How to Build an AI Agent Without Coding
Before You Start
To build an AI agent, you need an account with credits loaded and the Chain Commands app installed. If you plan to have the agent send SMS or email, you will also need the relevant broadcast app configured with a sending number or SMTP provider. For agents that read or write to a database, you will need data already stored in the platform or an external database connected.
This guide walks through building a simple monitoring agent using chain commands. The same principles apply to any type of agent. Once you understand the basic workflow, you can adapt it to email processing, data routing, content moderation, or any other agent use case.
Step-by-Step: Building Your First Agent
Log into your admin panel and navigate to the Chain Commands app. Click Create New Chain Command and give your agent a descriptive name, something like "Daily Lead Classifier" or "Hourly Website Monitor." The visual workflow editor opens with an empty canvas.
Your agent needs to read data from somewhere. Add a step that pulls the information the agent will evaluate. This could be a database query that retrieves new records, a webhook receiver that captures incoming data, or an API call that checks an external system. For a monitoring agent, this step might query your appData table for unprocessed entries. For a webhook-triggered agent, the incoming data arrives automatically as the first input.
Add a step that sends the data to an AI model. Choose your model based on the complexity of the decision. GPT-5-nano (1-2 credits) works for simple yes/no or classification tasks. GPT-4.1-mini (2-4 credits) handles moderate analysis. Write a clear prompt that tells the AI exactly what to do: classify the data, extract specific fields, score it, summarize it, or decide on an action. The more specific your prompt, the more reliable your agent's decisions will be.
Based on the AI's response, your agent needs to take different actions. Add a conditional step that checks the AI's output and routes the workflow down different paths. If the AI classified something as urgent, branch to the alert path. If it classified it as routine, branch to the logging path. If it said to ignore, end the workflow.
Each branch of your conditional logic needs an action. Common actions include sending an SMS or email notification, updating a database record, calling an external API, writing to a report log, or triggering another chain command. Add the appropriate action steps to each branch so the agent follows through on the AI's decision.
If your agent should run automatically on a timer, configure the schedule in the chain command settings. You can set it to run every hour, at specific times of day, on certain days of the week, or at custom intervals. The platform's cron system handles the scheduling and ensures the agent runs reliably. See How to Schedule AI Agents to Run on a Timer for detailed scheduling options.
Before relying on the agent, run it manually with sample data. Check that the AI makes the right decisions, the conditional branches route correctly, and the actions execute properly. Adjust your prompt or add more specific instructions if the AI's decisions are not accurate enough. See How to Test and Debug AI Agent Behavior for testing strategies.
Example: Building a Lead Classification Agent
Here is a concrete example. You want an agent that checks new contact form submissions every 30 minutes, classifies each one, and routes it appropriately.
- Step 1 (Data): Query the database for form submissions with status "unprocessed"
- Step 2 (AI): Send each submission to GPT-4.1-mini with the prompt: "Read this form submission and classify it as SALES, SUPPORT, or SPAM. Consider the subject, message content, and email domain. Return only the classification word."
- Step 3 (Branch): If SALES, go to the sales path. If SUPPORT, go to the support path. If SPAM, go to the discard path.
- Step 4a (Sales action): Send an SMS to your sales manager with the lead details and mark the submission as "routed-sales"
- Step 4b (Support action): Add the submission to your support queue and send a confirmation email to the customer
- Step 4c (Spam action): Mark the submission as "spam" and delete it
This entire agent runs every 30 minutes, processes any new submissions, and handles each one without anyone lifting a finger. At 2-4 credits per AI call using GPT-4.1-mini, processing 50 submissions per day costs roughly 100-200 credits.
Using Custom Apps for More Complex Agents
If your agent needs capabilities beyond what chain commands offer, like custom database tables, complex loop logic, error handling, or integration with multiple external APIs, consider building it as a custom app instead. Describe the agent's behavior to the AI app builder and it generates the server-side code for you, complete with scheduled background jobs and API endpoints.
Custom apps are better for agents that process large datasets, need to maintain state across runs, or have complex decision trees that would be hard to visualize in a flowchart. Chain commands are better for agents with straightforward logic that benefit from visual editing and easy modification.
Next Steps
Once your first agent is running, explore more advanced capabilities. Chain multiple agents together so the output of one feeds into the next. Connect agents to external APIs to extend their reach beyond the platform. And set guardrails to control what your agents can and cannot do as they handle more critical tasks.
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