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By Saravanan Balasundaram, Founder & CEO, Han Digital Solution
As enterprises move from deploying AI to managing autonomous AI agents, a new engineering capability is beginning to take shape, and it could redefine what technology teams look like over the next few years.
Not too long ago, the industry was focused on Prompt Engineers, professionals who helped users interact more effectively with AI models. More recently, the rise of Forward Deployed Engineers (FDEs) marked another important shift. Embedded within client organizations, these professionals build, customize, and deploy AI solutions, helping enterprises move AI from experimentation to real-world business applications.
As AI continues to evolve, we are witnessing yet another defining shift. The next frontier is no longer about writing better prompts or deploying AI faster. It is about building AI systems that can operate independently, while interacting with models, evaluating outcomes, and deciding what to do next without waiting for human intervention, all while doing so reliably, responsibly, and at scale. The industry has begun to call this Loop Engineering.
The term is new, but the problem behind it isn’t.
Enterprises today have access to powerful AI models, and the challenge is no longer simply building AI capabilities; it is making them work effectively inside complex business environments. AI systems need to interact with enterprise workflows, collaborate with other AI agents, validate outcomes, recover from failures, and understand when human intervention is required.
The next phase of enterprise AI will move beyond individual assistants that help employees summarise documents or generate code. We are moving towards a world where multiple AI agents will collaborate across software development, cybersecurity, finance and other core business operations.
As enterprise AI moves from individual applications to interconnected systems of autonomous agents, the nature of software engineering work itself is changing. The focus is shifting from building AI capabilities in isolation to designing frameworks, workflows and guardrails that allow multiple AI systems to operate together reliably at scale. And the evolution of AI talent is also following this progression.
Unlike earlier AI roles, Loop Engineering will not be defined by one specialized skill. It will require professionals who can combine AI engineering with systems thinking, workflow design, business context and governance.
And if we look more closely, several shifts are accelerating this transition.
With India’s Global Capability Centres (GCCs) becoming ownership centers for products, platforms and business-critical innovation, this shift is changing the talent equation too; thereby creating demand for engineers who can combine AI expertise with enterprise engineering capabilities across areas such as platform architecture, data engineering, automation, MLOps, and business workflows. The GCC workforce of the future will not just execute technology roadmaps created elsewhere; it will increasingly shape products, platforms and innovations that drive global businesses. This is reflected in hiring patterns, with nearly two-thirds of new GCC roles requiring GenAI, data engineering or intelligent automation capabilities.
This demand is no longer confined to Global Capability Centres (GCCs). IT services firms of every size are redesigning their delivery models around agentic AI to stay ahead of evolving client expectations, while AI-native startups are building orchestration platforms, agent frameworks and tools that make Loop Engineering possible at scale. Across GCCs, IT services firms, platform companies, and AI startups, one talent need is becoming increasingly clear: engineers who can operate above the prompt layer by designing, orchestrating, and continuously optimizing intelligent AI systems.
Loop Engineering capability itself is still nascent. In India, the currently countable pool of engineers who can design and operate these agentic loops is small. Still, with upskilling programs targeting this gap, that pool is expected to cross 10,000 professionals in 2-3years, as customers move from single-shot AI deployments to the next stage of always-on, agentic systems.
At the same time, enterprises cannot build the next generation of AI systems by starting from scratch. Platforms such as SAP, Oracle, Microsoft, Pega, ServiceNow, and Guidewire continue to power critical business processes across industries. However, the challenge is that talent combining deep enterprise platform expertise with AI capabilities has not scaled at the same pace as adoption. Therefore, the next generation of technology professionals will need to bridge this gap, bringing AI into enterprise applications and creating intelligent workflows that deliver measurable outcomes.
The growing complexity of enterprise AI is also accelerating the need for stronger governance, automation, security and responsible adoption. As organizations deploy AI systems across critical business functions, they will need professionals who can build the right controls around data, identity, compliance and decision-making. India’s evolving data protection landscape will further reinforce this need as enterprises focus on deploying AI systems that are not only efficient, but also trusted and accountable.
Given that India’s technology services model is shifting from delivering just outcomes to delivering global value, the value of talent will increasingly lie in designing intelligent systems, solving complex business problems and creating measurable impact. This is precisely where the next generation of AI engineers, including Loop Engineers, will create value.
Whether the industry eventually calls this capability Loop Engineering, AI Orchestration or something else is secondary. What matters is that enterprise AI is entering a new phase, and organizations that build adaptable talent ecosystems will be better positioned to lead it.
The half-life of technology skills is shrinking faster than job titles can keep pace. A role that does not exist today can become mission-critical tomorrow. And while we know that India’s advantage will not come from chasing every new AI title after it emerges, but will come from building a workforce and delivery ecosystem capable of adapting before the next role even has a name.
