If you are building AI agents for the healthcare industry, you have likely already accepted that “average accuracy” is a misleading comfort metric. 💡In healthcare AI agents, hospitals, patients, and buyers are not purchasing technology alone; they are purchasing trust. One unsafe response in a single conversation can escalate and
We have had the privilege of working closely with one of a global mental-healthcare organisation building safe, evidence-based conversational AI for triage, therapy support and chronic-care management. Across product, engineering, conversation design, and even clinical teams, we consistently saw the same challenge surface again and again: “We can’t afford
The moment Sam Altman said, “It’s never been faster to go from idea to product,” the entire room at Dev Day nodded in agreement. With Apps SDK, AgentKit, and Codex, OpenAI just turned ChatGPT into the operating system for AI agents. Anyone can now design, deploy, and distribute AI-powered
In the age of Agentic AI, shipping without deeply understanding user experience isn’t just a risk, it’s an existential threat to your product. Product development used to be a fast loop: ship, track, fix, repeat. A few A/B tests, some bugs logged, and you were iterating in
One of my biggest fears while releasing AI agents was their unpredictable behaviour in production. You can test in staging, run evals on golden datasets, and even have your team dogfood the agent, yet the moment real users arrive, everything breaks. As AI agents move into customer-facing roles, these unpredictable