Greg Isenberg and Vasuman Moza describe forward deployed AI engineering as the work of turning model capability into dependable operational outcomes. The role starts by observing how a business actually works, identifying the costly bottleneck and deciding which steps should use deterministic code, a language model or a human decision.
A working prototype is only the beginning. The engineer must build evaluations, audit outputs, connect the system to existing tools and data, and measure whether the deployment reduces cost, increases revenue or lowers risk. This emphasis on implementation and judgment makes deployment, rather than raw model access, the durable source of value.
The proposed learning path begins with building a useful agent, then hardening it with observability, tests and failure handling. The final step is to defend the work with evidence: explain why each component exists, show how the system behaves under pressure and demonstrate a measurable effect on the underlying workflow.
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