The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra
Forward Deployed Engineering (FDE) is not a single discipline but an outcome-focused role that has evolved significantly from initial platform stability work (DevOps) to complex data integration and customer enablement. The core thesis is that as coding becomes cheap due to AI agents, the value of engineering shifts entirely to understanding the customer's problem, integrating disparate data sources, and being accountable for measurable business outcomes.
Key takeaways
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FDE is an outcome-based role, not a code-writing one.
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The durable part of FDE involves integrating data, understanding the customer context, and maintaining accountability to a specific result, rather than merely writing software code.
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FDE's evolution tracks platform maturity.
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Early FDE focused heavily on DevOps and ensuring platform stability (e.g., deploying on an EC2 instance). This evolved into data integration using concepts like the Ontology, leading to modern platforms like Foundry that focus on 'data to decision-making.'
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Pricing models reflect accountability.
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The shift from seat-based pricing (assuming a tool) toward usage or outcome-based pricing confirms that the value lies in guaranteeing results, which is the hallmark of FDE.
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AI agents are simply FDE reborn.
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Agent engineering is viewed as a subset and manifestation of FDE principles, where engineers use LLMs to enable outcomes for customers. The role requires combining product knowledge with customer-facing solutioning.