Post by QAwerk

The part nobody mentions when they talk about agentic AI. Every week someone posts an org chart where they replaced their whole team with AI agents. CEO on top, then a coding agent, a sales agent, a marketing agent. The future is here, apparently. What those posts skip: what happens when the agent decides something wrong and nobody notices for three weeks. At QAwerk, we've been crawling AI agents specifically looking for failure modes. What we find, consistently, is not dramatic. It's quiet. An agent that misreads a goal and executes confidently in the wrong direction. An agent that handles edge cases in ways its creators genuinely did not anticipate. An agent that works perfectly in testing and then encounters real user behavior. The hype around agentic AI has pushed a lot of companies to ship fast. Which makes sense. The pressure is real. But there's a difference between moving fast and moving without looking. A few things that actually matter when you're building or deploying an agent: - What does failure look like, and how will you know it happened - What decisions can the agent make autonomously, and where does it need a human in the loop - How does it behave when the input is ambiguous, incomplete, or adversarial - What's the blast radius if it gets something wrong at 3am None of this is anti-AI. I've spent the last couple of years integrating agents into real workflows at Redwerk and watching what actually holds up under production conditions versus what looked good in a pitch deck. The technology is genuinely interesting. The gap between "interesting" and "reliable" is where the real work is. 25% of companies using generative AI are already running agentic pilots. Most of them will learn the hard way what they should have tested first. Some lessons are cheaper than others.