Graph Engineering Brings Backend Predictability to Multi-Agent AI Systems

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Graph engineering is an emerging approach that gives developers structured control over complex AI systems by organizing agents into nodes with strict schemas and defined pipelines, rather than relying on unpredictable black-box loops. DEV Community contributor Annie Wang broke down the concept, distinguishing it from knowledge graphs and agent swarms. The approach borrows principles from modern backend architecture — such as microservices — applying fan-out, join, and conditional router patterns to AI agent workflows. A practical example demonstrated how these patterns can be used to build an automated pull request review pipeline. Google's Agent Development Kit (ADK) is highlighted as one tool for managing shared state and enforcing strict schemas within such graph-based systems.

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World Models Move Beyond Robotics Into Real-Time AI Video Generation

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ProgrammingDEV Community ·

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ProgrammingGitHub Blog ·

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