The Forward Deployed Engineer Playbook for AI

Most AI startups face a similar reality: no established market, no standard workflows, and customers who can’t yet visualize what’s possible. Traditional sales-led discovery often fails in this environment.

Bob McGrew — former Chief Research Officer at OpenAI and early Palantir executive — explains how the Forward Deployed Engineer (FDE) model turns this challenge into an advantage. By embedding technical teams directly with customers, you can uncover critical problems, solve them fast, and feed those solutions back into a core product that gains use over time.

Main Story

The FDE model was born at Palantir out of necessity. Selling a flexible platform to intelligence and defense agencies with opaque, unique workflows required more than demos and slide decks. Palantir deployed two roles into the field: domain-savvy analysts (“echoes”) and adaptable engineers (“deltas”). Together, they bridged the gap between what the product could do and what the customer needed done.

Unlike the standard “find product/market fit, then scale” SaaS model, FDE assumes every new segment demands fresh discovery. The aim isn’t to minimize per‑customer effort — it’s to increase the value of each contract by solving more critical problems and generalizing those solutions into the core platform.

Echoes manage relationships and identify problems worth solving — ideally those in the CEO’s top five priorities. Deltas prototype rough but usable solutions quickly, prioritizing speed over perfect abstractions. The central product team then abstracts these bespoke fixes into reusable capabilities.

"The FD model effectively is doing things that don't scale at scale."

The discipline is in resisting the drift into pure consulting. FDEs sell outcomes, not hours. Success means delivering tangible results, then turning those results into product features that make the next deployment faster and more impactful.

For AI agent startups, the parallels are obvious. Each enterprise deployment is a unique segment. Embedding engineers allows teams to discover high‑value use cases from the inside, overcome adoption barriers, and build a product that gains use with every engagement.

Technical Considerations

For engineering leaders, the FDE model changes the operating rhythm:

• Constraints: Field deployments must work within the customer’s environment — legacy systems, security policies, and data silos. This demands flexible integration patterns and robust privacy controls
• Trade‑offs: Speed trumps elegance in early prototypes. The central team must later refactor and generalize without breaking field momentum
• Tooling: FDEs need portable, adaptable toolchains that work in varied environments, sometimes with limited connectivity
• Latency and throughput: AI agents embedded in critical workflows must meet real‑time or near‑real‑time performance requirements; tuning and caching strategies are field‑dependent
• Context management: For LLM‑based systems, context window limits and prompt engineering constraints shape what’s possible in early deployments
• Security and vendor risk: On‑site work often means handling sensitive data. Clear policies and minimal‑data designs reduce exposure
• Skills: Deltas must be comfortable spanning prototype code, integration, and customer interaction. Echoes must navigate domain politics and extract high‑value problem statements

Integration paths should allow quick wins without deep re‑architecture, while leaving room for deeper hooks as trust grows.

Business Impact & Strategy

For founders and business leaders, FDE shifts the growth equation:

• Time‑to‑value: Embedding engineers accelerates discovery and delivery of meaningful outcomes, reducing the lag between first meeting and first win
• Cost vectors: Headcount scales more slowly if each deployment makes future ones easier; the product becomes a force multiplier for FDEs
• KPIs: Track two metrics — the value of outcomes delivered per customer and the product use achieved (how much more an FDE can deliver without proportional extra effort)
• Org design: Maintain a tight loop between field teams and the core product group. Avoid isolating FDEs into a services silo
• Risks and mitigations: The main risk is over‑specializing for one customer. Mitigate by abstracting field work into reusable features after each deployment
• Evaluation: Success is measured not by uniformity or low marginal cost, but by the growing impact per engagement and the speed at which new segments can be addressed

Outcome‑based pricing aligns incentives. Instead of charging per seat or API call, contracts are tied to the tangible value of the problems solved, with room to expand as trust deepens.

Key Insights

• Embed technical staff with customers to uncover and solve critical problems from the inside
• Pair domain “rebels” (echoes) with rapid‑prototype engineers (deltas) for balanced field teams
• Abstract bespoke solutions into reusable product capabilities after each engagement
• Resist the pull toward pure consulting by selling outcomes, not hours
• Track both outcome value and product use as core success metrics

Why It Matters

For AI startups in uncharted markets, the FDE model offers a way to accelerate adoption and learning without waiting for a single, uniform product‑market fit to emerge.

Technical leaders gain a direct channel from real‑world pain points to product roadmap. Business leaders gain a model for deep customer engagement that compounds over time. Both sides benefit from a product that becomes easier to deploy and more valuable with each new customer.

The payoff is a cycle where every bespoke deployment strengthens the core, making it faster and cheaper to deliver the next high‑value outcome.

Conclusion

The Forward Deployed Engineer model is not a shortcut to scale — it is a deliberate strategy for scaling insight, trust, and product use in markets where no playbook exists. For AI startups facing fragmented use cases and steep adoption barriers, it offers a way to “do things that don’t scale at scale” and still win.

Watch the full conversation with Bob McGrew for a deeper dive into the model and its application: https://www.youtube.com/watch?v=Zyw-YA0k3xo