Build an Agent for Checkout Knowledge Questions

Outcome

By the end of this tutorial, you will know how to use SophMate for WordPress AI agent while keeping the work reviewable inside WordPress.

Scenario

A store wants a checkout assistant that can answer shipping and return questions from policy sources without seeing account credentials.

What the image shows

The tutorial image shows Agents because agent quality depends on prompt scope, tools, triggers, runs, evals, memory, and trace review.

Before you begin

• Confirm SophMate is active and the relevant module is available to your user role.
• Check provider, budget, and approval settings before asking SophMate to draft or execute work.
• Keep customer data, API keys, and private credentials out of prompts unless the workflow is explicitly designed to handle that context.

Guardrail

Keep agent scope narrow, bind only necessary tools and sources, and review run traces before publishing broadly.

Common mistakes to avoid

• Giving the first agent too many tools, sources, and goals.
• Publishing without eval cases for common questions and edge cases.
• Failing to review traces after real users interact with the agent.

Step 1: Create the agent draft

Open SophMate > Agents and create a new agent with a clear name, purpose, and expected audience. Keep the first version narrow.

Step 2: Bind Knowledge Base content

Select the shipping, return, and payment FAQ items the agent may use. Do not give it broad access when a focused policy set is enough.

Step 3: Write strict instructions

Tell the agent not to invent shipping, tax, coupon, payment, or availability details. It should cite sources and ask for support handoff when uncertain.

Step 4: Add eval cases

Create examples for common questions and edge cases. The agent should pass policy, tone, and refusal expectations before publication.

Step 5: Publish and monitor runs

After publishing, review run traces, failed cases, and customer handoff events before expanding the agent to more surfaces.

Review checklist

• The agent has a narrow scope.
• Evals cover edge cases.
• Run traces are reviewed after launch.

Success signal

The agent workflow is successful when evals pass, traces show the agent staying within scope, and tool or Knowledge Base use is easy to review after real runs.

What to document

Document agent purpose, allowed sources, allowed tools, refusal rules, eval cases, launch surface, and run trace review cadence.

Owner and cadence

The agent owner should review evals before launch and run traces after launch, especially when tools or customer-facing answers are involved.

Escalate when

Escalate when an agent needs broader tools, handles customer-impacting answers, fails evals, or produces traces that are hard to explain.

Related SophMate paths

• Features/Agents
• Features/Knowledge Base

Next action

Run this workflow on a low-risk example first. Once the result is easy to review and explain, decide whether it should become a repeatable playbook, workflow, watcher, agent, or documented team process.