How AI Agents Make Decisions and Take Actions

The Observe-Decide-Act Loop

Every AI agent, regardless of what it does, follows the same fundamental loop:

• Observe: The agent reads data from its assigned source. This could be new rows in a database table, unread emails in an inbox, recent log entries, incoming webhook payloads, or the current contents of a web page.
• Decide: The agent sends the observed data to an AI model along with a prompt that explains what to do with it. The AI model analyzes the data and returns a decision, classification, summary, or recommended action.
• Act: The agent takes the action specified by the AI's response. This might mean sending an SMS alert, updating a database record, routing data to another system, generating a report, or triggering the next step in a workflow.

This loop is what separates AI agents from simple automations. A basic automation connects a trigger to an action with no intelligence in between. An agent has an AI model in the middle that can interpret, classify, summarize, and make judgments about the data it sees.

How the Decision Step Works

The decision step is where the AI model does the thinking. You write a prompt that tells the model exactly what you want it to do with the incoming data. The quality of this prompt determines how well your agent performs.

For a lead routing agent, the prompt might say: "Read the following form submission. Classify it as a sales inquiry, a support request, or spam. Return only the classification word." The AI model reads the form data, understands the context, and returns one of the three categories. The agent then routes the submission based on that classification.

For a content moderation agent, the prompt might say: "Review the following user comment. If it contains profanity, threats, or spam links, respond with BLOCK. If it is acceptable, respond with ALLOW." The agent feeds each new comment through this check and either publishes or hides it automatically.

The prompt can be as simple or as detailed as needed. For complex decisions, you can include examples of how you want the AI to handle specific scenarios. For simple classification tasks, a one-sentence instruction is usually enough.

Triggers: What Starts an Agent

An agent needs something to start it running. On this platform, agents can be triggered in several ways:

Scheduled Triggers

The agent runs at set intervals, every minute, every hour, once a day, or on specific days and times. Scheduled agents are ideal for monitoring tasks, periodic data analysis, and regular report generation. You configure the schedule in the agent scheduling settings, and the platform's cron system handles execution automatically.

Webhook Triggers

The agent runs when an external system sends data to a webhook URL. This is how agents react to real-time events. When your payment processor sends a webhook about a new charge, when your form builder sends a submission, or when another service sends a notification, the agent activates and processes the incoming data. See How to Trigger a Workflow From a Webhook for setup details.

API Triggers

The agent runs when you or another system calls its API endpoint directly. This gives you programmatic control over when the agent executes. Custom apps can expose API endpoints that trigger agent logic on demand.

Actions: What an Agent Can Do

After the AI makes its decision, the agent acts on it. The range of available actions depends on how the agent is built, but common actions include:

• Send messages: SMS text messages, emails, or notifications to team members or customers
• Update databases: Write new records, modify existing ones, or flag entries for review
• Route data: Send information to different destinations based on the AI's classification
• Call external APIs: Forward data to other services, trigger actions in third-party tools
• Generate content: Create reports, summaries, or responses using the AI's output
• Trigger other workflows: Start additional chain commands or agent processes

An agent can perform multiple actions in sequence. After classifying an email, it might update the database, send a notification to the right team member, and draft a reply all in one run.

Conditional Logic in Agent Decisions

Most agents need to handle different scenarios differently. Conditional logic lets your agent branch into different action paths based on the AI's decision or the data it observes.

For example, a customer feedback agent might classify reviews as positive, neutral, or negative. Positive reviews get a thank-you reply. Neutral reviews get logged for analysis. Negative reviews get escalated to a manager via SMS. Each path is a different branch in the workflow, and the agent follows the right one based on what the AI decided.

Chain commands support conditional branching visually in the workflow editor. Custom apps handle conditions through standard programming logic that the AI generates for you.

Multi-Step Agent Workflows

Simple agents run one observe-decide-act cycle. More sophisticated agents chain multiple cycles together. The output of one step becomes the input for the next.

A data processing agent might first read raw data from a CSV upload, then call the AI to clean and normalize the records, then call the AI again to classify each record, and finally write the cleaned and classified data back to the database. Each step builds on the previous one, and the entire pipeline runs without human intervention.

You can build multi-step agents using chain commands that link together or using custom apps that handle the entire pipeline in one background job. The choice depends on complexity. Chain commands are easier to visualize and debug. Custom apps handle more complex logic with loops, error handling, and database transactions.

Getting Started

The best way to understand how agents work is to build one. Start with a simple monitoring agent that checks a data source and sends you an alert. Once you see the observe-decide-act loop in practice, you can expand to more complex workflows. See How to Build an AI Agent Without Coding for a step-by-step walkthrough.