Forward Deployment Engineering
Production AI agents. Shipped, not demoed.
Forward Deployment Engineering is a discipline pioneered at Palantir: senior engineers embedded with the customer to ship production AI systems. I run a small FDE practice focused on AI agent infrastructure. The bet: most agent demos are impressive, most agent production deployments are held together with hope. I ship the system that survives production.
The principles
Three ideas that distinguish a production AI system from a demo. Every FDE engagement is built on these.
Mechanical constraints
Make the wrong thing impossible. Use types, schemas, validators — not prompts — to keep the agent on-rails.
Invariant enforcement
Every output must satisfy the contract. Verify, then act.
Deployable today
No "phase 2" features that never ship. The system that ships is the system that wins.
What's included
Production agent system
A working AI agent system deployed in your environment, with observability and kill switches.
Safety architecture
Substrate pattern, defence in depth, runtime policy gates. Auditable, defensible.
Deployment patterns
CI/CD for agents, blue-green, canary, rollback. No surprises.
Runbooks
What to do when the agent is wrong, slow, or being abused.
Handover
Your team owns the system at the end. I leave, you ship.
FAQ
What is forward deployment engineering for AI?
Forward Deployment Engineering (FDE) is a discipline pioneered at Palantir: senior engineers embedded with customers to ship production AI / data systems. The FDE works alongside the customer team, scoping the problem, building the system, and handing it over. For AI in particular, FDE means shipping agent systems that survive production: mechanical constraints, invariant enforcement, and deployment patterns that let your team move fast without breaking things.
Is forward deployment engineering the same as consulting?
No. Consultants give advice and leave. FDEs ship code into the customer's production environment. The FDE is on the hook for the system working, not just for the recommendation.
What is the alternative to Palantir for AI forward deployment?
Independent FDEs and small specialised firms. Dipankar runs an FDE practice focused on AI agent infrastructure: production agent runtime, safety, observability, and Rust-accelerated performance. The advantage over a large vendor is faster iteration, lower cost, and a focused expertise in AI-native stacks.
How much does an AI forward deployment engagement cost?
FDE engagements are project-based, not monthly retainers. Typical projects: USD 25K-100K for a 4-12 week engagement, with a clear scope and deliverables. Larger multi-quarter engagements are quoted separately.
What kind of teams use forward deployment for AI?
Teams that need to ship AI agents into production fast, but don't yet have the in-house expertise to do it safely. Typical cases: financial services (regulatory constraints, audit requirements), enterprise SaaS (multi-tenant agents, customer data), and regulated industries (healthcare, legal).
Engage the FDE team
4-12 week engagements, scoped, with a clear deliverable. A 30-minute call to scope the problem.
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