The Human Element

You’ve likely heard the buzzwords “agent” and “agentic” more times than you can count this year. Agents are everywhere—from customer-facing AI agents to sales operations agents.

Why are AI agents abuzz right now, and why should you care?

AI agents are reshaping the future of work by automating tasks and complex workflows, improving decision-making, and augmenting human skills. For leaders interested in HR automation, understanding how agents work and their potential impact on HR disciplines is critical to staying competitive.

The basics: what is an AI agent?

First of all, there is no universally-agreed-upon definition of “agent.”

As Bill Gates describes on his blog, AI agents are evolving into proactive partners that anticipate needs before we even ask:

“Agents are smarter. They’re proactive—capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions.”

It’s clear that AI agents don’t just analyze data or make recommendations—they take action and augment human decision-making. Whether it’s scheduling meetings, surfacing insights, or executing routine tasks, these agents work autonomously and can potentially free up HR teams to focus on the big picture. They can help people draft performance reviews, analyze engagement trends, structure career development plans, and automate follow-ups, making HR more strategic and efficient.

Consider what that could mean for HR. Instead of getting bogged down in manual processes, leaders can spend their time on high-impact work that requires human involvement: workforce planning, skills mapping, succession strategies, and organizational design. The result? A more strategic, agile HR function that’s equipped to drive business success.

What does “AI agent” mean for HR?

In the world of HR, AI-powered agents can step in as fully embedded members of an HR team, executing tasks including:

• HR policy-related tasks including administering policy and automating compliance like time and attendance management, return-to-office or hybrid work policies, leave of absence management, benefits questions, and more.
• Tasks related to HR-led programs, such as performance management. With AI-driven insights, real-time observability, and improved quality of feedback, HR can help managers spot issues before they escalate.
• Talent acquisition tasks such as scheduling interviews or sending follow-up emails requesting feedback on candidate experiences.
• Tasks that require synthesizing data including generating candidate summaries, performance review templates, or onboarding checklists with speed and accuracy.
• Tasks that require personalization, such as coming up with training and development programs tailored to employees' specific needs and wants.
• Analysis-related tasks like identifying top talent or suggesting retention strategies.

How are AI agents different from copilots, machine learning, genAI, and LLMs?

It’s easy to get overwhelmed by AI terminology. Let’s break it down using an HR example: automating performance reviews.

AI agents act autonomously, reasoning and planning before making decisions on behalf of a person or team. Imagine an AI-powered performance review agent that not only gathers 360-degree feedback but also analyzes trends and suggests tailored development plans for each employee—handling the entire process while surfacing insights to the HR team.

• A subcategory of agents, copilots can collaborate with you in real time by augmenting your decision-making process. If an HR leader is reviewing an employee’s performance, a copilot could surface relevant feedback, compare it to past reviews, and suggest phrasing for constructive coaching points—helping refine the evaluation without taking over the task entirely.

Machine learning is the discipline of training algorithms to learn from data and improve over time. It is different from artificial intelligence, though people often confuse the two. Stanford Business refers to machine learning as a step beyond computing “when a computer finds patterns or correlations in data and figures out an unexpected solution on its own.” In the performance review process, an ML-powered tool could detect inconsistencies in feedback trends and flag them to HR, ensuring fairer evaluations across the organization.

Large language models (LLMs) power AI tools by generating human-like language. It’s the foundation for agents and copilots, making them both intelligent and able to converse with humans in a natural way. An HR leader could ask an LLM, “How can I give constructive feedback to a high performer who struggles with delegation?” The model would generate a thoughtful, data-backed response, helping managers navigate nuanced conversations effectively.

Generative AI (GenAI) is an application that uses an LLM to predict words, thus generating responses. GenAI creates new content based on patterns in large datasets. GenAI could help managers draft personalized performance summaries that synthesize feedback, highlight achievements, and suggest areas for growth, saving hours of manual writing.

What role do LLMs play in AI agents?

Large language models (LLMs) serve as the backbone for many AI agents. AI agents are built atop LLMs.

LLMs make agents smarter, more intuitive, and better at understanding people, which in turn helps HR teams provide faster, more personalized support. Here are some ways LLMs power AI agents:

• Understanding and interpreting language: LLMs allow agents to process and respond to natural language inputs, such as employee questions about policies, benefits, or processes, in a way that feels seamless.
• Generating conversational responses: They help create engaging and accurate conversational outputs, whether for chatbots, virtual assistants, or other employee-facing tools, ensuring communication is clear and helpful.
• Text data analysis: By analyzing vast amounts of text, like employee feedback, survey results, or performance reviews, LLMs uncover trends, patterns, and actionable insights, empowering better decision-making.
• Processing power: LLMs understand and extract information from unstructured data at a scale beyond what humans are capable of.
• Enhancing intuition and effectiveness: LLMs amplify the capabilities of AI agents, making them more conversational, intuitive, and effective in delivering value to HR teams and the broader organization.

Not all LLMs or their uses are created equal. While some agents rely on a sole off-the-shelf LLM, other agents like Wisq’s use multiple LLMs to take advantage of the strengths of each and use specially-tuned domain-specific algorithms to refine queries and improve reasoning.

What is an AI worker or an AI teammate?

An AI worker or AI teammate is simply an agent fully embedded on a team and integrated into a team’s workflow. It functions as a member of a team that is configured to play a defined role.

AI workers or teammates represent a shift toward hybrid teams, where digital agents and human employees collaborate seamlessly. AI teammates aren’t just tools—they’re on their way to becoming important contributors to the workforce, helping companies scale, innovate, and adapt.At Davos this year, Marc Benioff predicted a turning point in the workforce, saying that the current group of executives would be the last to oversee entirely human teams. Dario Amodei, CEO of Anthropic, said he predicts that by 2026 or 2027, “we will have AI systems that are broadly better than almost all humans at almost all things.” We aren’t there just yet, but it’s clear that AI agents will play an important part in the future of work.

The Evolution of AI in the Workplace

AI teammates represent a significant shift from traditional workplace automation. While earlier technologies focused on automating repetitive tasks, modern AI teammates can handle complex cognitive work, understand context and specifics to your company and organization, participate in creative processes, and adapt to changing situations. They're designed to complement and expand human capabilities rather than replace them.

What are key characteristics of an AI teammate?

• AI teammates are integrated into existing workflows and systems. They're available 24/7, can simultaneously assist multiple team members, and maintain consistent performance levels. Unlike traditional tools, they can understand context, have long-term memory, and apply learned patterns to new situations. They don’t just help with the work; they do some of the work.
• AI teammates can specialize in roles or industries, like humans. They can be trained on company-specific data, procedures, and best practices, making them valuable repositories of institutional knowledge while continuously improving their performance through interactions.
• They collaborate with people. They excel at tasks such as data analysis, pattern recognition, and information synthesis, while humans provide creativity, emotional intelligence, and strategic thinking. This partnership creates a more robust and versatile team dynamic.

AI teammates will shift how we think about work and teams. Success will depend on thoughtful implementation of AI teammates, clear communication, and a commitment to maintaining the human element in increasingly human-AI hybrid workplaces.

What does “artifact” mean in the context of AI?

Let’s dig a little deeper into artifacts, a term you might have heard in conversations about agents. In the context of AI, an artifact is the output from an AI system. Usually, it’s in the form of a document. Think of a meeting summary with action items, a report following an employee engagement survey, or a career development plan created after analyzing an employee’s skill gaps and growth opportunities. Artifacts organize complex information into a digestible format, making it easier to reflect on, share, and later, make decisions.

AI is only as effective as the data it learns from. Thoughtful inputs lead to more valuable insights and recommendations. For example, if an employee provides a detailed self-assessment for their performance review, an AI agent can generate a highly personalized development plan. On the other hand, a vague or rushed response may lead to more general recommendations.

These are the types of artifacts you might expect from an AI agent for HR:

1. Report or summary: Automated summaries of large datasets, meeting notes, or interview analyses that could include citations of relevant regulations and policies. Example: A leave of absence case might include a reference to the eligibility requirements outlined in FMLA.
2. Predictive insights: Forecasts or recommendations, such as identifying skill gaps or suggesting ideal internal candidates for promotion. Example: AI analyzes engagement and performance data to recommend employees who may benefit from upskilling or leadership development programs.
3. Visualizations: Graphs, charts, or dashboards. Example: A skills gap dashboard that visualizes the competencies your workforce needs versus what’s available internally.
4. Generated content: Articles, training modules, or onboarding guides created based on company needs. Example: An AI-personalized onboarding plan for new hires, tailored to their role, location, and manager.
5. Contextualized templates: Forms or frameworks, made with contributions from agents and humans, that help facilitate tasks like performance reviews, project plans, or feedback sessions. Example: A performance review template that dynamically adjusts questions based on an employee’s role and tenure.

Why HR and AI agents are a winning combination

AI agents are becoming an essential part of HR teams, automating workflows and surfacing insights that drive better decision-making. Unlike other AI tools, agents can act autonomously while seamlessly collaborating with human team members by enhancing and not replacing their work.

But great HR isn’t just about efficiency. It’s about people. The future isn’t AI versus humans; it’s AI and humans working together. By combining AI’s data-processing power with human creativity and strategic thinking, HR teams can focus on what matters most: building high-performing, engaged organizations.

The key is thoughtful implementation. When HR leaders provide AI agents with the right inputs and guardrails, they unlock their full potential: creating a smarter, more strategic, and more impactful HR function. The combination of AI and HR is truly a powerful force.