# Before you launch AI support, test the handoff

Canonical page: https://aiteamrecord.com/note/test-ai-support-handoffs-before-launch/

AI Team Record editorial — 2026-10-07

Five practical checks for an AI support pilot: inspect the receiving queue, preserve context, test exceptions, and separate routing from customer resolution.

## Follow the request to the next person's first action

An AI support pilot should show what happens when the system cannot finish the work. For a small team, a failed handoff can leave a customer waiting in a queue nobody checks. Ask the implementation partner to demonstrate the complete path from the customer's request to the next person's first useful action.

This fieldnote develops the acceptance-test question in our first-call checklist, Before you hire an AI team, ask for these five things. The scenarios and acceptance rule below are AI Team Record's editorial suggestions. They are not an industry standard, incidents observed at a listed provider, or evidence that a provider has passed these tests.


## Define the destination before the demonstration

Write down which inbox or team should receive each kind of unresolved request. Include working hours, who checks the queue, and what the customer should be told outside those hours. A message promising a transfer is only one part of the path.

Intercom's documentation distinguishes the rules and guidance that trigger escalation from the workflows that route the conversation afterward. A rule requesting escalation does not itself specify the receiving teammate or inbox. That is a product-specific example; ask your partner to identify the equivalent controls in the system you are buying.

Source: [Intercom: Manage Fin AI Agent's escalation guidance and rules](https://www.intercom.com/help/en/articles/12396892-manage-fin-ai-agent-s-escalation-guidance-and-rules)

## Run five cases with your support lead present

Use synthetic records or data your team has approved for testing. Agree on the expected result before running each case. Our suggested starting set is:

1. A customer clearly asks for a person. Inspect whether the configured destination receives the conversation and whether the customer receives an accurate next-step message.

2. The question has no answer in the approved help content. Check that the system follows the agreed fallback without inventing a policy.

3. A request arrives outside staffed hours. Check that the destination remains visible to the next shift and the message does not promise an immediate human reply.

4. The connected ticket system is unavailable. Check that the failure is visible to the operator and that the customer is not falsely told a ticket was created.

5. A customer returns to an unresolved conversation. Check that the next person can find the prior context and unfinished work. After a human takes over, send another test message to check that it does not unexpectedly route back to AI.

Run these in a test environment first. Where an internal live test is necessary, agree on cost and audience restrictions before enabling it. Intercom's deployment guide describes workflow previews, recommends an Else fallback path, and explains that a later customer message can trigger a workflow that re-engages Fin after escalation. Its preview also has limitations, including notification behavior. Ask which parts of your receiving system and alerting path need a separate check.

Source: [Intercom: Deploy Fin AI Agent over chat](https://www.intercom.com/help/en/articles/8286630-deploy-fin-ai-agent-over-chat)

## Keep the conversation and inspect the resulting record

For each case, keep an approved, access-controlled record of the input, expected destination, customer-visible reply, resulting ticket or conversation ID, actual destination, test time and reviewer. Avoid copying private customer details into a public project report.

Open the receiving system during the demonstration. Have the support lead check whether the record exists, whether it reached the right queue, and whether the handoff preserves the issue, attempted steps and unresolved question. Anthropic's evaluation guidance distinguishes an agent's stated success from the final state it leaves in the environment. It also describes how results can vary between runs. For this pilot, repeat the cases and inspect their actual outcomes rather than relying on one successful transcript.

Our proposed acceptance rule is to treat a missing handoff, a misleading completion message or an unowned queue as a release blocker for that route. Agree on other thresholds with the team responsible for operating it. Passing five examples is a starting check, not proof of reliability across every conversation.

Source: [Anthropic: Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)

## Separate routing success from customer resolution

Ask for separate counts of required handoffs, successful routing, cases awaiting a person, and cases the customer confirms are resolved. Agree on the observation period and how to handle later replies. Do not automatically call a quiet customer a satisfied customer.

Intercom distinguishes confirmed resolutions from assumed resolutions, where the customer leaves without requesting more assistance after an answer. Its documentation says a later request for further help in the same conversation can reverse a counted resolution. It also defines procedure handoffs separately. These definitions do not establish how another vendor counts success. Ask for the exact definition behind every dashboard figure.

Source: [Intercom: Fin AI Agent outcomes](https://www.intercom.com/help/en/articles/8205718-fin-ai-agent-outcomes)

## Leave the pilot with an operating decision

Finish with the failed cases, the person responsible for each fix, the agreed retest and the person authorized to pause the route. Ask which changes require the test to run again: a new support policy, destination, integration or model can affect the path you just checked.

Use the pilot record to decide whether to launch the tested route, narrow its scope or continue testing. When researching a provider, inspect the scope and sources behind its claims. A directory match is a starting point for that research; it does not establish that a team can deliver your particular handoff.


## Sources and editorial approach

This is AI-assisted editorial guidance reviewed for this directory. It does not describe a project we delivered or endorse a particular provider.

- [Intercom: Manage Fin AI Agent's escalation guidance and rules](https://www.intercom.com/help/en/articles/12396892-manage-fin-ai-agent-s-escalation-guidance-and-rules) — checked 2026-10-07
- [Intercom: Deploy Fin AI Agent over chat](https://www.intercom.com/help/en/articles/8286630-deploy-fin-ai-agent-over-chat) — checked 2026-10-07
- [Anthropic: Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents) — checked 2026-10-07
- [Intercom: Fin AI Agent outcomes](https://www.intercom.com/help/en/articles/8205718-fin-ai-agent-outcomes) — checked 2026-10-07