AI agents are forcing a fundamental rethinking of what digital twins must be. Traditional digital twins — physical asset mirrors, supply chain models, and AI simulation sandboxes — were built for observation and planning by humans. AI agents, however, don't just read data; they write it and execute decisions with real downstream consequences. This demands a new kind of digital twin: a live operational data layer that continuously reflects current system state, encodes business semantics (customers, orders, shipments), and gives agents always-fresh, structured context to act on. Without this, even the most capable agents will fail — acting on stale, fragmented, or semantically incoherent data. The post also covers context engineering practices enabled by this architecture, including context drift detection and multi-agent coordination. Materialize is presented as a SQL-based platform for building these agent-ready digital twins.
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Digital twins before AIWhy AI agents and context engineering drove the next evolution of digital twinsAI transforms digital twins into live operational infrastructureWhy investing in agents means investing in a different data infrastructureSort: