Hands working on a glowing holographic interface, representing engineers building a system directly alongside a customer team.
Forward deployed engineering puts AWS builders and customer teams at the same interface, working on one production system together.

On 30 June 2026, AWS announced a 1 billion US dollar investment in a new unit called Forward Deployed Engineering, or FDE. The unit embeds experienced AWS engineers, many of whom build AWS AI services, directly inside a customer’s business, engineering, and security teams. Those engineers build and deploy production AI systems using the customer’s own data, governance, and processes.

A forward deployed engineer does not write a report and leave. They sit with your team and ship working software. According to Reuters, AWS sends a pod of about five to six engineers to a customer for a 45-day engagement, drawn from a unit it expects to grow to thousands of people. Those engineers work alongside AI agents , which are tools that complete tasks on their own. AWS calls the approach agentic-first, and positions it as a way to shorten delivery from months to weeks or days.

The model is built around shared business outcomes rather than billable hours. AWS structures each engagement around results, summed up in its line “When customers succeed, we succeed.” The goal is that the customer is self-sufficient when the work ends. You keep the new solutions and the new engineering capability. Named early customers include the Allen Institute, Cox Automotive, the NBA, the NFL, Ricoh, and Southwest Airlines.

How it differs from traditional consulting

Traditional consulting assesses a problem, recommends a path, and treats each engagement as a standalone project. Forward deployed engineering builds with you inside your systems and hands over a running product.

Traditional consultingAWS Forward Deployed Engineering
Main outputReport and recommendationsRunning production AI system
Team on siteAdvisorsEngineers who build AWS AI services
StructureStandalone projectShared business outcomes
TimelineMonthsDays to weeks
At the endHandover documentSelf-sufficient customer team
Step 1 Embed the pod Around five or six AWS engineers join your business, engineering, and security teams.
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Step 2 Build with agents Engineers work alongside AI agents on your data, governance, and processes.
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Step 3 Ship to production The system reaches production in weeks or days, not months, structured around business outcomes.
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Step 4 Leave you self-sufficient You keep the solution and the new engineering capability when the pod exits.

The forward deployed engineer role is not new. Palantir originated it in the mid-2000s, embedding engineers inside sensitive client environments for weeks or months to build rather than advise. The pattern is now spreading across the industry, and AWS is scaling it for the cloud AI era. AWS also runs a Forward Deployed Engineering for Partners program, where partners build their own AWS-credentialed engineering teams to deliver production agentic systems.

Why it matters

Enterprise AI often stalls between a promising pilot and a system in production. Forward deployed engineering attacks that gap by putting the people who build AWS AI services next to your data and your constraints. That is a bet on delivery, not advice.

The 1 billion US dollar commitment signals that AWS sees embedded, agentic delivery as a strategic front, not a side service. It also puts pressure on traditional consulting, where the deliverable is a recommendation rather than a running product. For teams trying to move from a prototype to production , the model promises a faster path and, importantly, capability you keep.

Further reading

Sources