What are AI Agents?

AI agents are specialized AI assistants with persistent instructions and dedicated tool access that deliver consistent, reliable results.

Agents are like AI team members with specific roles and expertise. They remember their job across all conversations.

How Agents Differ from Regular Chat

Getting Started
Advanced Knowledge

Regular chat starts fresh each time. Agents have persistent instructions, roles, and tools that apply automatically to every conversation.Key advantages:

Persistent Instructions

Give agents permanent guidelines, roles, and expertise that apply to every conversation automatically.

Consistent Behavior

Agents follow the same approach every time, delivering predictable, reliable results across all interactions.

Dedicated Tools

Equip agents with specific external connections (MCP) they’ll use automatically when needed for their tasks.

Specialized Roles

Define agent personalities, tone, and expertise to match specific workflows or team functions.

Configure once, use everywhere. Agents maintain their role and instructions across all conversations.

When to Use Agents

Use agents for:

Recurring workflows: Tasks you perform regularly with consistent requirements
Specialized expertise: Domain-specific knowledge applied consistently
Cross-tool workflows: Complex tasks spanning multiple systems
Team standardization: Shared workflows across your organization

Agent Examples

Practical agents you can create for common business workflows:

Email Assistant

Automatically review your last unread emails, summarize key information, flag urgent messages, and draft responses based on your communication style.Tools needed: Email (Outlook, Gmail)
Complexity: Simple

Meeting Scheduler

Find available time slots across multiple calendars, schedule meetings with participants, send invitations, and create meeting agendas based on meeting context.Tools needed: Calendar, Email
Complexity: Simple

Customer Support Agent

Handle customer inquiries by accessing your CRM, checking order status, processing refund requests within defined limits, and escalating complex issues to human agents.Tools needed: CRM, Order Management, Email
Complexity: Moderate

Data Analysis Agent

Query databases for sales data, perform cohort analysis, identify trends, and generate reports with visualizations based on your reporting standards.Tools needed: Database, Spreadsheet, Visualization tools
Complexity: Moderate

Lead Qualification Agent

Review new leads from multiple sources, enrich data from public sources, score based on your criteria, assign to appropriate sales team members, and send personalized outreach.Tools needed: CRM, Email, Data enrichment
Complexity: Complex

Cross-System Workflow Agent

Orchestrate processes across multiple systems. For example, retrieve information from Outlook, create records in Odoo, update spreadsheets, and notify teams via messaging platforms.Tools needed: Multiple system integrations
Complexity: Complex

Start with simple, single-purpose agents and expand to more complex workflows as you gain experience.

Getting Started with Agents

Finding and Accessing Agents

Learn where agents are located and how to access them in WonkaChat.

Using Existing Agents

Start using pre-built agents created by your organization.

Creating Your First Agent

Build your own agent tailored to your specific workflows.

Business Teaching

Provide business context to agents for better, more accurate results.

Agent Architecture

Agents are built on several key components that work together to create specialized AI assistants:

1. Agent Name

A clear, descriptive identifier that indicates the agent’s purpose and makes it easy to find and select when starting conversations.

2. System Prompt (Instructions)

The system prompt is the foundational set of instructions that defines your agent’s identity and behavior. Unlike regular conversation messages that can be modified or ignored, the system prompt is persistent and authoritative. This prompt shapes every response the agent generates.System prompts include:

Role definition and expertise areas
Communication tone and style
Operational rules and constraints
Business context and policies
Decision-making frameworks
Error handling procedures

The quality of your system prompt directly impacts agent effectiveness. Well-crafted instructions produce consistent, reliable results; vague instructions lead to unpredictable behavior.

3. Model Selection

The AI model is an important choice that significantly affects both performance and cost. Different models have different capabilities, speed, and pricing.Key considerations:

Performance: More capable models (thinking models) excel at complex reasoning but are more expensive
Cost: Simpler models are cost-effective for straightforward tasks
Speed: Faster models reduce latency for high-frequency interactions
Context window: Determines how much information the agent can process at once

Model selection impacts your agent’s quality and operational costs. Match model capability to task complexity: don’t overspend on simple tasks or underinvest in complex workflows.

In addition to the model choice, you can adjust model parameters that control response randomness, creativity, and repetition. See Creating Your First Agent for detailed parameter configuration.

4. Capabilities

Agents can be equipped with various capabilities beyond text generation:Code Execution: Run code snippets to perform calculations, data transformations, or complex logic operations.Web Search: Access current information from the internet when needed for real-time data or recent events.External Connections (MCP): The most important capability, direct integration with your business systems through the Model Context Protocol.Agents dynamically select which tools to use based on the task and their system prompt guidance.

MCP connects agents to your business systems for real actions.
See Tools Connection for setup details.

Execution Loop

Agents operate autonomously through a continuous loop, iterating until they achieve the desired result:

Agents iterate through multiple approaches and tool combinations until they successfully complete the task or reach defined limits.

Model Selection

WonkaChat supports multiple AI providers, allowing you to choose any model that best fits your needs. Model selection depends on several factors:

Task complexity: More complex reasoning requires more capable models
Response speed: Some models prioritize faster responses
Cost considerations: Model pricing varies significantly across providers

For detailed guidance on selecting the right model for your agent, see Creating Your First Agent.

Security and Access Control

Permission Inheritance

Agents operate within your existing security boundaries. They cannot perform any action you couldn’t perform yourself without WonkaChat. If you lack permission to access a system, read a file, or execute an operation manually, the agent cannot bypass those restrictions for your.

See Security & Governance for comprehensive security details.

Welcome to Wonka Chat

AI Agents

Tools & Connection

Improved Usage

Security & Governance

Support & Resources

Legal

How Agents Differ from Regular Chat

Persistent Instructions

Consistent Behavior

Dedicated Tools

Specialized Roles

When to Use Agents

Agent Examples

Getting Started with Agents

Finding and Accessing Agents

Using Existing Agents

Creating Your First Agent

Business Teaching

Agent Architecture

Execution Loop

Model Selection

Security and Access Control

Permission Inheritance

Welcome to Wonka Chat

AI Agents

Tools & Connection

Improved Usage

Security & Governance

Support & Resources

Legal

​How Agents Differ from Regular Chat

Persistent Instructions

Consistent Behavior

Dedicated Tools

Specialized Roles

​When to Use Agents

​Agent Examples

​Getting Started with Agents

Finding and Accessing Agents

Using Existing Agents

Creating Your First Agent

Business Teaching

​Agent Architecture

​Execution Loop

​Model Selection

​Security and Access Control

​Permission Inheritance

How Agents Differ from Regular Chat

When to Use Agents

Agent Examples

Getting Started with Agents

Agent Architecture

Execution Loop

Model Selection

Security and Access Control

Permission Inheritance