Customer-Facing Agents

Build one agent for your product and serve it inside your product, through Claude and ChatGPT, and in messaging interfaces.

Customer-facing agents let customers ask questions, work with their data, and take action through your product. Build one agent with tools for product actions and instructions that encode your product’s rules and policies. AgentOS can serve that agent inside your product, through Claude and ChatGPT as an MCP connector, and in Slack or other messaging interfaces.

customer_agent.py

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

agent = Agent(
    name="Customer-Facing Agent",
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    add_history_to_context=True,
    num_history_runs=5,
    enable_agentic_memory=True,
)

agent_os = AgentOS(agents=[agent], db=db)
app = agent_os.get_app()

if __name__ == "__main__":
    agent_os.serve(app="customer_agent:app", port=7777)

This configuration provides an HTTP API, persistent multi-turn history, and per-user memory. Authentication is disabled until you enable it. See Serve as an API for JWT authorization and opt-in per-user isolation.

Serve one agent across surfaces

The agent runs in your AgentOS as a service. Product interfaces, MCP clients, messaging interfaces, and backend jobs connect to the same agent.

Surface	How it reaches the agent
In-product chat	POST /agents/{agent_id}/runs with user_id and a per-thread session_id
Claude and ChatGPT	With MCP configured, AgentOS MCP Server publishes the agent as a natural-language tool at /mcp
Inline action (“summarize this”, “draft a reply”)	The same endpoint, a short-lived session_id
Slack / Telegram / WhatsApp	An interface adapter maps the channel onto runs and sessions
Backend event (webhook, cron)	A custom FastAPI route calling agent.arun(...)

Stored memory can follow a user when surfaces call the same agent with the same user_id and database. Session history continues only when they also reuse the same session_id. Messaging interfaces usually create surface-specific session IDs.

Choose a path

You have	You want	Start with
A SaaS product with customer accounts	JWT authentication and opt-in per-user isolation	Serve as an API
Users in Claude or ChatGPT	Your agent published as an MCP tool	AgentOS MCP Server
Users in Slack or the browser	The same agent where they already work	Interfaces
An indexed document corpus	Answers grounded in retrieved content	Knowledge agents
Live systems such as Slack, Drive, or databases	Current records queried at run time	Connecting your data
Multi-day user relationships	Memory that persists per user across surfaces	Sessions and memory

Explore

Serve as an API

Turn the agent into an HTTP service with streaming, sessions, and auth.

Sessions and memory

Store multi-turn history and per-user memory in the configured database.

Connecting your data

Give the agent access to Slack, Drive, databases, and MCP servers.

Interfaces

Connect Slack, Telegram, WhatsApp, and browser clients through interface adapters.

Developer Resources

• Runtime overview
• Sessions
• Memory
• Scout template