Agentic AI introduces a different kind of enterprise technology challenge. A foundation model can generate an impressive response, but an enterprise agent must do much more. It needs to understand business context, access the right data, use approved tools, follow permissions, work across enterprise systems, handle exceptions, and operate within workflows people already depend on.
When something goes wrong, teams also need to understand what happened, why it happened, and how to recover.
That makes moving an agent from a controlled pilot to a production environment a fundamentally different engineering challenge.
Across enterprises, Agentic AI roadmaps increasingly focus on models, platforms, and projected ROI. But moving an agent from a promising proof of concept to a reliable production system introduces a different challenge: engineering.
Forward-Deployed Engineering helps close that gap by embedding specialized engineers with enterprise teams to build, integrate, test, and operationalize AI within the environment where it needs to work.
If you're evaluating Agentic AI for your organization, this article explores how Forward Deployed Engineering can help move AI beyond the pilot stage.
Key Takeaways
- Only 23% of organizations are actively scaling an AI agent in production; despite 62% experimenting (McKinsey), the model is only one part of the challenge; engineering often becomes the bottleneck when AI moves into production.
- Agentic AI implementations most often break on tool/permission scoping, observability, failure recovery, data readiness, and enterprise integration, not model quality.
- Forward Deployed Engineering (FDE) embeds specialized engineers directly inside an enterprise's environment to build, integrate, and operationalize AI, rather than just recommending what to build.
- FDE is designed to transfer capability to internal teams, not create long-term dependency on outside engineers.
The Agentic AI Adoption Gap Is Sometimes an Engineering Problem
The market is investing heavily in agentic AI, but adoption and production readiness are not moving at the same pace.
McKinsey's 2025 State of AI survey found that 62% of organizations were experimenting with or piloting AI agents, while 23% reported scaling an agentic AI system somewhere in their enterprise. (McKinsey, via Forbes).
Lenovo's 2026 CIO Playbook, based on IDC research, found that nearly half (46%) of AI proofs of concept had progressed into production. At the same time, Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls.
These findings describe different measures and populations, so they should not be treated as directly comparable benchmarks. Together, however, they illustrate the broader implementation challenge: organizations can experiment with AI relatively quickly, while scaling systems that operate reliably inside enterprise environments requires substantially more work.
Where Agentic AI Implementations Break in Production
Agentic AI production readiness typically struggle not because the model cannot generate an answer, but because the surrounding enterprise systems cannot safely support the agent's actions.
Moving an agent from a controlled POC into a live enterprise environment introduces several engineering challenges that model capability alone cannot solve.
- Tool & Permission Scoping
Agents need access to the right tools, data, and actions without unnecessary privileges. Engineers must define permission boundaries, control sensitive operations, and ensure a single agent error does not create broader business impact.
- Observability Into Agent Decisions
Production teams need visibility into how an agent behaves. That can include what the agent attempted, what data it accessed, which tools it used, how a workflow progressed, and where human intervention occurred. Without adequate visibility, diagnosing failures, evaluating performance, auditing activity, and improving the system become significantly harder.
- Recovery From Partial Failures
Agents can encounter failed API calls, rejected transactions, unavailable systems, unexpected responses, or incomplete workflow execution. Production architectures therefore need controlled transaction boundaries, appropriate retries, recovery mechanisms, monitoring, and human escalation paths. These controls help prevent a partial failure from leaving a business process in an unknown state.
- Data Readiness
Agent performance depends on the data it can access and interpret. Fragmented sources, inconsistent records, poor data quality, and unclear access patterns can limit an otherwise capable agent. Production deployment requires trusted data sources, appropriate data pipelines, usable knowledge sources, and well-defined access and integration patterns.
- Enterprise Integration
Agents must work with the systems businesses already depend on, from ERP and CRM platforms to ITSM, identity systems, data platforms, and legacy applications. Secure authentication, authorization, API management, logging, rate limits, error handling, and workflow integration can become core parts of the implementation.
The model provides intelligence. The surrounding engineering determines how that intelligence is connected to enterprise systems, controlled, observed, and operated.
The Execution Gap: From AI Capability to Enterprise Execution
Once organizations identify these implementation challenges, another issue often becomes visible: the specialized engineering capacity required to solve them.
An agentic AI initiative may require AI and GenAI engineers, data engineers, integration specialists, cloud engineers, application engineers, security teams, ServiceNow specialists, and enterprise architects.
Hiring every capability permanently takes time. Traditional consulting can help define strategy and recommendations without necessarily providing the engineering capacity required for implementation. Staff augmentation can add individual skills without necessarily bringing together the disciplines required to solve the complete production problem.
Meanwhile, internal engineering teams are often responsible for maintaining the systems, workflows, applications, and platforms that the AI initiative depends on.
This creates a need for specialized engineering capacity that can work directly within the environment, address implementation problems as they emerge, and work alongside the teams that will ultimately own the solution.
That is where Forward Deployed Engineering fits.
What Is Forward Deployed Engineering?
Forward Deployed Engineering (FDE) is an embedded engineering model where specialized engineers work alongside enterprise teams to build, integrate, and operationalize AI solutions within their existing technology environment.
The distinction matters.
Forward Deployed Engineers do not simply tell an organization what it should build. They work inside the environment where the problem exists. Depending on the initiative, that can mean working alongside:
- AI and ML teams
- Data engineers
- Cloud engineers
- Enterprise architects
- Integration teams
- Security teams
- ServiceNow teams
- Application engineers
- Business stakeholders
The objective is straightforward: move the initiative from an identified engineering problem to a production-ready enterprise AI solution.
This is especially relevant when an organization already has an AI platform or proof of concept but lacks the specialized engineering capacity required to deliver it.
How Forward Deployed Engineers Address the Execution Gap
When specialized engineers work within the enterprise environment, implementation becomes part of the engagement rather than a step that happens afterward.
- No handoff gap: Engineers work directly within your environment, catching integration, permission, data, and workflow issues as they arise instead of after the fact.
- Faster time to value: Specialized engineers focus on the highest-priority production challenges, helping move proven AI initiatives forward without lengthy handoffs between strategy and implementation.
- Built-in trust and governance: Security, observability, governance, integration, and recovery requirements are addressed as part of the engineering work, not treated as afterthoughts.
- Capability Stays with Your Team: FDE engineers work alongside internal teams, share knowledge, and document implementation decisions so your people can operate and extend what is built.
What Changes When an FDE Role Works from Inside Enterprise?
When engineers work within the enterprise environment, execution becomes part of the engagement rather than a step that happens afterward. They see the systems, dependencies, constraints, and decisions shaping the initiative firsthand, allowing them to address issues as they emerge and keep the work moving forward. Here’s what that changes:
- From Advice to Implementation
Traditional engagements may identify what needs to happen next. FDE teams work alongside your engineers to make it happen.
Instead of simply documenting an integration gap, a forward deployed engineer works through integration. Instead of recommending governance controls, they help implement technical controls. Instead of identifying data-quality issues, they work with data owners to resolve what's blocking the use case. The distance between finding the problem and solving the problem becomes much smaller.
- From POC Success to Production Readiness
A POC can operate under controlled conditions. Production cannot. FDE specialists work with the constraints that appear when AI encounters real enterprise data and legacy systems, existing permissions and security requirements, production APIs, real users and compliance requirements, and operational support models.
That changes the engineering question from "Can this AI solution work?" to "Can this AI solution operate reliably here?"
- From Isolated AI to Connected Enterprise Systems
An agent becomes more useful when it can work within the systems and workflows that run the business.
FDE teams help connect AI to enterprise applications, data sources, APIs, and workflows so agents can retrieve information, take approved actions, verify results, and escalate when human intervention is required.
- From External Expertise to Internal Capability
A successful FDE model should not create permanent dependency. Embedded engineers work alongside internal teams, document implementation decisions, share knowledge, and help establish the practices needed to maintain and extend the solution.
The goal is not simply to deliver an implementation; it is to leave the enterprise better equipped to operate what was built.
Where FDE Fits Among AI Delivery Models
Forward Deployed Engineering does not replace every other delivery model. It serves a different purpose depending on where an organization is in its AI journey.
| Delivery Model | Primary Role | Typical Focus | Where It Fits |
|---|---|---|---|
| Strategy Consulting | Define direction | Strategy, roadmap, operating model | When the organization needs to determine what to pursue and how to approach it |
| Staff Augmentation | Add capacity | Individual skills and resources | When an internal team needs additional engineering resources |
| Systems Integration | Connect systems | Platforms, applications, integrations | When the primary requirement is connecting enterprise technologies |
| Internal Engineering | Own delivery | Product and platform development | When the organization has the required capabilities and capacity internally |
| Forward Deployed Engineering | Embedded execution | Build, integrate, operationalize | When specialized engineering capabilities need to work directly within an active enterprise initiative |
FDE is particularly relevant when an organization needs specialized engineering capabilities embedded directly into an initiative and coordinated around a defined production outcome.
What an FDE Team May Bring to an Agentic AI Initiative
A production AI initiative rarely needs one type of engineer. Depending on the implementation challenge, an embedded team may bring together specialized disciplines to address the engineering requirements that emerge as an agent moves toward production.
| Where implementations break | Discipline that addresses it | Responsibility |
|---|---|---|
| Tool & permission scoping | AI & GenAI Engineering | Define agent tools, evaluation methods, access boundaries, and model or agent integrations. |
| Observability | AI & GenAI Engineering | Implement agent monitoring, evaluation, tracing, and visibility into tool and workflow execution. |
| Enterprise integration | Integration Engineering | Connect APIs, enterprise applications, identity systems, and workflows; implement authentication, authorization, and error handling. |
| Data readiness | Data & AI Analytics | Prepare data pipelines, knowledge sources, data quality processes, analytics, and evaluation data. |
| Recovery & production infrastructure | Cloud & Platform Engineering | Build deployment environments, monitoring, reliability patterns, recovery mechanisms, and operational infrastructure. |
| Business-experience gaps | Application Engineering | Adapt applications, user experiences, and workflow interfaces to support AI-enabled processes. |
| ServiceNow-centric workflows | ServiceNow AI Engineering | Implement AI and agentic workflows, integrations, data connections, and automation within ServiceNow processes. |
The exact team composition depends on the AI initiative. The model should be designed around the engineering challenge, not a predefined staffing package. Different disciplines can be brought together as needed to build, integrate, test, and prepare the solution for production.
What Should an Enterprise Look for in an FDE Partner?
The right FDE partner should do more than add engineers to an AI initiative. The partner should be able to connect specialized capabilities to the technical and operational requirements of the environment.
1. Enterprise Engineering Depth
Look for broad enterprise engineering expertise beyond AI, including integration, data, cloud, applications, security, and platform engineering.
2. Experience With Complex Environments
Enterprise environments come with established processes, technology constraints, and competing priorities. Look for an FDE delivery partner that can adapt to the way your organization operates rather than expecting the environment to adapt to the engagement.
3. Depth That Matches Your Initiative
AI initiatives can change as requirements become clearer. Look for access to varied engineering expertise that can be brought in when the initiative requires it.
4. Engineering-Led Delivery
The engineers should be able to work through technical decisions, resolve blockers, and remain accountable for implementation rather than stopping at recommendations.
5. Flexible Team Composition
FDE teams should be structured around the initiative rather than a fixed staffing package. Team composition and engineering disciplines can evolve as the work moves from discovery through integration and production.
Why V-Soft for Forward Deployed Engineering?
V-Soft's Forward Deployed Engineering approach brings specialized engineers into the enterprise environment to help move AI initiatives toward production. The approach is designed around the engineering gaps that can emerge between an AI proof of concept and a production-ready solution.
- Embedded Engineering: Addresses the capacity gap by placing specialized engineers alongside the teams responsible for the initiative.
- Enterprise Integration: Addresses integration challenges by connecting AI capabilities to existing applications, data, APIs, identity systems, and workflows.
- Production Readiness: Addresses the gap between POC success and reliable production operation, with security, observability, governance, reliability, and recovery considered from the start.
- Knowledge Transfer: Reduces long-term dependency by working alongside internal teams, documenting implementation decisions, and transferring knowledge throughout the engagement.
This is not simply about adding more people to an AI project. It is about bringing the right engineering capabilities to the point where an AI initiative needs to move from experimentation to enterprise execution.
"The work changes when AI meets the real enterprise environment. You have to understand the systems, permissions, integrations, data, and business processes around it. That is where embedded engineering helps turn a promising AI solution into one that is ready to operate."
Move Your Agentic AI Initiative Towards Execution
Agentic AI changes the expectations placed on enterprise AI. The technology is increasingly expected to interact with enterprise systems, work within business processes, and perform real tasks. The challenge is therefore not simply selecting a capable model. It is engineering the AI into the environment where the business operates.
If your organization already has an AI initiative, platform, or promising proof of concept but needs additional engineering capacity to move it toward production, Forward Deployed Engineering can help close that execution gap.
Talk to V-Soft's Forward Deployed Engineering team about the engineering capabilities required to integrate, operationalize, and scale your AI initiative.
