AI Agents

AI Agents are intelligent assistants that go beyond simple automations. Powered by large language models, they can understand context, reason through problems, use tools to gather information, and take actions autonomously.

What Makes Agents Different?

Traditional Automations

Follow predefined paths
Execute fixed sequences
Rule-based decisions
Predictable outcomes

AI Agents

Reason about tasks
Dynamic decision-making
Use tools intelligently
Adapt to context

Agent Capabilities

Contextual Understanding

Agents understand natural language instructions and can interpret ambiguous requests based on context:

"Prepare me for my call with Acme Corp tomorrow"

The agent will:

Look up the Acme Corp account
Review recent activities and conversations
Check health scores and open tasks
Summarize key points for the call

Tool Usage

Agents can use tools to:

Fetch data from Statisfy and integrated systems
Take actions like sending emails or creating tasks
Search through documents and knowledge bases
Calculate metrics and analyze trends

Multi-Step Reasoning

Agents break complex tasks into steps:

Task: "Find accounts at risk of churning and draft outreach emails"

Agent reasoning:
1. Query accounts with declining health scores
2. For each account:
   a. Analyze recent activity patterns
   b. Identify key risks
   c. Draft personalized outreach
3. Compile results

Agent Configuration

When creating an agent, you configure:

Instructions (System Prompt)

Define the agent’s role, expertise, and behavior:

You are a customer success analyst specializing in account health.
Your role is to:
- Identify early warning signs of churn
- Provide actionable recommendations
- Communicate findings clearly and concisely

Always base your analysis on data, not assumptions.

Model Selection

Choose the AI model that powers your agent:

Model	Characteristics	Best For
Gemini Auto	Automatically selects based on task	General use
Gemini 3 Pro	Advanced reasoning, higher accuracy	Complex analysis
Gemini 3 Flash	Fast responses	Simple tasks
Gemini 2.5 Flash	Balanced speed and capability	Most use cases

Reasoning Level

Control how deeply the agent thinks:

Level	Description
Auto	Model decides based on task complexity
Low	Quick responses for simple tasks
Medium	Balanced thinking for standard tasks
High	Deep reasoning for complex analysis

Toolkits

Enable groups of tools the agent can use:

Toolkit	Tools Included
Account Data	Get account details, health history, contacts
Activity Data	Fetch activities, meetings, emails
Task Management	Create tasks, add comments, update status
Communication	Send emails, Slack messages
CRM	Salesforce operations, opportunity updates
Data Analysis	Run Python code for counting, aggregation, and calculations

Scope

Define what data the agent can access:

Scope	Description
Global	Access all organization data
Account	Limited to specific account context

Creating an Agent

Step 1: Define the Agent’s Purpose

Start with a clear goal:

What problems will this agent solve?
Who will use it?
What actions should it take?

Step 2: Write Instructions

Craft detailed instructions that cover:

# Role
You are a [specific role] that helps [target users] with [main task].

# Capabilities
You can:
- [Capability 1]
- [Capability 2]
- [Capability 3]

# Guidelines
- [Guideline 1]
- [Guideline 2]

# Constraints
- Never [limitation 1]
- Always [requirement 1]

Step 3: Select Tools

Enable only the tools the agent needs:

More tools = more capability but slower responses
Fewer tools = faster but more limited

Step 4: Configure Model Settings

Choose based on your needs:

Complex analysis → Higher reasoning, Pro model
Quick answers → Lower reasoning, Flash model

Step 5: Test Thoroughly

Test with various scenarios:

Typical use cases
Edge cases
Unclear or ambiguous requests

Prebuilt Agents

Every Statisfy workspace comes with 25 prebuilt agents ready to use out of the box. These cover the most common customer success, sales, and account management tasks.

Prebuilt agents are fully customizable. You can modify their instructions, tools, and settings to match your team’s workflow.

Account-Scoped Agents

These agents work in the context of a specific account:

Agent	Description
Account News Research	Researches recent news about an account using web search
Account Next Best Step	Recommends the optimal next action for an account
Account Plan	Generates a strategic account plan
Account Support Review	Summarizes recent support interactions
Account Transition	Prepares handover documentation for account transfers
Account Weekly Update	Generates a weekly account status summary
Call Coaching Feedback	Provides coaching feedback on recorded calls
Churn Analysis	Analyzes churn risk factors and suggests interventions
Customer Story	Drafts a customer success story with web research
Expansion / Upsell	Identifies expansion and upsell opportunities
Meeting Prep	Prepares briefing materials for upcoming meetings
Onboarding Blueprint	Creates a structured onboarding plan
People News Research	Researches news about key contacts at an account
Product Usage	Analyzes product usage patterns and trends
QBR / EBR Deck	Prepares quarterly or executive business review content
Renewal Forecast	Forecasts renewal likelihood and risks
Risk Mitigation	Creates a risk mitigation plan with web research
Stakeholder Mapping	Maps key stakeholders and their relationships
Value Delivery	Documents value delivered to the customer

Global Agents

These agents work across your entire book of business:

Agent	Description
Competitive Insight	Researches competitors using web search
Customer Advocate	Identifies top advocates for references and case studies
Customer Lookalike	Finds accounts similar to your best customers
Feature Request	Aggregates and analyzes feature requests
Weekly Priority	Generates a prioritized weekly task list
Weekly User Coaching	Provides coaching recommendations for team members

Example Agents

Account Health Analyst

Purpose: Analyze account health and provide recommendations Instructions:

You are an account health analyst. When asked about an account:

1. Review health score history and trends
2. Analyze recent activities and engagement
3. Identify risk factors and warning signs
4. Provide specific, actionable recommendations

Always explain your reasoning and cite specific data points.

Tools: Account Data, Activity Data

QBR Preparation Assistant

Purpose: Prepare materials for quarterly business reviews Instructions:

You prepare comprehensive QBR materials by:

1. Summarizing key metrics and changes
2. Highlighting achievements and successes
3. Identifying challenges and proposed solutions
4. Recommending discussion topics

Format output as a structured agenda with talking points.

Tools: Account Data, Activity Data, Task Management

Renewal Risk Monitor

Purpose: Proactively identify and address renewal risks Instructions:

You monitor accounts for renewal risks by:

1. Tracking usage patterns and engagement trends
2. Identifying accounts with concerning signals
3. Drafting personalized outreach messages
4. Recommending specific retention actions

Prioritize accounts by risk level and ARR.

Tools: Account Data, Activity Data, Communication

Using Agents in Automations

Agents can be embedded in automation flows using the Agent V2 component:

Trigger → Gather Context → Agent Analysis → Take Action

This combines the reliability of automations with the intelligence of agents.

Best Practices

Instruction Writing

Be specific about the role

Instead of “You are helpful”, try “You are a customer success analyst with expertise in identifying churn risk signals”

Define output format

Specify how responses should be structured:

Format your analysis as:
## Summary
[1-2 sentence overview]

## Key Findings
- Finding 1
- Finding 2

## Recommendations
1. Action 1
2. Action 2

Set boundaries

Clarify what the agent should NOT do:

Do not:
- Make up data or statistics
- Commit to timelines without verification
- Share sensitive internal information

Tool Configuration

Enable minimum necessary tools

Too many tools can confuse the agent and slow responses. Enable only what’s needed for the specific task.

Match tools to purpose

An agent analyzing account health doesn’t need task creation tools. An agent drafting outreach doesn’t need CRM tools.

Testing

Test with real scenarios

Use actual account data and realistic questions to validate the agent works as expected.

Test edge cases

What happens when:

Data is missing?
The question is ambiguous?
Multiple accounts match?

Monitoring and Improvement

Track Agent Performance

Response quality: Are answers accurate and helpful?
Tool usage: Is the agent using tools appropriately?
Completion rate: How often does the agent successfully complete tasks?

Iterate on Instructions

Based on feedback:

Add guidance for common failure modes
Clarify ambiguous instructions
Add examples for complex tasks

Review Tool Usage

If the agent isn’t using tools effectively:

Check that needed tools are enabled
Add explicit instructions about when to use specific tools
Simplify by removing unnecessary tools

Next Steps

Creating Agents

Step-by-step guide to creating agents

AI Components in Automations

Use agents within automation flows

​AI Agents

​What Makes Agents Different?

Traditional Automations

AI Agents

​Agent Capabilities

​Contextual Understanding

​Tool Usage

​Multi-Step Reasoning

​Agent Configuration

​Instructions (System Prompt)

​Model Selection

​Reasoning Level

​Toolkits

​Scope

​Creating an Agent

​Step 1: Define the Agent’s Purpose

​Step 2: Write Instructions

​Step 3: Select Tools

​Step 4: Configure Model Settings

​Step 5: Test Thoroughly

​Prebuilt Agents

​Account-Scoped Agents

​Global Agents

​Example Agents

​Account Health Analyst

​QBR Preparation Assistant

​Renewal Risk Monitor

​Using Agents in Automations

​Best Practices

​Instruction Writing

​Tool Configuration

​Testing

​Monitoring and Improvement

​Track Agent Performance

​Iterate on Instructions

​Review Tool Usage

​Next Steps

Creating Agents

AI Components in Automations

AI Agents

What Makes Agents Different?

Agent Capabilities

Contextual Understanding

Tool Usage

Multi-Step Reasoning

Agent Configuration

Instructions (System Prompt)

Model Selection

Reasoning Level

Toolkits

Scope

Creating an Agent

Step 1: Define the Agent’s Purpose

Step 2: Write Instructions

Step 3: Select Tools

Step 4: Configure Model Settings

Step 5: Test Thoroughly

Prebuilt Agents

Account-Scoped Agents

Global Agents

Example Agents

Account Health Analyst

QBR Preparation Assistant

Renewal Risk Monitor

Using Agents in Automations

Best Practices

Instruction Writing

Tool Configuration

Testing

Monitoring and Improvement

Track Agent Performance

Iterate on Instructions

Review Tool Usage

Next Steps