Use this guide alongside the current product documentation. Available models, integrations and allowances depend on your configured account.
What does an AI agent need?
An agent needs a defined task, a model and tools it is allowed to use. In Superagento, these are visible parts of the canvas. Optional session memory keeps context for a conversation, while structured output defines the fields the rest of your workflow expects.
| Canvas component | Your decision |
|---|---|
| AI Agent | Set its role, instructions and input data. |
| Chat Model | Choose an available provider and credential source. |
| Agent Tools | Enable only the tools this task needs. |
| Session Memory | Optionally connect a trusted conversation ID. |
| Structured Output | Define a record or list with required field types. |
Turn agent results into explicit actions
Superagento agents can use a calculator, the current time, input lookup, reference-text search and configured HTTP GET endpoints. These tools help an agent work with information. Sending email and writing spreadsheet rows belong in downstream workflow actions, where their configuration remains visible.
For example, ask an agent to classify an incoming request and produce a short summary. Map those fields into Google Sheets, then add a condition before sending an email. Keep categories constrained and give incomplete requests a review path so a missing field does not silently become a confident answer.
Use the model and credentials that fit the task
AI steps support providers including OpenAI, Anthropic and Google Gemini. Available models depend on your account and the configured catalogue. Use Superagento credits when provider access is available, or connect your own supported API key. Your provider can charge for calls made with your key; normal workflow limits and action charges still apply.
Start with a small set of representative inputs. Compare whether the output follows your schema, uses the right tools and stays useful when information is missing. A more expensive model is not a substitute for clear instructions and a testable result.
Test the agent before connecting a live action
A successful request is only the start of evaluation. Check the answer, tool arguments, tool results and usage in the run details. Repeat the test with an empty input, an unexpected value and a conflicting instruction inside the input. Adjust the agent's scope when a case fails.
- Define one outcome and the fields it must return.
- Connect a Chat Model and the smallest useful tool set.
- Add structured output when another step needs named fields.
- Test with sample data and inspect the full run.
- Connect the next action after its input mapping is correct.
Frequently asked questions
Can I build an AI agent without coding?
Yes. Superagento lets you connect an agent, model, tools and output configuration on a visual canvas. Connecting a custom API still requires understanding that API's fields and authentication.
Is an AI agent the same as a chatbot?
A chatbot is a conversational interface. An agent can also select from enabled tools to complete a task. Superagento places that task inside a visible workflow, with explicit actions after the agent.
Does Superagento support agent memory?
Yes. Optional Session Memory uses a conversation ID to retain context. Keep the ID specific to the intended conversation; leave memory disconnected for independent requests.
Can an agent send email on its own?
Use a configured downstream email action for delivery. The agent's tool set is for reasoning and information lookup; workflow actions control writes and messages.
Put the idea into practice
Start with a small test and a result you can verify. Keep the first destination reversible, then inspect the complete run before turning on automatic actions.
Related guides
- How to Build an AI Agent Without Code in Superagento
- AI Agents vs AI Workflows: What Is the Difference?
- AI Workflow Automation for Small Teams