Why agentic AI must be treated as privileged infrastructure, not a productivity feature.
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Why supervised autonomy is the operating model that makes enterprise AI scalable.
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AI at scale requires continuous control, not periodic oversight.
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Why early discipline determines whether agentic AI scales into advantage or stalls into risk.
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The enterprise workforce is changing. Not gradually, but structurally.
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Organizations that combine AI-assisted augmentation with disciplined governance and strong operating models will be best positioned to sustain this evolution with confidence and trust.
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Why the next leap in enterprise capability requires new controls, not just new tools.
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There are new frameworks for using teams of coding agents with little or no human supervision. Though most are not ready for daily use, the ideas and components within are worth understanding, and should drive organizations to do more using multiple agents with human supervision.
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2025 was the year software developers learned to be a technical team lead supervising one or two coding agents. 2026 is shaping up to be the year that developers employ several coding agents to accomplish tasks simultaneously, and perhaps without supervising them at all.
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Why accountable oversight is the foundation of enterprise AI at scale.
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How to prevent AI ambition from outrunning operational trust.
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Why modern governance must be continuous, embedded, and measurable.
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