The first thing I check is which team the agent will live inside. A support agent needs your help docs, your refund policy, and a clean handoff to a human. A sales agent needs lead capture and a way to book a meeting. A product guidance agent needs your catalog and a tone that sounds like your brand. A single agent that does all three usually does none of them well, which is why I take what an AI agent on a website actually does seriously before I pick a vendor.
The job types sit in different buckets, and the agent I pick depends on which bucket is the heaviest:
Support: damaged orders, returns, subscription changes, account lookups. Needs identity checks and a clean route to a human for anything touching money.
Sales: pricing questions, demo requests, lead capture. Needs CRM push and meeting booking, not just an FAQ.
Product guidance: sizing, compatibility, comparisons. Needs your catalog and policies as the only source.
I look for three things in any vendor before I wire one up. First, where the agent's answers come from - your catalog, FAQ, and policy pages, not the open internet. Second, whether it can take a real action in my stack (Calendly, Slack, Stripe, Zendesk) rather than just send text. Third, how it behaves when it does not know: does it admit it, hand off, or guess and move on.
I usually start with the bucket that hurts most when it goes unanswered, then expand once the agent is holding its own there.
Training it on your catalog, policies, and brand voice
The first version of an agent I trained pulled from the entire help center, every policy page, and the full product catalog. It also pulled in three years of internal meeting notes someone had indexed by accident. It answered a pricing question with a line from a slide deck about a plan we had retired. That was the day I learned the garbage-in rule the hard way: the agent is only as on-brand as the documents you let it read.
A clean training set looks like this:
Catalog: product names, variants, sizing charts, compatibility notes. No internal SKUs, no supplier codes.
Policies: shipping, returns, refunds, warranty. The exact pages a customer would see, kept current.
Brand voice: a short style sheet with tone words, words to avoid, and a few example replies that sound like the team.
Past tickets: redacted of names, emails, and order numbers, then sorted by intent so the agent can pattern-match real questions.
What stays out matters as much. Refund authority, account changes, anything that touches PII or money should not be answered from a static page; it should be gated behind an identity check and routed to a human with the ticket context already attached. e-commerce customer support
The morning after I turned the agent on, I sat with the support inbox and watched ten real conversations. Eight were clean. Two were not - one hallucinated a return window we had retired in 2023, and one offered a discount code that did not exist. I pulled the agent back into staging, fixed the two failure modes, and pushed again the next morning. That loop is the part nobody warns you about: launch day is a Tuesday, not a launch.
A short pre-launch checklist kept me from rebuilding the same fix every Friday:
Scope: which intents the agent owns on day one, which it hands off, and which it does not see at all.
Identity gate: any flow touching money, account changes, or PII routes through verification before the agent responds.
Fallback path: a clear handoff to a human with the ticket context already attached, not a dead-end contact-support link.
Tone pass: three sample replies read aloud to make sure the voice still sounds like the team.
AI Replies or Human Takeover outbound campaign features
The first week after putting an AI agent on your website is mostly review. I read every transcript, tag the misses by reason (wrong source, missing policy, bad handoff, tone drift), and ship one fix a day. The volume of misses drops fast. By the second week, the inbox feels lighter and the human team stops dreading the 9 a.m. queue.
What I do not do is call it done. Every time a policy changes, a product retires, or a new SKU lands, the training set gets a small update and the agent gets re-run on the last week's hard questions. The agent that ships today is not the agent I want running next quarter, and treating going live as a one-time event is how the plot gets lost.
