Sometimes, the demo works but the actual rollout doesn’t. An agent on one task is reliable, but run several against the same codebase and the results stop adding up: they rebuild what a neighbor just shipped, make conflicting changes to the same file, take an approach another agent ruled out an hour earlier. The gap…
When several agents work the same codebase, the way their assignments are divided determines the outcome. Done well, their merges fit together and align to the team goals. But not done well and you get a series of changes that each look right on their own but don’t add up. How you split the work…
The call usually opens with praise. The AI feature shipped on time, users love it and engagement charts are pointing the right way. Then finance closes the quarter, and the feature everyone celebrates loses money on every single request. That is the part the CTO called about. I get some version of this call every…
At their deepest level, LLMs are still a kind of magic. Even the developers who build them find them to be, channeling Winston Churchill, “a riddle, wrapped in a mystery, inside an enigma.” That’s why everyone working with LLMs in their enterprise stack needs a way to peer into the dark mass of weights to…
Brands like Snickers, M&M’s, and Twix are familiar to most consumers, but Mars Inc. doesn’t just produce snacks. The family-owned company, with a revenue of approximately $65 billion, is also one of the largest manufacturers of pet food and ready meals, and its more than 100 production facilities operate around the clock. Of course, this…
For a long time, the role of the chief information officer has been defined around systems, architecture and innovation. The expectation was straightforward: ensure uptime, modernize infrastructure and occasionally introduce transformative technologies. From where I sit, the most important technology decisions are no longer purely technical. They are capital allocation decisions. Every architecture choice, platform…
A fraud model looks at an insurance claim and returns a score of 0.23: low risk. A SHAP explanation lays out exactly why: no prior claims, a modest claim value, an unremarkable claimant profile. A human adjuster reads the explanation, agrees with it and signs off. Every box that explainable AI asks us to check…
AI is known to be confidently wrong, and now it’s influencing humans to be that way, too. In a new study, researchers tested AI’s influence on humans reviewing innovation proposals, and found that AI recommender tools were persuasive enough to convince the evaluators to reject decisions made by independent human experts, thus causing them to…