> ## Documentation Index
> Fetch the complete documentation index at: https://help.statisfy.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Creating Agents
> Step-by-step guide to creating and configuring AI agents in Agent Studio
# Creating Agents
This guide walks you through creating a new AI agent from scratch, covering configuration options and best practices.
## Prerequisites
* Access to Agent Studio
* Understanding of your use case and goals
* Knowledge of what tools the agent will need
***
## Step 1: Start a New Agent
1. Navigate to **Agent Studio** from the main navigation
2. Click **Agents** in the sidebar
3. Click **Create Agent** or the **+** button
You'll see the agent configuration form.
***
## Step 2: Basic Information
### Agent Name
Choose a descriptive name that indicates the agent's purpose:
| Good Names | Poor Names |
| ------------------------- | ---------- |
| "Account Health Analyzer" | "Agent 1" |
| "QBR Prep Assistant" | "Helper" |
| "Renewal Risk Monitor" | "AI Bot" |
Names should help users quickly understand what the agent does without reading the full description.
***
## Step 3: Write Instructions
Instructions (the system prompt) define how your agent behaves. This is the most important configuration.
### Structure Your Instructions
```markdown theme={null}
# Role
[Who the agent is and what it specializes in]
# Primary Tasks
[What the agent should do when asked]
# Guidelines
[How the agent should approach tasks]
# Output Format
[How responses should be structured]
# Constraints
[What the agent should avoid]
```
### Example: Customer Health Agent
```markdown theme={null}
# Role
You are a customer success analyst specializing in account health assessment and early warning detection.
# Primary Tasks
When analyzing an account:
1. Review health score trends over the past 90 days
2. Analyze engagement patterns from activities
3. Identify key contacts and their recent involvement
4. Compare against similar successful accounts
5. Generate specific, actionable recommendations
# Guidelines
- Base all conclusions on data, not assumptions
- Prioritize actionable insights over general observations
- Consider both quantitative metrics and qualitative signals
- Explain your reasoning clearly
# Output Format
Structure your analysis as:
## Health Assessment
[Overall assessment with health score context]
## Key Findings
- [Finding 1 with data support]
- [Finding 2 with data support]
## Risk Factors
- [Risk 1]: [Mitigation suggestion]
## Recommended Actions
1. [Specific action with expected outcome]
2. [Specific action with expected outcome]
# Constraints
- Never fabricate data or statistics
- Don't make promises about outcomes
- If data is insufficient, say so explicitly
```
***
## Step 4: Select the Model
Choose the AI model based on your needs:
### Model Options
| Model | Speed | Capability | Cost | Best For |
| -------------------- | ------- | ---------- | --------- | -------------------------------- |
| **Gemini Auto** | Varies | Varies | Optimized | Most use cases |
| **Gemini 3 Pro** | Slower | Highest | Higher | Complex analysis, critical tasks |
| **Gemini 3 Flash** | Fastest | Good | Lower | Quick queries, simple tasks |
| **Gemini 2.5 Flash** | Fast | Very Good | Moderate | Balanced needs |
### Recommendations
* **Start with Gemini Auto** - Let the system optimize model selection
* **Use Pro** for complex research, multi-step analysis, or critical decisions
* **Use Flash** for quick lookups, simple formatting, or high-volume tasks
***
## Step 5: Configure Reasoning Level
Reasoning level controls how much the agent "thinks" before responding:
### Options
| Level | Behavior | Use When |
| ---------- | ------------------------- | ---------------------------------- |
| **Auto** | Adapts to task complexity | You're unsure what level is needed |
| **Low** | Quick, direct responses | Simple questions, factual lookups |
| **Medium** | Moderate analysis | Standard analysis, most tasks |
| **High** | Deep reasoning | Complex problems, nuanced analysis |
Higher reasoning = better analysis but slower responses and higher cost. Match the level to your actual needs.
***
## Step 6: Enable Toolkits
Toolkits give your agent capabilities. Enable only what's needed.
### Available Toolkits
| Toolkit | Capabilities | Enable When |
| ------------------- | ------------------------------------------------------------------------- | ----------------------------------------------- |
| **Account Data** | Get account details, health scores, custom fields | Agent needs account information |
| **Activity Data** | Fetch activities, emails, meetings | Agent analyzes engagement |
| **Contact Data** | Get contact details, roles, engagement | Agent works with people |
| **Task Management** | Create/update tasks, add comments | Agent manages follow-ups |
| **Project Data** | Get project status, tasks, timelines | Agent reviews implementations |
| **Communication** | Send emails, Slack messages | Agent takes outreach actions |
| **CRM Integration** | Salesforce/HubSpot operations | Agent syncs with CRM |
| **Knowledge Base** | Search documents, articles | Agent needs reference materials |
| **Data Analysis** | Run Python code for accurate counting, aggregation, and data calculations | Agent needs precise arithmetic or data analysis |
### Guidelines
Each enabled toolkit adds complexity. An agent with 3 focused toolkits will often outperform one with 10 toolkits.
A health analysis agent needs Account + Activity data. It probably doesn't need Task Management or Communication.
Will users expect the agent to take actions (create tasks, send messages) or just provide information?
***
## Step 7: Set Scope
Define what data the agent can access:
### Scope Options
| Scope | Access | Use For |
| ----------- | ------------------------ | ------------------------- |
| **Global** | All organization data | General-purpose agents |
| **Account** | Specific account context | Account-focused workflows |
### Account Scope
When using account scope:
* Agent can only access data for the specified account
* Useful for embedding agents in account-specific contexts
* Provides data isolation and relevance
***
## Step 8: Add Datasets (Optional)
Connect specific data sources for the agent to reference:
* **Knowledge bases** - Internal documentation
* **Playbooks** - Standard procedures
* **Templates** - Response templates
This gives the agent access to your organization's specific knowledge.
***
## Step 9: Add a Presentation Template (Optional)
If your agent generates presentations, you can give it its own branded PowerPoint template:
1. In the **Presentation Template** section, click **Upload Template**
2. Select a `.pptx` file (PowerPoint format)
3. Once uploaded, the section shows **Template uploaded** with **Replace** and **Remove** buttons
### Template Mode
After uploading, choose how the agent uses your template:
| Mode | Behavior | Use When |
| -------------------------- | -------------------------------------------------------------- | ------------------------------------------------------- |
| **Brand Guide (Creative)** | AI creates new slides inspired by your template's look & feel | You want on-brand decks with flexible layouts (default) |
| **Strict Template** | AI fills content into your template's existing slide structure | Your deck must follow a fixed slide-by-slide format |
An agent's own template takes priority over your organization's default presentation template. To set the organization-wide default instead, see [Stella AI advanced features](/stella-ai/advanced_features).
***
## Step 10: Test Your Agent
Before deploying, test thoroughly.
### Test Scenarios
1. **Typical request**
* Ask something your users commonly would
* Verify the response is helpful and accurate
2. **Ambiguous request**
* Ask something vague
* Does the agent ask clarifying questions or make reasonable assumptions?
3. **Edge case**
* Ask about an account with missing data
* How does the agent handle incomplete information?
4. **Out of scope**
* Ask something outside the agent's purpose
* Does it gracefully redirect or explain limitations?
### Example Test Prompts
```
Test 1: "Tell me about Acme Corp's health"
Expected: Detailed analysis with specific metrics
Test 2: "Acme Corp"
Expected: Asks what information is needed OR provides summary
Test 3: "What's the weather?"
Expected: Explains it can't help with that / redirects to purpose
```
***
## Step 11: Deploy
Once tested:
1. Click **Save** to save your agent
2. Toggle **Active** to enable it
3. The agent is now available for use
***
## Configuration Examples
### Research-Focused Agent
```
Name: Account Research Assistant
Model: Gemini 3 Pro
Reasoning: High
Toolkits: Account Data, Activity Data, Contact Data
Scope: Global
Instructions:
You conduct deep research on customer accounts. When asked about
an account, provide comprehensive analysis covering health metrics,
engagement trends, key contacts, and strategic recommendations.
Always cite specific data points and explain your reasoning.
```
### Action-Oriented Agent
```
Name: Task Creator
Model: Gemini 2.5 Flash
Reasoning: Medium
Toolkits: Account Data, Task Management
Scope: Account
Instructions:
You help users quickly create and manage tasks. When asked to create
a task, gather the essential information (title, assignee, due date)
and create it immediately. Keep interactions concise and efficient.
```
### Communication Agent
```
Name: Outreach Drafter
Model: Gemini Auto
Reasoning: Medium
Toolkits: Account Data, Activity Data, Contact Data
Scope: Account
Instructions:
You draft personalized outreach messages. When asked to draft an
email or message, research the account and contacts first, then
create tailored content that references specific context. Always
output in a ready-to-send format.
```
***
## Best Practices Summary
Begin with minimal toolkits and instructions. Add complexity only when needed.
Vague instructions lead to inconsistent results. Be explicit about what you want.
Sample data doesn't catch real-world issues. Test with actual accounts and scenarios.
Your first version won't be perfect. Collect feedback and refine instructions.
Track how the agent is being used and whether responses are helpful.
## Next Steps
Advanced tips for agent optimization
Embed agents in workflow automations