The surge of AI agents is not only reshaping industries but also redefining job titles. Much like cloud computing gave birth to DevOps, the age of agentic AI is giving rise to new specialisations such as agent architects, agent orchestrators, and forward deployed engineers.
While some view these titles as rebranded extensions of existing roles, some argue they are distinct responses to the structural gaps in AI adoption.
“Traditional roles like ML engineers or data scientists are focused on building models, but today enterprises need talent that can design, deploy, and govern multi-agent systems that work like digital employees,” said Omkar Pandharkame, chief strategy officer at Supervity AI, a Virginia-based tech firm that offers AI Agents-as-a-Service.
Spin Off or New Roles?
Agent architects are emerging as the framework designers in this new AI landscape. They go beyond what DevOps or cloud leads once did, focusing instead on guardrails, orchestration patterns, and system-level design tailored to AI agents.
According to Pandharkame, the risks, learning curve, and operational models are “fundamentally new,” making the role distinct from anything before.
Agent orchestrators, on the other hand, take on the complexity of coordinating how multiple AI agents interact within workflows.
This requires not only technical fluency in retrieval-augmented generation and workflow design, but also the ability to ensure agents complement, rather than conflict, with each other. The result is smoother automation and a lower failure rate when AI agents move from proof of concept into production.
Bhaskarjit Sarmah, head of AI at Domyn, a composite AI platform to design, deploy, and orchestrate AI Agents, sees orchestrators as an extension of engineering roles but warns against underestimating their importance.
He told AIM: “These are the job roles now, of course… These are nothing, but it’s a kind of spinoff of an engineer.”
“Most of these job roles that exist can be grouped into three buckets.” he said.
“One is the bucket of AI research, [the second] is the bucket of AI engineers. [And the third] is the bucket of AI product managers.”
He explained that emerging roles, such as “agent orchestrator,” are essentially an extension of the AI engineer position. Individuals with existing engineering backgrounds can readily transition into these specialised AI roles.
While orchestrators may sit in the broader bucket of AI engineers, Sarmah noted, they reflect the immediate need for expertise in managing agentic complexity.
Technology jobs are being redefined at their core, said Neeti Sharma, CEO of TeamLease Digital, a Bangalore-based firm that offers specialised staffing & solutions across IT, ITeS, telecom and engineering, in an interaction with AIM.
“From building models or running IT systems to managing and connecting several AI tools, the skills required are going to be very different,” she explained.
Roles such as agent architect and agent orchestrator are expected to design flexible AI systems that enable efficient communication among different agents, and make large language models work well inside enterprises.
Sharma emphasised these are not just updates of older skills, but new responsibilities that require setting up and managing AI programs that can make autonomous decisions.
FDEs and AI Safety
If architects and orchestrators define and coordinate, Forward Deployed Engineers (FDEs) bring agent systems to life with speed.
Pandharkame described them as hybrid professionals who combine customer-facing skills with AI-first development. “An FDE is not a rebranded solutions engineer; they require deep understanding of business workflows and the agility to build AI agents in days, not months.”
Their responsibilities differ significantly from traditional developers. Instead of long product cycles, FDEs rapidly spin up demos, proving valuable in real-world settings. As Pandharkame put it, the role “sits at the intersection, one part consultant, one part generative AI engineer.” Companies like Supervity AI are aggressively hiring for these positions, viewing them as a natural evolution of pre-sales engineering.
Still, experts caution that these titles are not hype but stepping stones towards more critical specialisations.
“There was no AI safety engineer before, but now we need AI safety engineers,” Sarmah said, predicting that the role will be central as organisations confront risks in scaling AI.
Sharma from TeamLease Digital echoed this point, highlighting that the next three-to-five years will bring roles dedicated to ensuring AI follows rules and ethics, managing AI products comprehensively, and integrating AI with human processes.
“These are the new responsibilities enterprises will need to scale AI responsibly,” she said.
A Shifting Talent Landscape
These emerging roles illustrate the changing balance between engineering expertise and domain knowledge.
Pandharkame argued that hybrid talent is the winning formula: “Engineering expertise without domain knowledge builds brittle agents. Domain knowledge without engineering depth limits scalability.” This blend of skills is especially visible in FDEs, who must be as comfortable in a customer boardroom, as in a code editor.
While universities lag behind, in-house academies and bootcamps are filling the gap.
Enterprises are retraining existing talent pools with professional services and financial firms leading adoption. And the list of roles is still growing. Pandharkame anticipates positions such as AI employee success managers, agent security engineers, and autonomy auditors joining the mix within five years.
For now, the trio of architects, orchestrators, and forward-deployed engineers represent the early stages of work with agentic AI.
Like DevOps engineers a decade ago, they are likely to become mainstream fixtures of enterprise teams. Yet, as Sarmah’s comments remind, the most urgent need may lie in the roles that safeguard AI’s reliability, rather than merely extend its reach.
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