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Context engineering is the practice of managing the information an AI model sees (documents, tool outputs, memory, and structured metadata about the systems it reasons over) so it can make accurate decisions inside a real engineering organization. Most engineering teams have access to the same AI coding agents: Claude, GPT, Gemini, the major variants everyone is shipping. The model is no longer the differentiator.

The Monday morning thread. Someone asks who owns checkout-service. Someone else asks what changed in the Production Readiness Scorecard last week. A third person wants to know if the Kubernetes migration is blocking the launch next Thursday. The answers exist. They live in Cortex. But getting them into the thread means someone stops what they're doing, opens a tab, finds the data, and pastes it back. By the time they do, the conversation has moved on.

Most engineers can tell you exactly how many PRs they merged last quarter. Far fewer can tell you what any of it did for the business. The best engineering leaders can. They draw a straight line from their team's work to ARR: which reliability investment protected revenue, which migration unblocked a strategic customer, which operational improvement reduced churn. They lead with outcomes, not story points.

Software development has never moved this fast. JetBrains' 2026 AI Pulse Survey found that 90% of developers now use at least one AI tool at work. CircleCI's 2026 State of Software Delivery report, covering 28 million workflows across 22,000 organizations, found that daily CI workflow runs jumped 59% year over year, the largest single increase they've ever recorded. In that same period, CI success rates dropped to a five-year low.

Most engineering career advice treats the leadership track as a ladder where each step is a slightly bigger version of the one before it. That metaphor is the reason so many career transitions go sideways. IC, manager, director, and VP are four different jobs. Each has its own failure modes, its own definition of what counts as your work, and its own relationship to the code. The skills that earn a promotion to one level are rarely the skills that make someone effective at the next.

Tamar Bercovici has been at Box for 15 years. She leads the core platform, the backend layer that storage, search, metadata, and AI capabilities all run on. When her systems go down, Box goes down. On a recent episode of the Braintrust podcast, she said the debate around AI-generated code tends to focus on whether the models will write clean code and/or introduce bugs. Tamar's focus is somewhere else entirely.