AWS is putting $1B behind forward deployed engineering. What should buyers ask for?

AWS announced a $1B Forward Deployed Engineering organization to embed experts with customers and co-develop agentic AI solutions.

Source: https://www.aboutamazon.com/news/aws/aws-1-billion-forward-deployed-ai-engineers

This feels like a useful moment for the forward deployed engineering category. The big cloud vendors are no longer treating AI implementation as only tooling, enablement, or partner services. They are moving toward embedded teams that sit closer to the messy workflow, the data, the security model, and the business owner.

The buyer question should not just be: can your engineers build an agent quickly?

I would want to ask:

1. Which workflow are you owning all the way to production? Not demo, prototype, or enablement. Which real workflow has a named owner, users, failure modes, and a system of record?

2. What gets transferred to the internal team? A forward deployed team should leave behind more than code. The operating model matters: runbooks, evals, permission boundaries, approval gates, observability, escalation paths, and postmortem loops.

3. How are AI actions constrained? If an agent can touch customer data, tickets, records, payments, code, or internal tools, the permission model and audit trail are part of the product.

4. What is the success metric after the FDE team leaves? Time-to-demo is not enough. Better metrics are: adoption, cycle time reduction, exception rate, rollback rate, human review load, cost per completed workflow, and whether internal operators can safely modify the system.

5. Who owns the weird edge cases? This is where forward deployed engineering usually earns its keep. Real deployments fail in handoffs, unclear approvals, bad source data, policy exceptions, and systems that do not quite match the diagram.

My read: AWS putting this much weight behind FDE is a sign that enterprise AI is becoming an implementation discipline, not just a model/platform buying decision.

If you were evaluating a forward deployed AI team, what would you ask before letting them near a real workflow?