choose the version of the model to be used from the dropdown menu.

Now, Studio offers support for multiple language models, expanding the flexibility for creating agents and automations better suited to different use cases.

This expansion allows you to choose exactly the model that best adapts to the needs of file interpretation, performance, and response depth desired for each conversational flow.
Available GPT models:

gpt-4.1-mini
gpt-4.1
gpt-4.1-nano
gpt-4o
gpt-4o-mini
o4-mini
gpt-5
gpt-5-mini
gpt-5-nano
gpt-5.1
gpt-5.1-chat

Available Gemini models:

gemini-2.5-pro
gemini-2.5-flash

Still in this tab, you can:

Set the model’s temperature;
Set the maximum number of tokens;
Enable/disable context history (short-term memory).

In the Response tab, it is possible to:

Define whether the agent's response will be sent to the user in the customer service channel or stored in a variable. When storing in a variable, it allows other agents to use it.

Define the agent's response format. Being able to choose various formats (including a customized format).

In the Interpretation tab, it is possible to:

Define the types of files that the agent will be able to interpret, as well as configure the treatment if the agent receives an unsupported type.

After defining the agent's settings, simply click Save. If you haven't made any changes, simply click Cancel.

Still in the Instructions tab, it is possible to configure instructions for the AI Agent. For each instruction, it is possible to select its level, choosing between: System, User, Agent, History. Let's go through each one:

System Level

The system level defines the general behavior, persona, and restrictions of the AI agent. It is the most fundamental instruction that establishes the context and the rules of how the model should respond, regardless of the user's specific input.

Example of use: "You are a Python programming specialist. Respond in a clear and objective manner, providing code examples whenever possible."

User Level

The user level represents the end user's direct input or query. It contains the specific question or instruction that the user wants the AI to solve in a given interaction.

Example of use: "What is the capital of France?"

Agent Level

The agent level represents the responses generated by the AI agent itself. In a multi-turn conversation, previous AI messages can be included as context for subsequent interactions, maintaining the coherence of the dialogue.

Example of use: "The capital of France is Paris."

History level

The history level allows using message histories from other agents. This way, it is possible to integrate their context into the current agent.

Example of use:

In practice:

Add an instruction

Select the instruction level:

Enter the instruction:

In the History instruction, you will see the Manage history icon. It allows you to configure how the message history from other agents should be inserted. You can configure the number of messages and their order.

In the System and Agent instructions, you will see a shield icon. It offers guardrail instruction suggestions that you can add to your agent. Simply copy and paste them into the instruction.

For the example, let's create a new system instruction and add a guardrail for the knowledge base.

In each instruction, it is possible to perform remove and duplicate actions through the buttons:

It is possible to move the instruction by clicking and dragging it by the side button (six dots) that appears when hovering the mouse. This way, you can organize the order of the instructions, which is crucial for the agent's behavior.

If the context history option is enabled in the model configuration, it will also appear on the instructions screen and can be ordered along with the others.

Configuring the Knowledge Base

Moving now to the Tools tab, we see the following when opening for the first time:

For the agent to access information, it needs catalogs. These catalogs centralize knowledge from files and URLs, organizing everything around the same topic. To learn more about catalogs, refer to the tutorial on Knowledge Base.

Since your agent has just been created, it does not have any linked catalogs yet. To add one, just click on the "Tools" button "+ Add Tools" + “Knowledge Base”.

By clicking on Knowledge Base, the Add Catalogs window opens. To create a new catalog, click on "Create catalog". You will be redirected to the Knowledge Base management screen, where you can create/remove catalogs.

Following our AI Agents FAQ example, we will create a catalog using an XLSX file with questions and answers and a URL containing information about the types of files accepted in an AI Agent’s knowledge base.

Once created, the catalog will be listed in the Manage Catalogs screen in the builder and will be available to link to your agent. For more details on creating catalogs, see the Knowledge Base tutorial.

To link a catalog to the agent, select the corresponding checkbox. After selection, the catalog will be visible for consultation.

Additionally, you can manage your files and URLs in the knowledge base, just select the desired items (such as XLSX files or links) to define which information the agent should use.

Running the Smart Contact

Now is a good time to test the Smart Contact (SC) with the AI Agent. To do this, connect the Start block to the AI Agent FAQ block and publish the SC flow. In the flow test chat, you can interact with the agent.

Configuring the Exit Conditions tab

To configure the Exit Conditions tab, let's create a new flow. The idea is to build a Blip Smart Contact with three agents:

Plans Agent: Specialized in the plans offered by Blip.
Products Agent: Specialized in available products.
Orchestrator Agent: Responsible for understanding the user’s intent and directing them to the correct specialist agent.

In this scenario, we will also add deterministic blocks to show that it is possible to combine them with AI Agent blocks.

To start, let’s add deterministic blocks that will welcome the user and collect their name. For this, we will copy ready-made blocks from the Block Library and adapt them for our example.

The initial flow looks like this:

Now, you can create the Orchestrator Agent and define its instructions.

In this example, it will have three system instructions:

The first defines its role as the orchestrator.
The second instructs it to address the user by name, using a variable for personalization.
The third is a guardrail to ensure that the orchestrator does not send unnecessary messages to the user.

The knowledge bases for the specialist agents will be the URLs for Blip’s products and plans.

The instructions for each agent will be as follows:

Role Definition: Describes the agent’s area of expertise.
Competitor Guardrails: Prevents the agent from mentioning competitors or similar services.
Tone of Voice Guardrails: Ensures that responses are in Portuguese and maintain a friendly tone.
Grounding Guardrails: Ensures that all information provided is based exclusively on the knowledge base.
User Name: Ensures that the agent addresses the user by name.

Now, just create the agents, configuring them with these instructions and the knowledge base, as seen in the previous steps.

Blip Plans Specialist Agent:

Blip Products Specialist Agent:

Now you can configure the exit conditions. They allow you to instruct the agent on which block the user should be directed to, ensuring that the conversation flow continues correctly.

Let’s start with the Orchestrator. In the Exit Conditions tab, click the + Add routing button.

In Definitions, fill in the name, description, and target block, and save the instruction. Create an exit condition for each specialist agent.

It is also possible to define an exit condition for exception cases.

Now, for each specialist agent, let’s create an exit condition to return to the Orchestrator Agent. This allows the user to go back and switch context.

And this is how the flow looks:

Now it’s time to test!

Configuring the Tools tab

The Tools tab allows you to configure instructions for the agent to interact with external resources. To see a practical example, let’s go back to the Orchestrator Agent.

By clicking Add tool, a list of options will be displayed. For each one, in addition to setting a name, you need to provide a description — which guides the agent on when to trigger the action — and configure the specific data required for its execution.

Let’s use the Set Contact tool as an example, which is used to store user data. Since we have already captured the name, we will use it to fill in the contact information. To do this, simply change the name, provide the description, and map the variable {{name}} to the corresponding field.

You can add conditions for the contact definition to occur. For example, it is possible to include a validation for the user’s name.

To test the functionality, let’s adjust the instruction we used to pass the user’s name. Instead of using the variable {{name}}, we will change the instruction to fetch the name directly from {{contact.name}} and verify that the agent can still address the user by name:

Full Orchestrator example

Download the example here.

FAQ AI Agents example