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Customer Support AI Example

Customer support is one of the fastest ways to validate whether AxonFlow can govern a real application instead of just a demo prompt. The workflow usually combines three things:

  • governed LLM summarization and response drafting
  • governed access to customer, ticket, and order data
  • auditability for sensitive support operations

This page shows the public/community architecture pattern that teams can implement today, and where evaluation or enterprise features become useful as the support operation grows.

What You Can Build in Community​

With the public/community stack you can already build a support copilot that:

  • drafts responses for agents through Proxy Mode
  • checks ticket or customer records through MCP connectors such as PostgreSQL or HTTP
  • redacts sensitive data in governed responses
  • records governed activity in the audit trail
  • enforces system and tenant policies around SQL injection, PII handling, and dangerous requests

That is enough for internal support copilots, tier-1 agent assist flows, and early production validation.

Reference Architecture​

Support UI -> Backend -> AxonFlow Agent -> Policy evaluation -> LLM provider
                                   \-> MCP connector query -> Ticket or customer system

A typical request path looks like this:

  1. the support app sends a draft-response request through the Agent
  2. AxonFlow evaluates system and tenant policies
  3. the app optionally retrieves ticket or customer data through an MCP connector
  4. the final response is returned with policy metadata and audit coverage

Example: Draft a Support Response​

TypeScript​

import { AxonFlow } from '@axonflow/sdk';

const client = new AxonFlow({
  endpoint: process.env.AXONFLOW_ENDPOINT!,
  clientId: process.env.AXONFLOW_CLIENT_ID!,
  clientSecret: process.env.AXONFLOW_CLIENT_SECRET,
});

const response = await client.proxyLLMCall({
  userToken: 'agent-42',
  query: 'Draft a reply for a premium customer whose refund is delayed by 3 days.',
  requestType: 'chat',
  context: {
    provider: 'openai',
    model: 'gpt-4o',
    queue: 'billing',
    customer_tier: 'premium',
  },
});

if (response.blocked) {
  console.log('Blocked:', response.blockReason);
} else {
  console.log(response.data);
}

Example: Retrieve Ticket Data Through MCP​

Python​

from axonflow import AxonFlow

with AxonFlow.sync(
    endpoint="http://localhost:8080",
    client_id="support-app",
    client_secret="secret",
) as client:
    result = client.mcp_query(
        "postgres",
        "SELECT id, subject, priority, customer_email FROM tickets WHERE status = 'open' LIMIT 10",
    )

    print(result.redacted)
    print(result.policy_info)
    print(result.data)

This pattern is especially useful when support agents need governed access to customer records, order history, or operational systems.

Why This Example Matters​

Support workflows put multiple AxonFlow capabilities in one place:

  • request governance for agent-facing prompts
  • MCP governance for ticket and customer systems
  • policy-backed redaction for emails, phone numbers, and other PII
  • clear audit evidence for customer-data access

That combination is often the first serious proof point for platform teams assessing whether AxonFlow can support a production AI assistant.

When Evaluation or Enterprise Becomes Relevant​

Community is a good fit for early support copilots and single-team deployments. Evaluation and enterprise become more compelling when you need:

  • approval workflows for high-risk support actions
  • organization-tier policy management across many support teams
  • protected dashboards for approval queues, execution history, and operations
  • deeper connector and identity-management requirements
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