"Follow the usual AI suspects on X-Andrew Ng, Paige Bailey, Demis Hassabis, Thom Wolf, Santiago Valdarrama, etc.-and you start to discern patterns in emerging AI challenges and how developers are solving them. Right now, these prominent practitioners expose at least two forces confronting developers: amazing capability gains beset by the all-too-familiar (and stubborn) software problems. Models keep getting smarter; apps keep breaking in the same places. The gap between demo and durable product remains the place where most engineering happens."
"Andrew Ng has been pounding on a point many builders have learned through hard experience: "When data agents fail, they often fail silently-giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure." He emphasizes systematic evaluation and observability for each step an agent takes, not just end-to-end accuracy. We may like the term "vibe coding," but smart developers are forcing the rigor of unit tests, traces, and health checks for agent plans, tools, and memory."
Rapid capability gains in AI models coexist with persistent software engineering failures, causing applications to break in predictable ways despite smarter models. The main engineering effort occurs in closing the gap between demos and durable products. Agents often fail silently, producing confident but incorrect answers, which complicates root-cause analysis. Development teams are reinstating fundamentals: systematic evaluation, observability, unit tests, traces, and health checks for each agent step. Agents are being treated like distributed systems, instrumented with tools such as OpenTelemetry, validated with small golden datasets, and subject to regression tests and versioned test harnesses.
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