Foundry

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

Jul 28, 2026 · 16:49

Natalie Meurer argues that forward deployed engineering (FDE) has become a meaningless label because it has stretched from DevOps at Palantir in 2008 to data integration, ontology work in Slate and Foundry, solution architecture, and enablement—but its durable core is customer accountability and outcome-based pricing. Tracing FDE's history at Palantir, she shows how the role evolved from keeping the platform stable (2008) to data integration (2012), custom dashboarding in Slate (2016), and finally customer enablement in Foundry (2020). As coding agents make software cheap, she contends the lasting value lies in integrating data, understanding customers, and owning outcomes. Pricing tells the story: seat-based assumes a tool, while usage or outcome pricing puts the provider on the hook—exactly what FDEs have always done. She concludes agent engineering is FDE reborn, and that product, infra, and AI engineering are all trending toward the same customer-accountable model.

How Forward Deployed Engineering is done at Kepler — Vinoo Ganesh

Jul 28, 2026 · 22:20

Vinoo Ganesh, a former Palantir engineer who built the Frontline rotation program, argues that Forward Deployed Engineering is a product strategy, not a go-to-market motion, and shows how Kepler applies this philosophy. He illustrates with stories from Palantir: solving a shipping customer's 47-page requirements with a four-hour Slack alert, building a Parquet viewer after watching a data quality engineer manually spot-check CSVs, and the Groovy script that became a product supporting 100,000 people. Ganesh emphasizes detecting real problems by observing users' actions—any repeated task hints at a missing feature, and pulling out a phone mid-workflow is a bug report you'll never find in documentation. He also explains defining ontology: when different teams call the same entity 'clients,' 'billing,' or 'accounts,' the FDE must canonicalize terms to become the linguistic foundation. The hardest skill is discarding—ship everything as if it will run 18 months, because every hack goes into production. Ganesh concludes that FDEs drive product leverage by solving small problems on-site, then generalizing solutions into the core product.

Forward Deployed Engineering 101 — Kevin Bai, Anthropic, ex Palantir & Rippling Founding FDE

Jul 28, 2026 · 17:48

Kevin Bai of Anthropic, formerly at Palantir and Rippling, argues that Forward Deployed Engineering (FDE) is the go-to-market motion for selling highly technical platforms to non-technical buyers, as Palantir did with its Foundry platform. The key is not building bespoke solutions from scratch but assembling outcomes on a reusable platform of shared primitives; otherwise it becomes a dev shop. FDE targets a specific quadrant: complex product + non-technical buyer, exemplified by Palantir's $4M average contract value vs. ServiceNow's $1.2M and Workday's $600K. Two questions determine need: is your product complex enough to require hand-holding, and do you have engineers who can carry that? AI has made building easy and nearly everything agentic, pushing FDE toward the center of software sales.

On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft

Jul 17, 2026 · 17:35

Pablo Castro, Distinguished Engineer and CVP for AI Knowledge at Microsoft, argues that AI agents require three knowledge types—intrinsic (model training), extrinsic (RAG and grounding), and learned (optimization loops)—and demonstrates how Microsoft's Foundry IQ, Azure AI Search, and Agent Optimizer deliver them. He shows that combining retrieval methods outperforms individual ones, citing Azure AI Search evaluations where combined methods improve evidence recall and answer completeness. He introduces Foundry IQ as a layered system with agentic retrieval that reflects on data to satisfy information needs before returning results. Castro demonstrates creating a knowledge base that connects unstructured data, structured Parquet tables, and the web, then linking it to an agent via MCP server. He also showcases the Agent Optimizer, which uses hill climbing on baseline evaluations to automatically refine agent instructions, tool definitions, and skills based on runtime traces, enabling continuous learning loops that capture organizational differentiation.