Pip: There is a version of launching a business tool where you skip the testing, assume it works, and find out otherwise in front of a live customer. BD&C has been covering the smarter version of that story.

Mara: Iryna Tymchenko has two posts out that fit together neatly — one on getting an AI agent ready before it goes live, and one on what happens when your website alone is not enough to answer visitor questions.

Pip: Let's start with what it actually takes to prepare an AI agent for launch.

Preparing AI Agents For Launch

Mara: The core question here is simple: should you test your website AI agent before visitors meet it, and how much work does that actually take?

Pip: The post answers that with a hiring analogy worth quoting directly: "Picture hiring a new receptionist and putting them on the front desk without a single practice question."

Mara: The upshot is that an agent answers from whatever information it was given — outdated pages, missing details, and the result is a confident-sounding wrong answer in front of a real customer.

Pip: The checklist is genuinely low-friction: preview first, run your ten most common questions, try the awkward ones with typos and double questions, then share a test page with a small group before anything goes live.

Mara: When an answer is off, the fix starts with the source content, not the agent. That connects to the post's broader point about over-automating too quickly — a theme covered in earlier writing on what 2025 taught about moving too fast.

Pip: Good content in, good answers out. Which raises the question of what happens when the content you need is not on your website at all.

Training Chatbots On Private Documents

Mara: The tension in this segment is one a lot of small businesses will recognize — your public pages are tidy, but the answer a visitor actually needs is buried in a PDF brochure or a wholesale spreadsheet that was never meant to go online.

Pip: The post is direct about the fix: "The fix is not to publish every internal file; it is to upload documents to an AI chatbot so it can use them when a visitor asks."

Mara: What this means in practice is that the website becomes the foundation, and your private files fill the gaps — price lists for partners, maintenance schedules, product specs that live in DOCX or CSV form rather than on a public page.

Pip: So the agent is not guessing from marketing copy — it is reading structured rows. That is a meaningful difference for anyone who has watched a chatbot confidently invent a price.

Mara: The post breaks the file types into three practical piles: PDFs visitors already receive by email, Word documents with policies or service menus you update often, and CSV product data where each row is a product or SKU. The guidance on CSV is specific — keep columns clear, drop discontinued lines so yesterday's catalog does not produce today's wrong answer.

Pip: And the testing step loops back to the previous post's checklist: ask about the buried maintenance schedule on the preview page, fix the source file if the answer is vague, and ask again.

Mara: There is also a pointer to deeper integration for later. The post mentions MCP-powered backend connections and links to a plain-English explainer on what MCP actually is — but the practical advice is to start with documents and connect systems when you genuinely need them.

Pip: Which is, honestly, the sensible order of operations. Start with what you already have, make it findable, and expand from there.

Mara: The post ties it back to a recurring theme on the site: your business already has the data. Uploading a clean brochure or catalog is the small step that puts it to work at the front door.

Pip: Test before launch, fill the gaps with documents you already own — both posts are really making the same argument about not skipping the preparation step.

Mara: Next time we will see what else is coming through on the site. More soon.