Use this guide alongside the current product documentation. Available models, integrations and allowances depend on your configured account.
What is an AI agent?
An AI agent is a software system that uses an AI model, instructions and tools to pursue a task. Its useful distinction is the ability to decide what information or tool call it needs next. That does not imply unlimited autonomy, guaranteed accuracy or permission to use every connected account.
The surrounding application defines available tools, credentials, limits and stopping conditions. In Superagento, those choices are visible in the agent and its connected capability nodes. A chat model produces responses; an agent adds a controlled way to act on information through tools.
Compare agents, workflows and chatbots
Choose the smallest system that handles your task. If the next step is always known, a fixed workflow is often easier to test. If the next information lookup depends on the request, a bounded agent can be useful. A chatbot describes how people interact, rather than how the underlying work is controlled.
| Approach | Who chooses the next step? | Useful for |
|---|---|---|
| Fixed workflow | Rules and connections set by the builder | Moving known fields, schedules and repeatable actions |
| AI workflow | The workflow, with AI inside selected steps | Classification, extraction and content generation |
| AI agent | The model, within its enabled tool set | Tasks requiring different lookups or calculations |
| Chatbot | Depends on its underlying implementation | A conversational interface for a person |
A practical example: route a customer request
For a form with a fixed category field, a condition can route the record directly. Adding a model may add cost without improving that decision. For an open-text request, an AI step can propose a category and a short summary while the workflow applies your routing rules.
If categorization needs a calculation or information from a configured reference endpoint, an agent can use that tool before producing the result. The workflow can then store the result in a sheet. Keep uncertain cases in a review destination rather than giving every model answer permission to send a customer message.
How should you evaluate an agentic workflow?
Evaluate the complete task, including its actions. A fluent answer can still contain the wrong category, an invented fact or a malformed field. Test ordinary requests alongside missing data, repeated events and input that tries to change the system's instructions.
- Write down the expected output and allowed tool use.
- Keep representative test inputs with the result you would accept.
- Check both model output and destination-service behavior.
- Inspect costs and partial failures before increasing volume.
Further reading
Frequently asked questions
Is every AI workflow an agent?
No. A predefined workflow can call an AI model without letting it choose tools or control the process. Naming a feature an agent does not explain its permissions or autonomy.
When should I use a single AI step?
Use one when the task is a defined transformation such as summarizing supplied text or extracting named fields, and it does not require the model to select tools.
Can an agent and a workflow work together?
Yes. Put the agent inside a workflow and map its output into explicit conditions and actions. This combines flexible reasoning with a visible process.
Does adding more agents make a workflow better?
Not automatically. More agents introduce additional calls, dependencies and failure cases. Add complexity only when tests show that the simpler design cannot complete the task.
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
- Visual AI Agent Builder for Your Team
- AI Workflow Automation for Small Teams
- How to Build an AI Agent Without Code in Superagento