All Posts

Your agent's dashboard is green, every action logged. That tells you what it did, not whether it was allowed. Observability, guardrails, authorisation, auditability: 4 different controls most teams collapse into 1. What each one actually does, why a system prompt is none of them, and the point where watching an agent stops being enough.

A human approving every agent action isn't a control, it's a signature, and it doesn't scale. The test for which of your safeguards actually enforce anything, and where a human still earns their seat.

An agent that can act can't be measured with metrics built for chat. Here's what to actually watch, where the honest numbers come from, and the instruments that lie to you on the way.

Air Canada told a tribunal its chatbot was a separate legal entity responsible for its own actions. It lost. When an agent ships, someone still has to own what it's allowed to do, and that's a product job, not a code one. An agent with no product owner is a liability with a friendly voice.

For weeks, a lender's support agent quietly handed out free credit. Nobody caught it, because nothing looked broken. It was a buy-now-pay-later lender I worked with, for a fintech. The agent had real authority: it could pause a plan, waive a fee, restructure a schedule, on its own. Useful. Also quietly expensive. Every time it paused a plan sitting on a 0% promo, the pause broke the promo's conditions, and a broken promo defaulted to interest-free, so the customer kept the 0%

On a fintech engagement last year, I watched a servicing agent tell a customer in financial hardship that it'd paused her payments for 60 days and that her 0% promotional rate was safe. She thanked it. The chat closed with a high CSAT. It was impossible. The startup was a buy-now-pay-later lender: split a purchase into a few interest-free installments, with some plans running on a promotional 0%. That 0% isn't a gift, it's conditional. The customer keeps it only while they ho

Modern products ship AI fast, but few deliver real user value. This guide shows how to design AI features that solve real problems, improve outcomes, and integrate seamlessly into your product. Learn principles, examples, and tactics to build AI that drives retention, adoption, and long-term competitive advantage.

Who This Guide Is For Compared to building everything manually, juggling design, frontend/back-end coding, and QA, this approach can be 10x faster and cheaper, but you’ll need guardrails. Lovable handles rapid MVPs brilliantly, BUT !!! complex workflows still need product discipline. I built this guide is for anyone who wants to build real apps, no coding or PM experience required. Maybe you’ve got an idea stuck in Notion, a client project that needs a mini-SaaS, or just want