Teams use xpander.ai to build Agents as products. That means they don’t just create one-off automations or single-use scripts. Instead, they use xpander to build AI Agents that are:
  • Reusable – packaged with functionality that can be leveraged across use cases and teams.
  • Deployed – running reliably in production, not just in notebooks.
  • Integrated – accessible via APIs, MCP and A2A, Slack, and more.
  • Maintained – versioned, monitored, and continuously improved like any software product.
  • Scoped for others – usable across teams or externally, with clear access and governance in place.
Some examples include:

Simple Tool Calling Agents

Agents with tools and instructions

Knowledge Agents

Agents with knowledge bases and storage

Reasoning Agents

Agents with memory and reasoning capabilities

Autonomous Agents

Agents that don’t require user interactions and powered by scheduled tasks and events

Multi-Agent Systems

Complex multi-agent systems that work together on achieving tasks

AI Employees

Production-grade agents designed to behave like software-powered teammates and can include all of the above

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Code Examples

from agno.agent import Agent
from agno.models.openai import OpenAIChat

agno_agent = Agent(model=OpenAIChat(api_key="your-openai-key"))
agno_agent.print_response(message="What's your role?")
agno_agent.print_response(message="What did I ask you?")

$ python without_backend.py

User: What's your role?

Agent: I serve as an AI developed by OpenAI, designed to assist with a 
wide range of inquiries and tasks. My role involves providing information, 
answering questions, generating creative content, assisting with 
problem-solving, and offering general guidance across various topics.

User: What did I ask you?

Agent: I'm sorry, but I don't have the ability to recall past 
interactions or questions you might have asked. Can you please 
remind me or ask your question again?

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What the Backend Provides

The Backend automatically provides memory, custom instructions, model settings, tools, and storage - all configured through xpander.ai’s platform. All agents in xpander.ai have a complete agent lifecycle with CI/CD pipelines, event triggers that can be configured with Slack, WebUI, MCP, and webhooks - all without infrastructure setup and available out of the box. The Workbench allows you to customize the agent backend and test the agent in real-time.

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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