Turn support SOPs into a tested AI agent

Ecommerce CX teams running AI agents on Shopify stores spend weeks configuring automations intent by intent. Policies keep changing (return windows, shipping cutoffs, peak-season promos), the configuration drifts out of sync, and off-script or multi-request tickets escalate to humans. Maintaining the automation turns into a second job, and BFCM exposes every gap at the worst possible moment.

1. Gather your current support SOPs, macros, return and shipping policies, plus a sample of a few hundred recent tickets from your helpdesk.
2. Connect Yuma to your Shopify store and helpdesk (Gorgias, Zendesk or similar) so the agent can read orders, tracking and customer history, and take backend actions like cancellations, refunds and address edits.
3. Ask Yuma to draft a Handbook from those SOPs and past tickets: one readable document covering brand voice, policies and a playbook per topic. Running several stores or brands? Give each brand its own voice section inside the same Handbook.
4. Connect Claude to Yuma through MCP and run the Handbook audit prompts (https://docs.google.com/document/d/1IWfp81133kSHNId_bCuj_DmTiZw4JXsrMA7x1FdkRZU/edit?usp=sharing) to check the Handbook against your policy docs. Ask it to flag contradictions, missing edge cases and outdated rules, then propose edits. Review every edit before accepting it.
5. Write tests for each playbook using real tickets and the reply and action you expect. Run them, fix the Handbook wherever a test fails, and rerun until they pass.
6. Publish the Handbook. Nothing goes live until a human publishes it. Roll out on a subset of topics or ticket volume first and read transcripts daily during the first week.
7. Extend the same Handbook to outbound playbooks triggered by order events (shipping delays, delivery issues, abandoned checkouts over email and SMS) and to phone calls where Voice is enabled.
8. When a policy changes, edit the relevant Handbook section, rerun the tests and publish. The agent picks up the change on the next ticket with no reconfiguration.

Maintaining automation becomes maintaining documentation instead of decision trees. Yuma merchants Tediber and Petlibro reach automation rates of 64% and 79% respectively. One versioned Handbook governs inbound email and chat, outbound messages and phone, so a policy change reaches every channel on the next ticket, and per-playbook tests catch regressions before customers ever see them. Setup that used to take weeks of intent-by-intent configuration compresses into drafting, testing and approving a single document.

Run steps 1 to 6 on your own tickets with a 30-day free Yuma trial (https://yuma.ai/support-ai) before committing to a rollout. Copy the audit and testing prompts into a Claude project or skill so the whole team can reuse them. State each policy once in the Handbook and reference it from playbooks, so a single edit never leaves a stale copy behind. Add a test for every new edge case before publishing the fix. Let Claude or ChatGPT read and propose Handbook changes over MCP, but keep human approval on every publish. Include order-status and tracking lookups in your tests, since most where-is-my-order tickets depend on live Shopify data. Before November, replay last year's BFCM peak-week tickets through your tests as a stress rehearsal.

Reply quality depends on Handbook quality: vague or contradictory policies produce vague answers. Backend actions require the right Shopify and helpdesk permissions. Tickets outside documented policy still route to human agents by design. Automation rates vary with catalog complexity, ticket mix and how thoroughly policies are documented. Voice and some outbound channels may depend on plan and rollout availability.