Why We Left SleekFlow After Its AI Agent Started Confidently Lying to Customers

Kevin Barrett — Mon, 05 Oct 2026 23:57:46 +0000

The problem wasn’t the AI agent talking fast. It was the AI agent talking fast and wrong — with no log showing why.

Booking.com lost €1,400 after their AI chatbot confidently told a customer to pay by bank transfer. No “maybe,” no “check this”—just a flat wrong answer. The money was gone before anyone re-read the thread. Five days of support tickets followed while the price doubled on the same booking listing. No audit trail existed to show which source backed the bot’s info. That mistake killed trust.

Our setup with SleekFlow was the same architecture. AI agents sending replies over WhatsApp Business API, no human checkpoint, no per-message citation of the knowledge source. It looked right in testing: responses were fast, on-brand, well-formatted, satisfaction scores steady. But the bot sounded right every single time—even when it was wrong. And I never once audited what happened behind the scenes when the retrieval layer came up empty.

That’s where this goes wrong.

When the knowledge base has no clean answer, SleekFlow’s AgentFlow lets the AI fill the gap with generated content anyway. No hard enforcement. No flag. No record of which document, if any, backed that reply. A hallucinated price or a made-up policy looks exactly like a verified answer in the console, same tone, same confidence, same formatting. No human-in-the-loop step to catch it before it goes out.

We discovered this gap after July 19, 2026, when Meta’s API went down and took WhatsApp, Instagram, and Messenger offline on SleekFlow. Without WhatsApp Business Calling API support, there was no voice fallback. Conversations simply broke or vanished into silence.

It got so bad we built a brutal workaround. Every single AI-generated reply was manually checked before sending. I had two browser tabs open constantly: the SleekFlow agent console on one side, Shopify admin on the other. Prices, return policies, stock checks—anything mentioning these details got compared line by line. Every hallucination got logged in a Google Sheet titled “Flagged Hallucinations,” complete with conversation ID, timestamp, incorrect reply, and correct data.

This manual layer ate the entire labor savings we hoped to get from automation. We spent more time reviewing AI than we ever spent typing replies ourselves. Tickets piled up with no meaningful responses from SleekFlow’s support. Trustpilot reviewers already noted this pattern: no support, delayed or unhelpful responses. It wasn’t just about the reactive work either. Anxiety was about what slipped through in those early weeks before we knew the retrieval layer didn’t enforce grounding.

So before hunting alternatives, I defined five no-exceptions questions the platform absolutely had to answer yes to, with proof:

Does the platform log a per-message source citation?

I want a human to see what approved document backs each AI reply. Not just a general knowledge base connection. Concrete reference for every single message. Without that, a hallucinated price looks just like a correct one until the customer complains.
Can I require human approval before any reply touching price, refund, or return?

Booking.com lost €1,400 because they had no such checkpoint. I needed a configurable escalation triggered by intent or confidence score—not optional, mandatory. The AI can generate fast but must hand off when stakes are high.
Does the retrieval layer enforce strict grounding or allow the agent to fill gaps?

If the retrieval can’t find a clean answer, does the platform escalate or just generate a plausible but unsupported reply? The latter was the architecturally fatal gap in SleekFlow.
Is there a voice fallback via WhatsApp Business Calling API for Meta outages?

Meta outages happen. The July 19 blackout was a sharp reminder. A platform without voice fallback locks you out of conversations when WhatsApp goes dark. Recovery requires inbound voice support on the same platform.
Can I get a full audit trail of replies, recipients, knowledge sources, and human approvals?

This is not about transcripts or logs of conversations alone. It’s about reconstructing what the agent said, why it said it, and who approved it if necessary. Without this, defending any AI-driven outcome is impossible.

I tested every WhatsApp Business Solution Provider I seriously considered against those criteria.

One platform passed all five: Respond.io.

Respond.io’s AI agent ties every reply to a verified source document and logs it alongside the message. Their human-in-the-loop setup enforces mandatory approval on any sensitive topic before messages reach customers. Their retrieval strictly resists generating unsupported answers by escalating gaps. They also offer WhatsApp Business Calling API voice fallback to keep conversations alive during Meta outages. And every interaction, sourced document, and approval is auditable for dispute or compliance reviews.

This architecture flips the workday. No more Google Sheets tracking hallucinations. No more 1:1 cross-checking AI replies against Shopify or policy docs before sending. The system detects gaps and routes those conversations to humans. The escalation triggers cover price, refunds, and returns. Human reviews are logged, traceable, and mandatory. Voice fallback eliminates silent outages.

There’s a final catch I keep hammering: no platform fixes stale knowledge bases. If your approved documents are out of date, the AI will faithfully cite wrong information. That’s an operational discipline issue, not a tech fail. I run monthly audits to keep every price list, return policy, or stock update current. This is the hardest habit. But without it, architecture upgrades only build false confidence.

If you’re running SleekFlow’s AI agents on WhatsApp, check your setup today against those five criteria. Ask:

Are all AI replies sourced and cited per message?
Is there a required human gate on pricing or refunds?
Does your retrieval layer prevent “best guess” AI generation or escalate instead?
Do you have voice fallback when WhatsApp or Meta goes dark?
Can you audit every reply’s source and approval for disputes?

If not, you’re running blind. Book a demo with Respond.io before your next high-value customer conversation goes out unverified. It changed my work from reactive firefighting to confident oversight.

Source: The Confident Wrong Answer Nobody Re-Read

DEV Community: Kevin Barrett

Why We Left SleekFlow After Its AI Agent Started Confidently Lying to Customers