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All digests tagged Forward Deployed Engineering
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AI success hinges on Forward Deployed Engineers mapping complex human workflows onto existing enterprise systems. The solution involves building agents on top of legacy platforms, using dependency graphs, and post-training models for context extraction.
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FDE at Cognition measures value by outcomes, not token usage. The goal is to maximize the overlap between product capabilities and customer problems across the entire SDLC.
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FDE success requires two principles: always scoping requests rigorously before building, and scaling processes by automating every stage of the pipeline using AI agents.
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Forward Deployed Engineering (FDE) has evolved from simple DevOps to an outcome-based discipline. As AI makes coding cheap, the value shifts to data integration and customer accountability.
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Forward Deployed Engineering (FDE) at Decagon is identical to product engineering. The goal is ensuring custom work compounds into a self-serve platform, scaling from bespoke deployments to generalized products.
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Forward Deployed Engineering (FDE) shifts sales from selling products or services to selling outcomes. This model requires building bespoke solutions on a reusable platform, which is crucial when targeting non-technical enterprise buyers.
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