Building an AI agent is becoming easier. Deploying one inside an enterprise is not.
The real challenge begins after the demo. An agent has to understand the context of a business, work across enterprise systems, operate within governance boundaries, interact with people, take actions safely, and ultimately deliver an outcome that can actually be measured. That is a very different problem from getting a model to produce a good answer in a sandbox and it's where most enterprise AI quietly stalls.
This is where I believe Forward Deployed Engineering becomes interesting.
What Forward Deployed Engineering really means
The original idea behind FDE was simple: put engineers close enough to the customer that they understand the real problem, build around it, and get something working in the real environment, not a prototype, but something that runs where the work actually happens.
AI changes what that model can become. An FDE team can now take a business problem and build a full execution chain around it:
Context → Data → Model → Agents → Tools/APIs → Human approval → Action
But there's an important point buried in that chain. The starting point shouldn't be the agent. It should be the business problem and the people who understand it deeply. Every business process contains nuances that systems don't capture user behaviour, human interactions, exceptions, experience, and judgement.
The record is not the context
Take a customer transaction. A traditional system records what was bought, when, where, and for how much. But a business or marketing team may see something entirely different. Was the customer happy? Was there hesitation? What influenced the decision? What signals pointed to satisfaction, frustration, or intent?
The transaction is the record. The behaviour around it is the context.
That context is what an AI agent needs if it is expected to do more than retrieve information or automate a predefined task. And this is the part that's easy to underestimate: building the agent is only half the job. Once an agent starts participating in a real business process, much harder questions emerge.
Should the agent act? What is it allowed to do? When does a human intervene? What data can it access? How do we govern its decisions? What does each decision cost? And, most importantly — was the outcome actually better?
This is why FDE for enterprise AI needs to bring together far more than engineering. It requires domain knowledge, business context, data, agent orchestration, governance, and economics, all working together.
The metric that matters: cost per successful business outcome
That last requirement economics deserves its own line, because most of the industry is measuring the wrong thing.
The metric I care about is cost per successful business outcome. Not cost per token. Not the number of agents deployed or workflows automated. Because the cheapest AI isn't necessarily the most economical AI.
An agent that costs slightly more but resolves more customer issues, prevents a production problem, catches an important exception earlier, or helps someone make a materially better decision can deliver dramatically better economics. The question shouldn't be "How much did the AI cost to run?" It should be "What did it cost us to achieve the outcome?"
From one deployment to a flywheel
There's another reason the FDE model becomes powerful in enterprise AI. One customer deployment shouldn't remain a one-off implementation. Every deployment should teach us something — and that learning can become a reusable agent, tool, workflow, evaluation, guardrail, connector, piece of domain knowledge, or platform capability.
This creates the real flywheel:
Deploy → Learn → Codify → Reuse → Scale
The first deployment solves a problem. Production teaches us what actually works. That learning gets codified. What gets codified becomes reusable. And what becomes reusable makes the next deployment faster and stronger. That is the moment FDE stops looking like consulting or implementation and becomes an operating model for continuously turning enterprise problems into production AI capabilities.
Where the learning has to live
But a flywheel only turns if the learning has somewhere durable to land and the hardest thing to capture is also the most valuable: context.
Most links in that execution chain have an obvious home. Data sits in systems. Models sit in a registry. Agents and tools sit in code. Context, the meaning, the relationships, the rules, the behaviour around the record is the one that tends to live in people's heads and leak away when the deployment ends.
So context needs a home of its own. Not a data warehouse, which stores the data. Not a catalogue, which indexes metadata so people can find it. Not the model, which does the reasoning. It needs a layer where the meaning of the enterprise is captured once and kept live: the entities and how they relate, the definitions the business agrees on, the lineage of where facts come from, the governance rules that constrain them, and the domain knowledge that turns a raw record into something an agent can act on responsibly.
When context has that home, everything above compounds. Reuse stops being a bin of disconnected parts and becomes cumulative understanding, an estate that knows more about itself after each deployment. Cost per outcome falls, not because the AI got cheaper, but because the context got reused. And the governance questions should the agent act, what can it access finally have somewhere to be answered at runtime, without a human standing behind every agent.
A home for context
This is the problem we set out to solve at SCIKIQ. We have built a home for context.
Rather than asking an enterprise to move its data before it can make it intelligent, we connect to data where it already lives and build a unified, governed context layer on top of it: Connect → Govern → Contextualise → Ask AI → Decide. The meaning, the relationships, and the rules are captured once and kept live, so the context an FDE team learns in production doesn't walk out of the door with them.
Two choices make that home practical rather than theoretical. It's built to capture context without a migration, so the layer sits over the systems you already run instead of demanding you rebuild them first. And it's built to be encoded largely without code, which matters more than it sounds, because the people who actually hold the context are the domain and business experts, not only the engineers. Give them a way to put meaning into the system directly, keep that layer governed, and an agent can finally be trusted to reason and act against it.
That is what turns the flywheel from an idea into an operating model. Every deployment deposits context into a place where it compounds. Every agent stands on ground the enterprise already understands. And the next outcome costs less because the context behind it is already there.
Where this goes next
I believe this distinction will matter more and more as enterprises move from experimenting with AI to putting it into real business processes.
The winners may not be the organisations with the most agents. They may be the ones that become exceptionally good at taking a real problem, understanding its context, deploying AI safely around it, measuring the outcome, and turning what they learn into something reusable.
Build close to the problem. Learn from production. Turn the learning into the platform and give that learning a home, because the form it takes is context, and context is what your enterprise, and every agent you point at it, will stand on. For us, that isn't a feature of the platform. Context is the product.
