Chapter 8 — Playground testing

The playground is a safe sandbox for testing your agent the same way a real visitor will — same chat pipeline, same retrieval, same skills, same routing rules. The only difference is that you're driving the conversation as the visitor.

In this chapter you'll:

Start a fresh playground session
Run a complete advisory → qualification → meeting-booking conversation
Actually complete a Cal.com booking from inside the chat
Accept the "remember me" consent prompt that fires after personal details are shared
End the session and watch the Insights panel populate with the summarisation outputs

By the end of the chapter you'll have a fully realised playground session that we'll come back to in Chapter 10 to see the persisted prospect record.

Use the playground constantly while you're configuring. Every change you make in Chapters 3–7 — agent profile, skills, routing — should be tested here before it goes live. A 90-second playground session catches more configuration errors than any amount of staring at the settings pages.

The playground screen

Open Playground from the team's left-hand rail.

Playground — empty

Three panels:

Left: Sessions list — every test session you've run, grouped by day. Filter by search, thumbs up/down, sentiment, and channel. Group by channel if you want to compare widget vs. playground conversations.
Middle: Conversation pane — the chat itself. Includes the greeting, suggested questions, and the message input.
Right: Insights panel — fills in after a session ends with summary, sentiment, outcome, resolution, conversation quality, content analysis, and key words.

A fresh team has no sessions; the middle panel just invites you to create one.

Starting a session

Click New session in the left sidebar. The platform creates an empty session and the conversation pane comes to life with the agent's greeting and your three suggested-question chips — exactly what we configured in Chapter 3.

Greeting and suggested questions

Two things to notice:

The greeting matches what you typed in the Greeting step of the agent wizard.
The placeholder text in the input box — "Ask about services or book a strategy call..." — is the Chat input hint field from the same screen.

If either of these doesn't match what you configured, you have a clue about where to look (the agent's greeting page).

The Advisory + Qualify Prospect path

A useful test conversation is one where a visitor arrives unsure what they need and ends up booked into a meeting. It exercises the Advisory Guidance skill (consultative tone), the Qualify Prospect skill (capturing the configured memories), and the Book Meeting skill (the inline Cal.com widget).

Turn 1 — Open with the problem

Type a realistic opener into the input. For this run we use:

Hi — we're a 60-person SaaS, growth has stalled. Not sure if it's a positioning problem or an ops problem. Can you help?

Click send. The agent thinks for a few seconds, then streams its reply. For our configuration:

First-turn reply — advisory probe

The reply opens with a brief framing of the issue, then asks for the Current Challenge memory in clearer terms. The word current challenge is highlighted in the agent's message — that's the platform telling you a memory type is being actively pursued. The agent has also recognised the Organization Size (60-person SaaS) from the visitor's message and silently captured it.

This turn is doing a lot at once:

Intent planner: classified as advisory.
Slot extractor: captured Organization Size = ~60.
Tool planner: didn't search the knowledge base (this turn is about understanding the visitor, not answering a question).
Responder: ran the advisory skill, asked for the next memory we need (Current Challenge).
Summariser: updating the session summary in the background.

Turn 2 — Provide the detail

Reply with concrete specifics:

Pipeline has flatlined for two quarters. We were doubling new logos every six months until late last year. Now we're winning about the same number of deals each quarter and our average deal size hasn't moved.

The agent's response now ties the situation to a specific service line, draws on the ingested content for diagnostic patterns, and offers the next step:

Second-turn reply — diagnoses Growth Strategy, offers strategy call

Note the 5 sources chip beneath the reply — the agent searched the knowledge base and is citing chunks from the content you uploaded in Chapter 4. The whole reply is grounded in your own copy.

Turn 3 — Accept the strategy call

Reply with:

Yes, that sounds useful. Can we set up that strategy call?

The Book Meeting skill activates. The agent acknowledges, and the Cal.com booking widget embeds inline in the chat:

The widget is a live <iframe> against the Cal.com event URL you configured in Chapter 6. It shows the calendar, the bookable dates, and the time slots — all served by Cal.com, not by the platform. The visitor never has to leave the chat.

Completing the booking

Click a date in the calendar — for this run we pick Monday 18 May at 14:00. Cal.com loads the booking form inside the iframe. Fill it in.

Booking form filled in

For this manual we use:

Name: Test booking
Email: test.booking@example.com

Click Confirm. The booking submits to Cal.com, the calendar invite goes out, and the chat updates with a confirmation banner and an agent acknowledgement:

Booking confirmed plus "Remember me" prompt

Two things just happened at once.

Booking confirmed. The agent says "Great — your strategy call is booked for Monday, 18 May 2026 at 14:00 BST." The platform recognises the booking event and the agent is now post-booking aware.
"Remember me" prompt fired. At the bottom of the chat: "You just shared your details — want us to remember you next time?" with [Remember me] and [No thanks] buttons.

The "Remember me" prompt is the platform's GDPR-aware consent gate. It surfaces automatically when a visitor has just submitted personal data — through a booking, a contact form, or after a fixed number of turns. The pattern is consent before persistence — until the visitor accepts, the platform holds the captured memories in-session but doesn't write them to the prospect record.

Accepting "Remember me"

Click Remember me. The prompt disappears.

Remember me accepted

From this point on, the memories the agent captured during the session — name, email, the booking, the Current Challenge, the Organization Size — will be persisted to the prospect record on session end.

What if the visitor clicks "No thanks"? The conversation continues but no personal data is persisted. The platform respects that decision per-visitor; subsequent sessions from the same browser also start without persistence until the visitor changes their mind via the Manage data option in the session actions menu (below).

Click the three-dot Session actions button to the right of the Playground heading.

Two items:

Manage data — opens the GDPR data-management panel. From here, the visitor can review what's been captured about them and request erasure. In a real widget deployment this is also surfaced to end users; in the playground, you use it the same way to see the visitor's perspective.
End session — closes the session, triggers the summariser, and locks the conversation. After this, you can't send more messages in this thread — though you can always start a new one.

End the session

Click End session. The message input greys out and a banner appears: "This session has ended. You cannot send more messages."

The summariser runs in the background, which can take a few seconds. Reload the page after a moment and the Insights panel populates.

Insights panel populated after session end

This is what comes back:

Status: Completed.
Summary — a one-paragraph synthesis of the conversation: "The user is seeking assistance for stalled growth in their 60-person SaaS company, unsure if the issue lies in positioning or operations. The assistant diagnosed the problem as likely stemming from a stagnation in the growth strategy rather than operational issues and offered a strategy call to address it. The user agreed to the call, which the assistant successfully scheduled."
Conversation quality: 5/5 (the platform's own assessment of how well the agent handled the conversation).
Outcome: Appointment requested.
Resolution: Resolved — "The user successfully booked a strategy call to address their concerns."
Sentiment: Positive.
Content Analysis — product mentions (Growth Strategy, Operational Excellence) and key words (stalled growth, growth strategy, pipeline, positioning, strategy call).

All of this surfaces in the analytics dashboard (Chapter 11) and contributes to the prospect record (Chapter 10).

What to test in the playground beyond the golden path

The conversation above is the happy path. A real configuration review should also exercise:

Cold-start informational queries — "What industries do you work in?" or "How do you bill?". Tests the Informational skill, the knowledge-base retrieval, and the FAQ fast-path.
Edge-case intents — type something genuinely off-topic ("Can you recommend a good pizza place?"). The platform classifies this as unrelated and the agent declines gracefully.
Unhappy paths — start a session, give one terse answer, never engage. Does the agent get pushy? Does the routing engine still drive at things despite weak signal?
Multi-turn drift — eight or ten turns deep into a conversation, does the agent still remember what was said early on?
Different prospect shapes — a 5-person consultancy (out of fit), a 300-person enterprise (in fit, big), a vague "I'm exploring" visitor. Tests that the qualification and routing rules behave sensibly across the range.

You don't need to do all of this every time you change a setting, but it's worth a 10-minute pass before any deployment goes live.

What's next

You've configured the agent and verified it works. Time to put it in front of real visitors.

Continue to Chapter 9 — Deployments & widget embed.