From Embedded to Everywhere: How Forward Deployed Engineering Was Born at PagerDuty by Doug McClure

Why It Matters

FDE turns custom, high‑value customer work into reusable product capabilities, shortening time‑to‑value and strengthening competitive differentiation.

Key Takeaways

• FDE merges field, product, and engineering for rapid feature delivery
• Early customer embeds turned into product‑wide releases
• AI Delivery Workbench enables code contributions in unfamiliar stacks
• Organizational shift moved FDE under product for ownership
• Flywheel model accelerates learning and scales impact

Pulse Analysis

The rise of professional services teams has long helped enterprise SaaS firms close deals, but the model often leaves a gap between bespoke implementations and the core product roadmap. PagerDuty’s Forward Deployed Engineering rewrites that narrative by placing technically adept engineers inside customer environments, allowing them to diagnose blockers, write code, and ship solutions directly into the platform. This hands‑on approach eliminates the traditional ticket‑and‑wait cycle, turning urgent fixes into permanent features that benefit every subscriber.

Central to FDE’s efficiency is PagerDuty’s AI Delivery Workbench, which parses unfamiliar codebases—such as Elixir—identifies patterns, and suggests PRs that meet internal standards. By automating the onboarding of engineers into new stacks, the team can move from problem identification to production release within hours. The strategic decision to relocate FDE under the product organization further aligns incentives, granting engineers full access to product engineering processes, code reviews, and release pipelines. This structural shift ensures that custom work is not an isolated side project but a first‑class contribution to the product backlog.

For the broader SaaS industry, FDE illustrates a scalable path to convert high‑touch, revenue‑critical engagements into reusable product innovations. Companies that adopt a similar flywheel—detecting signals, assessing feasibility, executing, then learning and scaling—can accelerate feature velocity while reducing technical debt from fragmented customer‑specific solutions. As AI‑driven tooling matures, the model promises even faster turnaround times, tighter feedback loops, and a competitive edge rooted in delivering exactly what customers need, when they need it.