GenAI vs Agentic AI: The Skills Powering the Future of Work
As a recruiter working at the intersection of data, AI, and emerging technology, I’ve seen a clear shift in the type of talent businesses are looking for. We’re moving beyond just Generative AI (GenAI) into a new phase of capability: Agentic AI — systems that can autonomously plan, reason, and act to achieve goals. Understanding the skills behind these two AI streams is critical not just for hiring, but for shaping the future workforce.
In GenAI roles, the goal is to guide models to generate high-quality, human-like outputs. Skills in prompt engineering are key, enabling professionals to interact with models effectively to get the desired results. Just as important is data annotation and curation, which involves structuring and labelling datasets to fine-tune and evaluate model behaviour. There's a growing need for professionals who can validate AI-generated content — checking for bias, factual accuracy, and relevance. Industry-specific knowledge is also crucial; domain experts in healthcare, law, or finance are in high demand to help tailor GenAI applications to real-world use cases. These roles typically attract talent from backgrounds in data science, NLP, product design, and technical writing, often combined with a basic understanding of machine learning concepts like embeddings and transformer models.
Agentic AI, however, introduces a whole new level of complexity. These systems don’t just respond — they act. They integrate memory, tools, and APIs to autonomously complete tasks with minimal human input. The professionals building these solutions often come from software engineering, AI engineering, and machine learning backgrounds. Key skills include orchestration and architecture design, where agents are composed of multiple AI components working together across tasks. There’s strong demand for those with experience in reinforcement learning, planning algorithms, and memory management, particularly using vector databases for persistent context. Developers who can integrate agents with external tools and APIs using frameworks like LangChain, AutoGPT, or similar platforms are highly sought after. Importantly, these agents need to operate at scale, so DevOps and production-grade software engineering skills are essential.
From a hiring perspective, the distinction is clear: GenAI leans toward creativity, communication, and language-focused AI use, while Agentic AI requires deeper systems thinking, automation skills, and the ability to build self-directed agents. Both areas are growing fast, but they call for different mindsets and technical strengths.
For businesses preparing to scale their AI capability, or professionals thinking about where to upskill, understanding this difference will shape how you build, hire, and grow.