“Chatbot” describes a conversational interface. “AI agent” usually emphasizes the ability to choose steps and use tools. The terms overlap, so they are a poor shortcut for deciding whether a product can resolve your store's requests. Compare the actual behavior: what it knows, what it can do and how it handles an exception.
Three capabilities to distinguish
- Scripted conversation: follows defined choices or rules. This can work well for predictable intake and routing.
- AI answers: interprets natural-language questions and generates replies using available knowledge. Fluent wording does not prove the answer is correct.
- Connected actions: retrieves or changes information through tools, subject to permissions and business rules. This is the capability to test when you need more than an explanation.
A chatbot can use AI and connected tools. An agent can still follow a tightly controlled workflow. Neither label guarantees memory, continuous learning, integration coverage or a particular resolution rate.
Compare the same customer request
Consider: “Can I cancel the blue shirt but keep the rest of my order?” A useful evaluation asks each provider to work through the same scenario.
- Identify the correct customer and order.
- Distinguish a partial cancellation from cancelling everything.
- Check the order's fulfillment state and store policy.
- Perform only the permitted action, or explain why review is needed.
- Confirm the result from the order system before telling the customer it is done.
An FAQ answer may correctly explain the cancellation policy without completing the cancellation. That can still be useful, but it should be measured as an answer or handoff rather than a completed action.
Test the difficult variations
Use examples from your real support categories, with synthetic customer details for evaluation. Include unclear wording, contradictory product information and a request outside policy. Then test an unavailable integration and an action that fails halfway through.
Check whether the system asks a relevant question, repeats an action, invents a result or sends a useful escalation. For account-specific requests, include the data-access checks needed to prevent the wrong record from being shown.
Evaluate knowledge maintenance
Ask where answers come from, how changes become available and how you can correct a wrong answer. A model does not necessarily learn from every conversation. A store policy update should have an identifiable source, owner and publishing process.
Try changing a test policy and checking the answer after the documented update process. Also ask what happens when two sources disagree. A confident answer from an old document is a knowledge-management problem regardless of how sophisticated the conversation sounds.
Measure resolved work and operating effort
Record correct answers, successful actions, appropriate escalations and repeat contacts separately. Compare similar issue types rather than giving one system easy FAQs and the other difficult complaints. Our AI versus live-chat KPI guide explains how to set up that comparison. Align the definitions with your customer support KPIs and use first-contact resolution exercises to inspect the cases behind the numbers.
Include the work your team must do: preparing knowledge, configuring integrations, reviewing exceptions and maintaining policies. Costs can include usage, subscriptions and human follow-up. There is no universal automation percentage or ROI that applies to every store.
Choose the scope you can operate well
A clear intake flow may be enough for a small, predictable queue. Connected actions matter when recurring requests require work in an order or CRM system. Start with the workflows you can verify and expand after reviewing actual outcomes.
If you are evaluating Chad, bring representative requests and the systems involved. Confirm which actions are supported for your setup and how exceptions reach your team. The useful comparison is whether the customer gets the right result with less repeated work, not which product has the more ambitious label.
