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Imagine this: you’ve got an AI workflow that technically works, but users are confused, trust is low, and adoption is stalling. The logic is sound, the data is clean, but something’s missing: clarity, context, confidence.
Enter the middleman between raw model output and real-world usability. They rewrite prompts, document edge cases, test with real data, and adjust the experience until it feels natural.
In agentic AI, these middlemen are called Forward Deployed Engineers, and without them, your AI stays stuck in demo mode.
We spoke with Barbara Neves, a Staff ML Engineer embedded as an FDE at Multimodal, to understand what the role really entails.
First Things First, What Is a Forward Deployed Engineer?
A Forward Deployed Engineer (FDE) is not just a software engineer.
Sitting at the intersection of technical implementation and business operations, the FDE role turns AI prototypes into production-ready agentic systems. That includes translating business logic into workflows, crafting prompts that produce understandable outputs, and tuning behavior until the system moves from merely functional to fully trusted.
Rather than working from the sidelines, FDEs embed directly within customer teams. This proximity lets them align model capabilities with day-to-day decision-making.
On one side, they work hands-on with orchestration tools and model APIs. On the other hand, they partner with domain experts, such as loan officers, underwriters, claims leads, to ensure outputs match business expectations.
That blend of technical fluency and operational empathy is rare. But it’s what makes the model work.
FDEs act as translators, editors, and QA leads rolled into one. They don’t just ship code, they make sure every agent response is contextualized, auditable, and useful in the real world.
So why do agentic AI deployments need FDEs in the first place?
Companies Need People Who Make AI Work… for Their People
Agentic AI systems often break down at the last mile.
The output is accurate but hard to interpret. Edge cases confuse users. Workflows fail under real-world data. These failures don’t reflect a lack of technical capability, they stem from a gap between system logic and user expectation.
FDEs bridge that gap.
They embed with frontline teams to understand how people actually work, what edge cases matter, and what signals build trust. They translate those insights into logic that the AI can follow, prompts that make sense, and interfaces that reduce friction.
That’s where FDEs like Barbara come in:
“We don’t just improve AI agents and workflows. We also help end-users and SMEs understand how they work, so they can trust the system and use it with confidence.”
AI only adds value if the humans using it believe it can help. And most companies and important institutions, especially in finance and insurance, lack the in-house talent to convert institutional knowledge into AI logic.
That’s why FDEs matter now more than ever.
This is not a hand-wavy transformation project. FDEs sit in the details. They work alongside analysts, underwriters, and claims handlers, iterating on what "good" looks like until the system consistently reflects it.
But don’t confuse them with consultants. As Barbara’s workday makes clear, FDEs are operational teammates, measured by outcomes, not slide decks.
How Forward Deployed Engineering Makes It Work
Barbara’s day rarely looks the same two days in a row, but it always balances three pillars:
Building (40%) – This is where code meets context. Barbara configures retrieval chains, tunes prompts, integrates APIs, and maps outputs to enterprise schemas. One day, she’s debugging an LLM hallucination in a claims workflow. The next, she’s connecting outputs to a downstream rules engine. She’s not optimizing toy demos, she’s building agents that move production work.
Collaborating (30%) – The “D” in FDE stands for “deployed” for a reason. Barbara sits with underwriters, risk officers, and claims leads to unpack how decisions actually get made. She joins standups with IT teams and reads the old SOP docs no one wants to admit are still in use. This context-gathering isn’t overhead, it’s the raw material for agentic design.
Testing + Tuning (30%) – Once workflows are wired, Barbara shifts into simulation mode. She throws messy, real-world data at the agent. When it breaks, she doesn’t panic, she surfaces the bug, identifies the logic gap, and patches iteratively. This fast loop is where AI becomes reliable.
But the job isn’t just technical. Barbara is also the one writing:
Agent explanations users actually understand
Internal spec docs that IT and compliance can review
Presentations to help execs understand the value delivered
If an agent output sounds clear, she wrote it. If the agent behaves consistently across inputs, she tested it. If a skeptical SME now trusts the system, she earned that trust.
“Even reordering the explanation of a model’s decision made users twice as likely to trust and accept the output.”
That insight didn’t come from a whitepaper, it came from observing users in the field. And it reflects the broader truth of forward deployed teams’ work: adoption isn’t a UX layer, it’s the work.
FDEs make the AI usable, believable, and eventually invisible.
Most domain experts, such as loan officers, underwriters, and analysts, know a dozen edge cases no spreadsheet has ever captured. That knowledge is invaluable, but unless someone captures and encodes it, AI can’t use it.
Barbara’s job is to find and codify that:
Working with SMEs to understand rare but important exceptions
Rewriting workflows to capture business logic
Creating live demos early to get real-time feedback
FDEs often identify logic gaps that even SMEs didn’t know existed:
“Sometimes, while developing, we even uncover edge cases that hadn’t been considered by SMEs or customers… which is always a fun and insightful moment.”
This kind of discovery isn’t accidental, it’s the result of structured collaboration, rapid iteration, and a healthy dose of curiosity.
In that sense, FDEs aren’t just engineers, they design systems. Their work ensures AI workflows reflect the tacit, undocumented logic that drives real decisions.
How a Forward Deployed Software Engineer Drives Business Value
The numbers speak for themselves:
Faster decisions – because agents are tuned to mirror how SMEs think
Fewer errors – because workflows are tested against real-world complexity
Less manual work – because edge cases are accounted for and automated
But metrics only scratch the surface. The true business value of a Forward Deployed Engineer is transformation without disruption.
Take Barbara’s deployment of Document AI for a high-volume process, around 300 documents per day. On paper, the impact was dramatic:
But those results didn’t come from a massive system overhaul or a slick new UI.
They came from weeks of working alongside SMEs, tuning the logic, refining prompts, and encoding decades of business nuance into something the model could reliably replicate. Barbara didn’t rebuild the process, yet she translated it into an agentic form.
This approach avoided the friction that often tanks enterprise AI rollouts: no new UI to learn, no IT migration risk, no disruption to compliance. Just outcomes.
And FDE-led impact doesn’t stop at go-live. That’s when the real work starts:
“We stay in touch with users. If something starts slipping, we jump in, investigate, and iterate. The goal is to treat deployment as the beginning, not the end.”
That post-deployment vigilance is what drives sustained ROI. It’s also what separates a POC from a production system. FDEs treat AI not as a product to ship, but as a system to steward.
Real AI Deployment Is Integration, Not Handoff
Enterprise success with AI isn’t about choosing the right tool, it’s about embedding that tool into the muscle memory of your operations.
The base platform wasn’t designed for AI researchers, it was built for real-world deployment in finance and insurance. It supports:
Modular orchestration – enabling complex workflows across multiple agents
Private infrastructure – with VPC, on-prem, or single-tenant SaaS options
Fine-grained observability – via confidence scores, audit logs, and field-level versioning
Compliance-by-default – with role-based access, policy-driven escalation, and traceable decisions
But tooling alone doesn’t ensure value; FDEs like Barbara do. They’re the connective tissue between platform capabilities and business outcomes.
They ensure the right fallback path gets triggered when confidence is low. They confirm that audit logs capture the exact fields needed by compliance. They work with IT to map the agent’s outputs into downstream systems, without adding brittle integrations.
The result?
Real agents instead of prototypes. Production systems, not demos. Systems that regulators can audit, SMEs can trust, and business leaders can scale.
Learn More About How Our FDEs Can Help
If you're exploring agentic AI and want to avoid yet another stalled pilot, book a demo with our experts.
We'll walk you through how an embedded Forward Deployed Engineer, paired with AgentFlow’s secure, modular platform, can turn prototypes into production workflows that actually move work forward.
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