AI Agent: How to Set It Up

AI Agent is a separate step inside an automation that can make decisions on its own by analyzing incoming data and choosing the right action depending on the situation.

No more manually setting up conditions and branching. Just describe the task in plain language, add instructions, and connect the right tools. The agent will use the selected LLM to choose the most appropriate action based on the situation.

How AI Agent differs from regular automation steps

A regular step always performs one specific action: sending a message, creating a record, or updating data. AI Agent works differently: it analyzes incoming data and decides what action to take based on the instructions you provide.

An agent has four parts:

• Model ("brain") — makes decisions, reads instructions, and chooses what to do.
• Instructions (prompt) — a description of the task in free form and in any language.
• Tools (actions) — actions in third-party services the agent can perform. Connections must be set up in Albato in advance.
• Memory of previous runs — turned off by default, but can be enabled to keep context between runs. This is useful for chatbots.

For example, when a new contact is added in HubSpot, the agent can analyze the contact data and decide what to do next. If the Want to Book a Demo? field is set to Yes, it sends a notification to Slack. If the contact’s Job Title contains Head, Director, Founder, or CEO, it adds the contact to a Google Sheet for high-priority leads, all within a single step.

What AI Agent can do

• Analyze incoming data and make decisions without manual conditions.
• Use actions from connected services as tools.
• Work with data from previous steps.
• Fill in required fields in actions automatically.

How to add AI Agent to an automation

Step 1 — add the agent

AI Agent can only be added as an action. It needs input data, so the automation must start with a trigger, such as a webhook or a schedule.

When you click + to add a new step, you will see a new option: AI agent.

After you add it, the step appears in the automation builder.

It includes:

• Name — by default, AI agent. You can rename it via the context menu (three dots -> Rename)
• Brain icon — select and connect the LLM that will make decisions.
• Gear icon — configure the instructions the agent will follow.
• Add tools button — connect the actions the agent can perform.
• Context menu — additional actions for managing the step.

Step 2 — connect a language model

Click Show LLM settings to choose the model. Available models:

• Albato AI — Albato's built-in model. No separate LLM connection required. See the Pricing section for details.
• OpenAI;
• DeepSeek;
• Google Gemini.

The list of available models will expand over time.

If you choose Albato AI, simply click Continue.

If you choose another model, set up a connection first: click Add connection or select an existing one, then click Continue.

In the settings window, the only required field is the model ID, which specifies the model from the provider you want to use, for example gpt-4 or gpt-5 for OpenAI. Other settings are optional.

You can change the model at any time by clicking Change LLM model on the left.

Step 3 — configure the agent instructions

Click the Gear icon to open the agent settings.

You will see three fields:

• User message — what the agent receives as input: data from previous steps or fixed text. Maximum: 1,000 characters.
• Agent instructions — what the agent should do; the core logic. Maximum: 1,000 characters.
• Guardrails — additional rules and boundaries the agent must follow. Maximum: 1,000 characters.

The AI Agent receives input data, instructions, and Guardrails. Together, these form the task for the model. The more clearly you describe the task, the more accurately the agent will act.

Tips for writing instructions:

• Briefly describe the agent's role and task.
• List which data it should analyze.
• Clearly state when it should act and when it should not.
• Define Guardrails and important rules.
• Avoid unnecessary details, repetition, and conflicting wording.

The Save button becomes active only after all required fields are filled in.

Step 4 — connect tools

Click Add tools to choose actions from connected services the agent can call. Albato offers around 5,000 actions that can be used as tools.

For each tool, specify:

• app;
• the action the agent can perform;
• the app account — an existing connection or a new one.

After clicking Continue, the field mapping window opens.

Each field has an Let the AI Agent to decide button. If enabled, the agent determines what value to place in that field based on your instructions.

When enabled, you can also add field-level instructions so the agent knows how to fill that specific value.

You can combine approaches: let AI Agent fill some fields automatically while configuring others manually with fixed or dynamic values.

Step 5 — configure agent memory (optional)

By default, the agent does not remember anything between runs: each run starts from scratch.

To enable memory, for example for a chatbot, click the three dots icon next to the AI model and turn on Use memory.

Additional fields:

• Memory size — the number of recent interactions the agent takes into account, from 1 to 100. One interaction equals one input plus one output. For example, if the agent receives the message Hello! during an automation run and replies with Hello! How are you?, that counts as one interaction.
• Thread ID — separates memory between different users or conversations. For a Telegram chatbot, pass the chat ID so each user has their own context.

Pricing

Each AI Agent run costs 3 transactions, regardless of how many tools are called.

Additionally:

• Albato AI — 1 transaction per every 2,000 tokens (input and output combined).
• Any other LLM — no additional token charges.

The number of tool calls does not affect the cost.

Example 1. Albato AI

AI Agent with Albato AI, 5 tools, 4,500 tokens used:

• 3 transactions for the agent run
• 3 transactions for tokens (4,500 -> 3 packages of 2,000)

Total: 6 transactions

Example 2. Any other LLM

AI Agent with an external LLM, 5 tools, 4,500 tokens used:

• 3 transactions for the agent run

Total: 3 transactions

Conclusion

AI Agent lets you build more flexible automations without long chains of conditions and branching. Describe the task, add instructions, connect the tools, and the agent will analyze incoming data to choose the right action within the rules you set.

AI Agent does not replace the automation itself. It simplifies its logic. The same automation can handle different scenarios: validate data, qualify leads, send notifications, create records in CRMs, or support chatbot scenarios.

If you have any questions about setup, contact our support team in the chat on the platform.