Two Ways to Build Agents
Both approaches run on xpander’s infrastructure - either serverless cloud or your VPC with AgentOS (xpander’s K8s environment). The key difference: How will users interact with your agent?☁️ Managed Agents
The agent IS your productVisual setup, instant deployment. Users interact directly - no frontend needed
Perfect when you need: • IT helpdesk that knows your company wiki • Sales bot that qualifies leads 24/7 • DevOps assistant with AWS/GCP access • HR bot handling employee FAQs
Perfect when you need: • IT helpdesk that knows your company wiki • Sales bot that qualifies leads 24/7 • DevOps assistant with AWS/GCP access • HR bot handling employee FAQs
🔧 Embedded Agents
The agent powers YOUR productYour app, your UI. xpander handles the AI heavy lifting
Perfect when building: • SaaS that needs AI copilot features • Trading platform with custom analysis • Healthcare app with compliance rules • Enterprise system with legacy APIs
Perfect when building: • SaaS that needs AI copilot features • Trading platform with custom analysis • Healthcare app with compliance rules • Enterprise system with legacy APIs
☁️ Managed Agents
Your complete AI agent, ready to use. Configure everything visually in the Workbench:- ✅ Select AI models (pre-configured or bring your own keys)
- ✅ Add tools and MCP servers from the repository
- ✅ Set up memory, knowledge bases, and instructions
- ✅ Connect directly to Slack, webhooks, or web chat
- ✅ Built on Agno-AGI framework (additional frameworks on roadmap)
What you get: A fully functional AI agent that users interact with directly. No frontend to build, no infrastructure to manage - just configure and interact with the agent via Slack, Webhook, WebUI or scheduled tasks.

Real-world managed agent examples
IT Support Bot
Deploy to Slack to answer employee questions, reset passwords, check system status
Customer Service Agent
Handle support tickets via web chat, search docs, escalate complex issues
Sales Assistant
Qualify leads through webhooks, update CRM, schedule follow-ups
DevOps Helper
Monitor deployments, run diagnostics, alert on issues via scheduled tasks
Knowledge Base Q&A
Answer questions from your docs, accessible via web UI or Slack
Onboarding Assistant
Guide new employees through processes, answer FAQs, collect information
🔧 Embedded Agents
Build your own product with AI capabilities. Use xpander as the AI backend for your application.- ✅ Integrate with any Python agent framework (LangChain, CrewAI, AutoGen)
- ✅ Add custom business logic and tools with
@register_tool
- ✅ Control your own frontend and user experience
- ✅ Deploy to xpander’s infrastructure or your K8s cluster
- ✅ Framework agnostic - works with any Python application
What you get: An AI backend for YOUR application. You build the UI, xpander handles the AI processing, memory, and scaling.
Real-world embedded agent examples
SaaS Platform
Your web app’s frontend sends user requests to xpander agents for AI processing
Mobile App
iOS/Android app uses xpander SDK to add conversational AI features
Enterprise Portal
Custom internal system with xpander handling the AI logic
E-commerce Site
Your checkout flow uses embedded agents for product recommendations
Healthcare App
Patient portal with HIPAA-compliant AI assistant powered by xpander
Educational Platform
Learning app where xpander agents provide tutoring and feedback
How to build embedded agents
1. Test your agent locally:local_test.py
your_app.py
xpander_handler.py
Without the
@on_task
handler: Tasks created via SDK are processed by managed agents automatically.
With the @on_task
handler: Your custom container code processes the tasks. Run xpander deploy
to deploy your container.Both agent types can be deployed to:- Serverless cloud: xpander’s managed infrastructure (default)
- Your VPC: Install AgentOS in your K8s cluster for complete data control
Dive Deeper
xpander is built for production-grade AI agent development. Learn more about the platform capabilities:Quick Start
Get started with your first agent in 5 steps
AI Model Configuration
Configure and manage AI models for your agents
System Prompts
Define your agent’s personality and behavior
Tools & Connectors
Integrate with external services and APIs
Agent Memory
Configure persistent memory and conversation history
Slack Integration
Deploy agents to Slack workspaces
Web Interface
Create professional web interfaces
MCP Integration
Connect MCP clients like Claude Desktop
Webhooks
Trigger agents from external systems
API Reference
Complete SDK and API documentation