Skip to content

How to Build a Multi-Agent Customer Onboarding Workflow in Python

New-customer onboarding spans identity checks, account configuration, first-questions, and a warm hand-off to success. This recipe uses the Role-Based pattern — inspired by AutoGen's group-chat onboarding example — so each specialist holds its role and reacts to the others, producing a complete onboarding plan in one coordinated pass.

Patterns used: Role-Based

Architecture

flowchart LR
    C[New Customer] --> R((Onboarding Room))
    R --> V[Verification\nidentity + KYC]
    R --> S[Account Setup\nplan + config]
    R --> F[FAQ\nfirst questions]
    R --> M[Success\nhandoff + next steps]
    V --> S --> F --> M
    R --> O[Onboarding Summary]

Implementation

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.structural import RoleBased
from pyagent_providers import AnthropicLLM, OpenAILLM

onboarding = RoleBased(
    agents=[
        Agent(
            "verification",
            OpenAILLM("gpt-4o-mini"),
            system_prompt=(
                "You handle identity verification. Confirm what's needed (email, org, KYC tier) "
                "and list any missing items. Never request more data than the plan requires."
            ),
        ),
        Agent(
            "account_setup",
            OpenAILLM("gpt-4o-mini"),
            system_prompt=(
                "You configure the account: plan, seats, integrations, and sensible defaults. "
                "Produce a concrete setup checklist."
            ),
        ),
        Agent(
            "faq",
            AnthropicLLM("claude-haiku-3-5-20241022"),
            system_prompt=(
                "You answer the customer's first questions clearly and link to the right docs. "
                "Anticipate the top-3 questions for this plan."
            ),
        ),
        Agent(
            "success",
            AnthropicLLM("claude-sonnet-4-20250514"),
            system_prompt=(
                "You are the success manager. Summarize the onboarding, define the 30-day "
                "activation goals, and write a warm hand-off note for the human CSM."
            ),
        ),
    ],
    rounds=1,
)

result = asyncio.run(onboarding.run(
    "New customer: a 25-person design agency on the Team plan, wants Slack + SSO, "
    "main goal is client project tracking."
))
print(result.output)
print(f"Roles: {result.metadata['roles']}")

Expected output

ONBOARDING SUMMARY — Northwind Design (Team plan, 25 seats)

Verification: email + org confirmed; SSO domain pending IT approval.
Setup:        25 seats provisioned, Slack connected, project-tracking template applied.
FAQ:          answered "how do invites work / SSO setup / mobile app".
Success:      30-day goal = 10 active projects; hand-off note sent to CSM Dana.

Roles: ['verification', 'account_setup', 'faq', 'success']

For ticket routing after onboarding, pair this with the Support Router.

Customization

Add a compliance role

onboarding.agents.append(
    Agent("compliance", OpenAILLM("gpt-4o-mini"),
          system_prompt="Confirm KYC tier, data-residency, and DPA requirements for this plan."),
)

Automate account creation with tools

Give the setup role a ReAct tool that calls your provisioning API.

Localize the welcome

onboarding.agents[2].system_prompt += " Write the FAQ answers in the customer's locale."

When to Use

Situation Use Role-Based?
A fixed set of specialists must collaborate on one customer ✅ Yes
You want a single coordinated onboarding artefact ✅ Yes
You're routing an inbound ticket to one specialist ❌ Use Supervisor
Each step strictly gates the next with approvals ❌ Use Pipeline

Cost Profile

Driver Typical model Avg cost Volume (1k onboardings/mo)
4 roles × 1 round mini + haiku + sonnet $0.006 $6/mo… ×1k = $6 → ~$180/mo
Per onboarding mix ~$0.006 ~$180/mo

See Also