# Auto Agents Best Practices
## AskAI - Auto Agents
Have you noticed the AskAI button in the workflow builder? It’s your assistant for building StackAI workflows faster and more seamlessly.
This guide is not a comprehensive overview of everything AskAI can do. Instead, it focuses on best practices for its most powerful capability: prompt-to-workflow generation (Auto Agents).
 
### AskAI request types
Before diving into Auto Agents, it helps to understand the three main types of requests AskAI handles. In practice, most prompts fall into one of these categories:
**1. General Q\&A —** Use this when you want to understand how something works. Typical questions include:
* How specific nodes or integrations work
* Workflow design best practices
* Conceptual questions about agents, prompts, or architecture
**2. Debug Mode —** When something breaks. Always include:
* Exact error message. You can directly Ask AI from the error banner that shows after an error so you don't event need to copy-paste the exact error.
* What the workflow is supposed to do
* Which node or step is failing
The more context you provide, the faster the assistant can help.
**3. Build Workflow (Auto Agents) —** Describe a workflow in natural language and generate it automatically. AskAI will build the workflow structure, add nodes and connections, ask clarifying questions if needed, and show the steps it followed.
You don't need to explicitly select a mode — AskAI infers it from the question you ask. However, Being explicit about your intent — whether you're asking a question, debugging an issue, or building a workflow — helps AskAI respond more accurately.
Let’s now focus on auto agents.
### Auto Agents best practices
Auto Agents are one of the fastest ways to go from idea to working workflow. In seconds, they can generate the structure, nodes, and connections of a multi-step AI agent.
But the difference between a workflow that needs heavy rework and one that becomes a strong starting point often comes down to how you guide the assistant.
The following best practices come from real usage patterns and will help you get the most value out of Auto Agents.
#### 1) Build manually first
* Before using Auto Agents, build a few workflows manually. You'll give better instructions and catch what's off in the output.
* Once you're building: stay hands-on. Understand the changes, monitor results, don’t take any change as valid without keeping a critical eye.
* This matters most when debugging or scaling. If you haven't reviewed everything yourself, troubleshooting under pressure is slow.
#### 2) Use a 4-part prompt
Give the assistant your logic and the goal:
1. Inputs and triggers — What starts the workflow? A form, a file upload, a schedule, an API call?
2. Processing — How many LLM nodes? What sources does each use? Any intermediate steps (classification, routing, extraction)?
3. Outputs and actions — What does it produce? A document, an email, a database record?
4. Apps and tools — Name specific integrations across all of the above. If you don't, the assistant will pick on its own — slower, and may not choose what you use.
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Example: If you know which web scraper you prefer, specify it, otherwise the agent will be slower to find it.
{% endcolumn %}
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{% endcolumn %}
{% endcolumns %}
AskAI is going to call [Ask Human in the Chat](https://www.stack-ai.com/insights/human-in-the-loop-ai-agents-how-to-design-approval-workflows-for-safe-and-scalable-automation) if it needs more information, but if you provide the context beforehand, you will be faster and you will keep deterministic what can be.
#### 3) Know what Auto Agents are good for
Auto Agents rarely generate a complete final workflow on the first attempt. They are best understood as a tool that accelerates different stages of workflow development — and the value they provide depends heavily on where you are in that process.
| Phase | Description | Value Auto Agents Can Provide |
| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Blank Canvas → Minimal Functional Version (V1) | Building the first working version of the workflow. Defining triggers, main processing steps, and outputs. | Highest value. Auto Agents can generate the core architecture: nodes, connections, and basic logic. With clear instructions, you often get 60–70% of a strong V1 instantly. |
| Minimal Functional → Testing Version (V2) | Expanding the workflow with additional capabilities and preparing it for testing and iteration. | Moderate value. Auto Agents can help add meaningful new layers to the workflow structure. |
| Testing → Product Version (V3+) | Refining the workflow based on testing feedback. Improvements are usually small and targeted. | Lower value. Changes tend to be granular, so manual edits or targeted questions to AskAI are often faster and safer. |
Auto Agents provide the most value when helping you design workflow structure.\
As the workflow matures and changes become more targeted, manual edits or direct questions to AskAI often become more efficient.
