Customer Support Agents

Support and product teams use agents when resolving a request requires conversation history, approved product knowledge, live customer data, or actions in another system. Agno combines persistent sessions, specialist teams, tools, approval gates, and human escalation in one runtime.

support_team.py

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.team import Team

model = "openai:gpt-5.5"

billing = Agent(
    name="Billing Support",
    role="Answer questions about invoices, payments, subscriptions, and refunds.",
    model=model,
)

technical = Agent(
    name="Technical Support",
    role="Diagnose product errors and provide troubleshooting steps.",
    model=model,
)

support_team = Team(
    name="Customer Support",
    model=model,
    members=[billing, technical],
    db=SqliteDb(db_file="tmp/support.db"),
    add_history_to_context=True,
    num_history_runs=5,
    instructions=[
        "Route each request to the relevant specialist.",
        "Consult both specialists when an issue crosses billing and product behavior.",
    ],
)

support_team.print_response(
    "My card was charged twice after checkout returned HTTP 500.",
    user_id="customer-42",
    session_id="ticket-1842",
)

Create a virtual environment, install the OpenAI and SQLite integrations, and set OPENAI_API_KEY before running the team:

uv venv --python 3.12
uv pip install -U "agno[openai,sqlite]"
uv run python support_team.py

The team can consult both specialists for this request. Reuse the session_id for later messages on the same ticket and the user_id for the same customer. Add confirmation-required tools before permitting account changes or refunds.

Build the resolution path

Stage	Agno capability	Purpose
Receive	AgentOS API or interfaces	Accept requests from a product, Slack, WhatsApp, or another service.
Identify	Sessions and authorization	Associate the request with a customer, preserve its conversation history, and establish the caller’s identity.
Ground	Knowledge and context providers	Search approved product content and retrieve current account or order data.
Route	Teams or workflows	Select a specialist or follow a fixed triage policy.
Act	Tools and human-in-the-loop	Create tickets, update records, or request approval for sensitive work.
Improve	Learning, evaluation, and observability	Reuse resolved cases, measure quality, and inspect failures.

Choose the execution model

Requirement	Use
One support policy and one set of tools	Agent
Dynamic routing among billing, technical, and account specialists	Team
Deterministic triage, service-level rules, or escalation steps	Workflow

Teams let the model choose the relevant specialist. Workflows encode branches and required steps in code. A support system can use a workflow for intake and escalation, with a team inside a step for specialist investigation.

Set action boundaries

Action	Boundary
Answer from approved product documentation	Give the agent read-only knowledge.
Read an order, subscription, or account	Establish the caller’s identity, enforce account-level access inside the provider or tool, and keep it read-only.
Change a plan, issue a refund, or cancel an order	Require confirmation before the tool runs.
Create an engineering ticket with missing fields	Request user input before the tool runs.
Send an external message	Gate the send tool and record the action in the run history.

Serve support channels

Channel	Guide
Product UI or backend	Serve as an API
Slack	Support team
WhatsApp	WhatsApp interface
Custom clients	AgentOS client

Improve from resolved requests

Goal	Pattern
Preserve the current ticket	Reuse its `session_id` and store session history.
Remember customer preferences	Store user-scoped memory.
Reuse successful resolutions	Start with the support learning pattern. Configure a tenant-scoped namespace for every shared production store.
Monitor response quality	Run background output evaluation.
Prevent regressions	Add support conversations to an eval suite.

Next steps

Task	Guide
Route technical and general requests through fixed branches	Conditional workflow example
Collect ticket fields from a Slack requester	Human input example
Connect live customer systems	Connecting your data

Welcome

Get Started

Features

Use Cases

Customer Support Agents

Build the resolution path

Choose the execution model

Set action boundaries

Serve support channels

Improve from resolved requests

Next steps

​Build the resolution path

​Choose the execution model

​Set action boundaries

​Serve support channels

​Improve from resolved requests

​Next steps

Build the resolution path

Choose the execution model

Set action boundaries

Serve support channels

Improve from resolved requests

Next steps