# The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra

## Executive summary

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

- FDE is an outcome-based role, not a code-writing one.: 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.
- FDE's evolution tracks platform maturity.: 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.'
- Pricing models reflect accountability.: 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.
- AI agents are simply FDE reborn.: 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.

## Technical details

- FDE Evolution (Palantir): The discipline progressed through distinct phases: 1) Platform Stability/DevOps (early focus on infrastructure); 2) Data Integration (using Java variants and modeling data via an Ontology, which acts as a taxonomy); 3) Custom Solutions (via tools like Slate, a drag-and-drop builder); 4) Enterprise Platforms (Foundry), focusing on enabling customers to achieve 'data to decision-making.'
- Ontology and Data Modeling: An Ontology is described as a key Palantir term of art, functioning as a taxonomy for data that allows FDEs to deeply understand the customer's environment and model data appropriately.
- Agent Engineering vs. FDE: While Agent Engineering was initially proposed as a subdiscipline of AI engineering with high customer accountability, the speaker argues that in modern practice, 'everything is trending toward' FDE principles—encompassing product, agent, and solutions engineering.

## Practical implications

- Engineers should view their role through the lens of outcome accountability, regardless of whether they are titled Product Engineer or FDE.
- The most valuable skill set is a blend of technical depth (DevOps, data integration) and business acumen (understanding customer needs and defining measurable outcomes).
- Companies adopting AI/agents must shift pricing models from seat-based to usage or outcome-based to reflect true value delivery.

## Topics

Forward Deployed Engineering, AI Engineering, Data Integration, Product Management, DevOps, Palantir Foundry, Slate, Ontology, EC2 instance, AIP (Artificial Intelligence Platform)

Source: https://www.youtube.com/watch?v=Byv311hdoHE