Fractional Forward-Deployed Engineers.
Agents running your operations. Not a demo. Not a deck.
Most companies can already picture the agents they want. Getting them live is where it falls apart — messy data, disconnected systems, and a proof of concept handed over before the real work begins. We embed an engineer with your team to build, ship, and transfer ownership of agents your people can actually run.
Fractional Forward-Deployed Engineers is a Layer 04 service.
Every service we offer maps to one of four layers. Fractional Forward-Deployed Engineers sits in the Agents — the layer that owns ai consulting & co-pilots.
Why agents stall — and how to get yours live.
Most companies don't lack imagination. They can describe the support agent that resolves tickets before a human sees them, the RevOps agent that cleans records the moment they land, the sales agent that scores and routes leads while the rep is in another meeting. Picturing them is easy. Building them into a system you trust is the part nobody warns you about.
The usual path goes like this. A consultancy runs discovery, delivers a deck, maybe ships a proof of concept against clean sample data. Then they leave — and you're holding the actual work: the integrations, the data cleanup, the production debugging, the change management. A forward-deployed engineer doesn't advise from the sidelines. They write code in your environment, wire your integrations, tune the agent against your real workflows, and watch it run in production. The model comes from the companies betting hardest on agents right now — Palantir pioneered it, OpenAI scaled a team from two engineers to dozens in a year, and Salesforce calls it today's hottest role.
The "fractional" part means you get that depth without a full-time hire. One engineer, focused on your deployment, for the weeks it takes to get something real into production. The engagement runs 12 weeks — enough time to audit the kernel, build and integrate the first agent, and transfer ownership so your team runs the next one without us.
The process.
Discover · audit the kernel
Map your data, integrations, and process gaps — so we know exactly what an agent can see, what's broken, and what has to be hardened before anything touches a live workflow.
Expose · wire the foundation
HubSpot becomes the queryable source of truth your agents call first. Integrations are connected, data is cleaned, and the kernel is hardened against the real load an agent will put on it.
Wire · build and integrate
We write the code. The first agent ships against a real workflow — not a sandbox with anonymized data. Integrations are live; the agent is in production.
Operate · transfer and run
We run the agent live, debug the edge cases that only surface under real load, and train your team to own it. You leave with runbooks — not documentation nobody opens.
What you have at week twelve.
Every engagement scopes against a maturity-level transition. This one moves you from L2 (Assembled) to L3 (Instrumented) — where the kernel is clean, the agents work, and your team trusts the numbers without us in the room.
Things people ask before booking.
What's the right first agent to build?
The one that removes the most manual work for the least integration risk. In week one we map your workflows and pick the highest-leverage starting point — typically a lead-routing agent, a churn-signal agent, a data-hygiene agent, or a support-triage agent. A focused agent in production beats five half-built ones every time.
What if we're not ready for a 12-week engagement?
Ask about a pre-contract proof of concept. We'll build something real against your data before you sign — so you can see the model work before committing to the full engagement. If the kernel isn't ready, we'll tell you that in the Diagnostic and scope the foundational work first.
See how fractional forward-deployed engineers fits into The Agents.
The engagement starts with a Diagnostic — 90 minutes, no slides. We map what your company already knows, what it's forgetting, and which agent is worth building first. You leave with a recommended starting point and a clear read on the kernel work between you and a production agent.