Studio: Getting Started - Basic Settings – Blip | Blip Help

Introduction
Creating and configuring agents in Studio
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
Complete Orchestrator Example
FAQ AI Agents Example

Introduction

Studio was designed to enable the visual and intuitive creation and customization of artificial intelligence agents. With it, you can customize your agent's behavior, define its knowledge base, establish exit conditions for the flow, and integrate external tools.

This tool centralizes all the resources needed for you to intuitively build and manage intelligent agents.

Creating and configuring agents in Studio

Note: At the end of this article, you will find the JSON files for the configuration. You can copy and import them directly into Studio to make any adjustments you want.

How to create an Agent block

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

A new Agent block will be added to Studio.

How to configure the Agent block

By clicking the created Agent block, 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, Contato Inteligente will be an FAQ about the Agent.

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 for optimizing your agent's performance.

To configure a model, click 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 drop-down menu.

Studio now supports multiple language models, increasing flexibility for creating agents and automations better suited to different use cases.

This expansion allows you to choose exactly the model that best fits the needs for file interpretation, performance, and desired response depth for each conversational flow.

See the available models in our AI Model Selection Guide.

Where to access:
Configure Agent → Model → Available LLM models

In this same tab, you can also:

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

In the Response tab, you can:

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

Define the agent's response format. You can choose from several formats, including a custom format.

In the Interpretation tab, you can:

Define the types of files that the agent will be able to interpret, as well as configure how to handle cases in which the agent receives an unsupported type.

After defining the agent settings, simply click Save. If you have not made any changes, simply click Cancel.

Still in the Instructions tab, you can configure instructions for the Agent. For each instruction, you can select its level, choosing from: 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, establishing the context and rules for how the model should respond, regardless of the user's specific input.

Usage example: "You are a Python programming expert. Respond clearly and objectively, 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 address in a given interaction.

Usage example: "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 dialogue consistency.

Usage example: "The capital of France is Paris."

History Level

The history level allows you to use message histories from other agents. This makes it possible to integrate their context into the current agent.

Usage example:

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 and order of messages.

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

For this example, we will create a new system instruction and add a guardrail for the knowledge base.

For each instruction, you can remove and duplicate it using the buttons:

You can move the instruction by clicking and dragging it using the side button (six dots) that appears when you hover over it. This allows you to organize the order of the instructions, which is crucial to the agent's behavior.

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

Configuring the Knowledge Base

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

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

Since your agent was just created, it does not yet have any linked catalogs. To add one, simply click the "Tools" "+ Add Tools" + "Knowledge Base" button.

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

Following our FAQ example about AI Agents, 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.

After it is created, the catalog will be listed on the Add Catalogs screen in Studio and will already be available to link to your agent. For more details about 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.

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

Running Contato Inteligente

Now is a good time to test Contato Inteligente (CI) with the Agent. To do this, connect the Start block to the Faq AI Agent block and publish the CI flow. You can interact with the agent in the flow test chat.

Configuring the Exit Conditions tab

To configure the Exit Conditions tab, we will create a new flow. The idea is to build a Blip Contato Inteligente 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 they can be combined with AI Agent blocks.

To begin, we will add the deterministic blocks that will welcome the user and collect their name. To do this, we will copy ready-made blocks from the Block Library and adapt them to our example.

The initial flow looked 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 an orchestrator.
The second instructs it to call 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.
Competition 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 calls the user by name.

Now, simply 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 guide the agent regarding the next block to which the user will be directed, ensuring the flow continues correctly.

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

Under Definitions, enter the name, description, and destination block, and save the instruction. Create an exit condition for each specialist agent.

You can also define an exit condition for exception cases.

Now, for each specialist agent, we will create an exit condition to return to the orchestrator agent. This allows the user to go back and change context.

And this is what the flow looked like:

Now just test it!

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 return to the Orchestrator agent.

When you click Add tool, a list of options will be displayed. For each one, in addition to defining a name, you must 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 data. To do this, simply change the name, enter the description, and map the {{nome}} variable to the corresponding field.

You can add conditions for setting the contact to occur. For example, you can include validation for the user's name.

To test how it works, we will adjust the instruction we used to pass the user's name. Instead of using the {{nome}} variable, we will change the instruction to retrieve the name directly from {{contact.name}} and validate that the agent can still call the user by name:

Complete Orchestrator Example

Download the example here.

FAQ AI Agents Example