No hype. No fluff. Just practical insights on how AI agents are changing the way small and mid-size businesses operate.
DBS Bank and Visa just tested AI agents making credit card transactions independently. That's not a chatbot writing emails — it's software spending money. Here's what businesses need to figure out before letting agents take consequential actions.
Security hardening for AI agents is necessary. But hardening applied wrong doesn't just fail — it creates new failure modes, buries real signals under false alarms, and hands attackers the exact leverage they need. Here's where hardening goes wrong and how to tell the difference.
Business Insider's solopreneur story is real. So is the METR study showing developers getting 19% slower with AI tools. Both are true at the same time. Here's what separates the businesses making it through the J-curve from the ones stuck at the bottom.
Intuit and Anthropic announced a partnership on February 24th that will let mid-market businesses build custom AI agents with 'no technical expertise required.' Building is the easy part. The five skills required to run agents well don't come with any platform.
Business Insider just profiled a solo founder running his entire company with 15 AI agents. The headline sounds impressive. The architecture questions it raises are the part nobody's talking about. Here's how to run multiple agents and actually know what each one is doing.
Mustafa Suleyman says all white-collar jobs will be automated in 18 months. He's probably directionally right. But his timeline ignores the hardest part of the problem — and it's the part that matters most for your business.
The news isn't about what happens to OpenClaw. It's about what happens to the boundary between human work and agent work — and why frontier operators see opportunity where others see uncertainty.
An AI agent's capabilities are only as reliable as your ability to verify them. We run automated skill evaluations using promptfoo on every change — before anything reaches a client's deployment. Here's how it works and why it matters.
'Production-ready' is used to mean a lot of things. For an OpenClaw deployment managing real client communications, connected to real business systems, and expected to be available 24/7, it means something specific. Here's the full picture.
Wall Street just wiped $285 billion off the books of enterprise software companies because of AI. Meanwhile, a small business that deeply knows its customers is sitting on something no enterprise AI budget can buy. Here's why that matters more than most people realize.
After deploying OpenClaw for multiple clients, the same mistakes keep showing up. Most of them aren't obvious until something goes wrong. Here's what to watch for and how to avoid it.
A 3-person team is producing what required 10 people 18 months ago — spending $1,000 a day on AI instead of salaries. That math doesn't work for every business. But the underlying shift it represents affects every business that pays people to do repetitive work.
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