What to Do When Software Engineering Becomes Context Engineering
In a Nutshell
Software engineering is shifting from writing code to engineering context for AI agents, as individual productivity gains stall at the team level because systems aren't designed for AI-native development. Senior developers must become context engineers who externalize tribal knowledge into maintainable instructions and agent skills, while teams adopt constraints engineering over traditional code review to manage the new review bottleneck. Success requires top-down investment in dedicated context maintenance, measuring system effectiveness through intervention ratios and rework rates rather than code volume, and treating context engineering as ongoing competitive infrastructure work.
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Kira Burn serves as director of engineering productivity and AI impact at JetBrains. Maxim Salnikov works at Microsoft as a senior solution engineer focusing on developer tools, specifically AI native or AI powered developer tools. Before joining Microsoft, Salnikov spent two decades in web development and continues to build daily, with his GitHub contribution graph showing increasing activity.
Salnikov describes himself as a full-time learner whose approach involves learning by teaching, training, and enabling at scale across approximately 100 enterprise customers and their developer teams.
Technical leads observe that individual developer productivity has increased by 50%, yet teams hit walls where system-level or organizational productivity gains are not realized. The core issue is that the system itself is not ready for this transformation.
Coding typically represents 30% maximum of enterprise development work. Even with 50% productivity gains in coding, this only affects a fraction of overall productivity. The deeper problem emerges when more code is produced at incredible speed, creating the next bottleneck: review.
AI-generated code presents a multi-dimensional review challenge. The code may be confidently wrong yet appear locally coherent while being globally inconsistent. AI agents ensure all tests pass, but this does not guarantee the code functions correctly within the broader application context. These defects are significantly more challenging to capture than traditional human-created bugs.
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