The concept of artificial intelligence (AI) is not new. The term, coined nearly 70 years ago, has led to computer scientists building programs to emulate human intelligence ever since. Now, engineers are building autonomous generative AI agents, or agentic AI.
The dividing line between generative AI and agentic AI generally boils down to proactiveness, independence and contextual awareness. Where generative AI is centered on tasks, agentic AI has the power to carry out granular, automated tasks, respond to dynamic environments on its own and invoke other interconnected systems as needed. Human oversight is still needed to ensure safety, efficacy and accuracy.
In learning and development (L&D), this includes course content creation and curation, enhanced skills gap analysis, advanced, specific coaching agents and administrative task automation.
What Could L&D Agents Look Like?
Agents now are most effective with focused goals, but their ability to interact with each other and form a wider multiagent system means companies can utilize them in a variety of ways. Agents can range from suggestion tools to actual content creators or strategic analysts to total human resources (HR) consultants.
From the perspective of an L&D provider, an agent may monitor customer feedback data and weigh it against research to suggest product planning corrections or reinforce a current roadmap. As an autonomous tool, it’s not something that needs to constantly be asked to do this — its whole purpose is to continuously pursue this one goal, perhaps programmed to update its human manager every week with its decisions and reasoning.
An agent could operate in a similar capacity for the learner, too. Having been trained on internal planning documents, policies and initiatives, an agent could monitor teams’ skills development against business goals and suggest new focuses for the most efficient use of time, the most effective learning path and the greatest chance of meeting those goals.
These are objectively “smaller” directives, but what could a larger agent system accomplish? If these two examples were a part of a system, perhaps once these proposed changes have been approved by a human manager, they would cue another agent tasked with actual course planning. This agent would have at least been trained on general educational best-practice documents and be up to date on suggested topics, able to develop a pathway that considers the previous agents’ reasoning and recommendations.
Again, the most efficient and effective agents are goal-oriented and streamlined, passing their work around a network of agents to reduce inaccuracies, so this learning plan agent may stop there. The human manager comes in, approves the plan and the next agent, this one specializing in actual content creation, starts designing what will eventually become a brand-new course that is sent out to human employees.
This whole multiagent system and systems like it in talent development, ultimately ensure teams are learning and gaining experience in the most relevant skills for their roles and goals, personalized to company policy and strategy and, eventually, to each individual’s learning style.
Extending Agentic AI to Workforce Planning
Beyond training, CHROs and their teams can build systems for workforce planning, training agents in their organizational goals and policies and wider talent best practices.
Other uses for agentic AI in broader talent management functions:
- Tracking employee skills and readiness
- Anticipating future workforce needs
- Authoring job postings aligned to internal growth
- Summarizing and assessing candidate resumes
With each potential company goal, like product innovation, brand awareness, or market capture, a multiagent system dedicated to that workforce’s skills taxonomy would be able to tell if they had the talent needed for success and develop HR strategies and content to reach it.
As with every AI tool, human interaction and intervention should be possible and encouraged throughout the process; its goal should still be to enable human employees.
How to Make the Most of These Agents
Agentic AI, as it is now, thrives on a “separation of concerns,” or a more defined goal, like the example of a suggestion tool as a smaller part of a greater system. The most effective systems thrive on clear workflows. They’re also constantly learning, as human workers should be, so they are not a “set-it-and-forget-it” technology. AI agents must be upskilled, too, in the same way their human counterparts must keep pace with changing business priorities, company and legislative policy and new technologies and skills.
It won’t be long until products and software across industries release their own agentic AI integrations, for both internal and external users. The focus on both AI-centered skills and irreplicable human skills increases even more with agentic AI. Things like prompt engineering, responsible use and compliance become more important as more powerful AI tools become available. On the other hand, skills like critical and strategic thinking, technological literacy and interpersonal communication are key to interpreting outputs, gauging accuracy and recognizing potential use cases. Before engaging with or building an AI agent, workers and organizations should hold a high level of these integral skills for the greatest chance of success and possibility to upskill their teams accordingly.
The rise of agentic AI in learning and development signifies a promising evolution in how organizations can optimize their workflows, tailor educational pathways and enhance employee skills. By leveraging the potential of these agents and interconnected multiagent systems, companies can create a dynamic, responsive and efficient learning environment. However, the successful implementation of these technologies hinges on the continuous interplay between human insight and AI capabilities. As agentic AI systems become more sophisticated, their role as enablers rather than replacements for human workers will be crucial. Embracing this technology while fostering critical human skills will pave the way for a more innovative and productive future in talent development.
