Most HR teams are already using AI in some form. The question has shifted from “should we adopt AI?” to “which kind of AI actually solves the problem we’re looking at?”
That’s where the distinction between generative AI and agentic AI starts to matter. Generative AI is the one most people have already used. You give it a prompt, it gives you a draft. Job descriptions, policy summaries, interview questions. It’s fast, it’s useful, and it’s reactive. It does nothing until you ask.
Agentic AI works differently. It watches what’s happening across your HR systems, spots patterns you might miss, and surfaces recommendations before you have to go looking. A manager doesn’t need to run a report to find out engagement is dropping in their team. The system flags it and suggests a next step.
The adoption data reflects how fast this shift is moving. McKinsey’s 2025 State of AI survey found that 88% of organizations are now using AI in at least one business function. Within HR specifically, Gartner reports that AI adoption climbed from 19% in 2023 to 61% by 2025.
And looking ahead, Gartner projects that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from under 5% just two years earlier.
This article walks through where each type of AI fits in HR, how they differ in practice, and how Engagedly has built both into a single platform through Marissa AI and its multi-agent Agentic Framework.
What Is Generative AI in HR?
Generative AI refers to software that produces content by recognizing patterns in training data. In HR, it works like a writing assistant. You give it a prompt, it gives you a draft. Think job descriptions, interview questions, policy summaries, or onboarding checklists.
Think of It As: A Prompt-Driven Assistant
It doesn’t take initiative. It waits for you to lead. Tools like ChatGPT are effective at understanding prompts and returning structured, readable content, but they stop there. They won’t flag a problem you didn’t ask about.
The real value shows up when you’re buried in repetitive work. Writing the same types of documents, summarizing long texts, generating FAQ answers for new hires. Bain & Company found that some HR teams have reduced their admin workload by 15 to 20% using these tools.
Where It Actually Helps
- Drafting job descriptions, internal memos, or policy documents so you’re not starting from scratch
- Generating rough review templates based on a role’s responsibilities
- Suggesting interview questions once you share the role and skill requirements
- Condensing long reports or policy documents into shorter summaries
- Putting together basic training outlines without hours of manual effort
Where It Falls Short
Generative AI doesn’t understand your company culture. It can’t adapt to how your team communicates, and it doesn’t know whether the feedback style at your organization is direct or diplomatic.
It depends entirely on what you tell it. Vague prompts produce vague output. And anything it generates still needs a human to review it, adjust the tone, check for accuracy, and make sure it fits the context.
For example, during onboarding, a generative AI tool might produce a checklist or draft a welcome message. But it won’t match the way your team actually talks. You’ll still need to rewrite it.
How Engagedly Uses Generative AI
Engagedly’s Marissa AI handles several generative AI tasks natively inside the platform. Instead of switching to an external tool, HR teams can:
- Generate job descriptions with role-specific requirements and responsibilities, all within the Engagedly interface
- Draft feedback using the SBI framework (Situation, Behavior, Impact), so managers don’t have to structure their feedback from scratch
- Create personalized praise and recognition messages by telling Marissa why an employee deserves recognition, and she generates the message in seconds
- Auto-generate OKRs and goals aligned with company priorities, just by entering a prompt
- Build Individual Development Plans (IDPs) with milestones, using persuasive and actionable language
- Summarize survey and Team Pulse responses, including open-ended answers, into concise insights
- Generate course descriptions, learning module summaries, and onboarding content without leaving the platform
The difference between using Marissa for this versus a standalone tool like ChatGPT is context. Marissa is trained on Engagedly’s own modules and HR best practices, so the output is already aligned to how the platform works. There’s no copy-pasting between tools.
Understanding Agentic AI in HR
Agentic AI goes beyond reacting to prompts. It’s designed to understand your goals, take independent action, and learn from its own results. In HR, that means moving from basic automation to systems that adjust based on what’s actually happening across your workforce.
Think of It As: A Decision-Support Partner That Adapts Over Time
Agentic AI doesn’t sit idle. It actively scans data from multiple systems, things like performance review scores, engagement survey trends, learning completion rates, and goal progress, and calls out what matters before you have to go looking for it.
The adoption numbers reflect where this is heading. CHROs project a 327% growth in agent adoption by 2027, according to Salesforce. Currently, over 45% of global leaders are already using AI agents for HR functions, with another 39% planning to adopt them soon. And Deloitte predicts that by 2027, half of the companies using generative AI will have launched agentic AI applications that can perform complex work with limited oversight.
What Sets It Apart
There are a few things that separate agentic AI from generative AI in practice:
- It works toward business objectives without needing specific prompts for every action
- It spots patterns and anomalies across your HR data and takes initiative, flagging risks or recommending actions
- It pulls context from multiple systems, including performance tools, L&D platforms, engagement dashboards, and payroll data, to build a complete picture before recommending a next step
How Agentic AI Shows Up in Real Workflows
The value of agentic AI is most visible in everyday HR routines. If engagement scores drop for a particular team, an agentic system can flag it and recommend that the manager schedule a check-in, without anyone filing a ticket or running a report.
It can also support development planning by pulling together someone’s past performance, current goals, and skill gaps to suggest a learning path that actually makes sense for that person, not just a generic recommendation.
How Engagedly’s Agentic AI Works
Engagedly launched its Agentic AI framework in March 2025, with Marissa AI serving as the coordinating “Super Agent” at the center. This wasn’t a bolt-on feature. The framework uses a multi-agent architecture where specialized AI agents handle different HR functions, and Marissa orchestrates the entire system.
Here’s how it actually works:
The Super Agent (Marissa AI) is the single point of contact. Whether you’re asking through a chat window or voice interface, Marissa receives your query in natural language, figures out which specialized agent should handle it, and routes the request behind the scenes. She stays with the conversation, asks follow-up questions if something is unclear, and delivers the response.
Specialized Agents operate underneath Marissa, each focused on a specific area:
- Goals Agent: Helps employees write better goals by learning from what has worked well in the organization previously. It suggests targets that are realistic but challenging, aligned with company objectives, and tracks progress in real time.
- Learning Agent: Recommends personalized learning paths based on an employee’s role, performance gaps, and career aspirations. When someone sets a development goal, the agent suggests relevant courses and connects them with mentors who already have strength in those areas.
- Talent Review Agent: Provides HR with data-driven insights for workforce planning, including identifying skill gaps and high-potential employees. It assesses the talent pool and suggests development paths based on actual performance data, not just manager nominations.
- Career Planning Agent: Helps employees identify growth opportunities and plan career moves based on their skills, interests, and what the organization needs.
- HR Helpdesk Agent: Answers employee queries by pulling from the company’s HR knowledge base. You can train Marissa with your own HR policies and documents, so it gives answers specific to your organization rather than generic responses.
What makes this agentic (not just generative): The system doesn’t wait for someone to ask. It monitors engagement trends, performance data, goal progress, and feedback patterns across the platform. When it identifies something that needs attention, say a pattern of declining participation in a department or a high-potential employee whose growth has stalled, it surfaces it proactively with a recommended action.
Engagedly also acquired HiringTool.co in May 2025, a GenAI-driven applicant tracking system, and integrated it into this framework. That means the agentic system now covers the full talent lifecycle, from hiring and onboarding through performance, learning, engagement, and retention, all under one roof.
Then in September 2025, Engagedly acquired Butterfly.ai, a frontline engagement analytics platform, and integrated with Deel for global payroll and HR data sync. These additions give Marissa’s agents even more data to work with, creating a more complete view of the workforce.
The result: a platform where AI doesn’t just respond to what you type. It watches what’s happening across your workforce, connects the dots, and tells you what needs your attention before it becomes a problem.
Agentic AI vs Generative AI in HR: A Side-by-Side Comparison
| Feature | Generative AI | Agentic AI |
|---|---|---|
| Input Dependency | Works only when given specific prompts | Understands goals and works toward them without detailed instructions |
| Initiative | Waits for input and responds | Recognizes issues and acts on them proactively |
| Learning | Built from training data; doesn’t update after deployment | Learns continuously by reviewing results and adjusting actions |
| HR Use | Generates content: templates, descriptions, summaries | Drives decisions by connecting insights across systems and recommending next steps |
| Data Scope | Works with whatever you paste into the prompt | Pulls context from performance reviews, engagement surveys, L&D, goals, and payroll data simultaneously |
| Example | Writes a draft performance review when asked | Spots a pattern of declining engagement and suggests coaching or a development plan before the problem escalates |
| Engagedly Example | Marissa generates SBI-based feedback, OKRs, job descriptions, and IDP milestones | Marissa’s specialized agents monitor workforce signals, flag risks, and recommend interventions across the full talent lifecycle |
Real-World Use Cases Across the HR Lifecycle
Generative AI in HR
1. Engagedly’s Marissa AI for Content Generation
Inside Engagedly, Marissa handles the content generation layer directly. HR teams use it to draft 360-degree feedback summaries, generate course descriptions for the LXP, write onboarding materials, and create survey questions. The advantage over a standalone tool is that Marissa is already connected to the platform’s data, so the output fits the context without manual adjustment.
2. ADP’s AI Digital Assistant
ADP built a virtual HR assistant to field everyday employee questions about time-off policies, benefit details, and payroll basics. It reduces the volume of repetitive queries hitting the HR team, freeing them up for work that requires judgment.
3. UBS’s Analyst Avatars
UBS introduced AI avatars that replicate how their analysts communicate. These tools break down dense training material into shorter, more practical points. It’s not a replacement for in-depth learning, but it makes the initial intake of information less overwhelming.
Agentic AI in HR
1. Engagedly’s Multi-Agent Framework in Action
Engagedly’s agentic system does more than any single use case. Here’s how it plays out across the HR lifecycle:
- Performance management: The Goals Agent monitors goal progress across the organization and flags when teams are falling behind or when individual targets need adjustment. Marissa suggests interventions based on patterns, not just deadlines.
- Engagement: The platform’s sentiment analysis runs continuously, identifying departments or teams where engagement is trending downward. Instead of waiting for the next quarterly survey, it alerts managers with specific recommendations.
- Learning and development: When an employee sets a development goal, the Learning Agent doesn’t just assign a generic course. It recommends specific learning paths, connects them with mentors who have strength in those areas, and tracks whether the learning is actually translating into performance improvement.
- Talent reviews and succession: The Talent Review Agent surfaces insights about skill gaps and high-potential employees, pulling from performance data, feedback history, and goal achievement. HR leaders get a clearer picture of their talent pipeline without running separate reports.
- Hiring: With the HiringTool.co acquisition now integrated, the agentic system extends into recruitment, using semantic matching and candidate analysis to surface the best fits, not just keyword matches.
- Frontline engagement: Through the Butterfly.ai acquisition and the EngagedlyFX (frontline.engagedly.com) platform, the agentic capabilities extend to deskless and frontline workers, a segment that traditional HR tech often misses.
2. Moderna’s Cross-Functional AI Use
Moderna combined its HR and IT departments to create a unified data approach. By linking these systems, HR can view feedback, performance, and engagement data in one place. This integration lets them spot what’s working or where someone needs support, so they can step in sooner.
3. Decidr and CareerOne in Recruitment
Decidr’s collaboration with CareerOne introduced agentic AI to job matching. By analyzing user profiles, preferences, and behaviors, the system provides more accurate job recommendations. The approach improved placement accuracy, especially during the initial stages of hiring.
When to Use Generative AI vs. Agentic AI
The choice depends on the problem you’re solving.
Use generative AI when:
- You need to draft something standard: a policy, a job listing, a feedback template
- You want to send communications at scale, like internal updates or onboarding emails, without customizing every message by hand
- You’re building training materials or course descriptions and need a solid starting point
Use agentic AI when:
- You want to spot a drop in engagement before it leads to turnover
- You need development plans that reflect actual performance data and career trajectories, not just generic templates
- You’re running talent reviews and need insights pulled from multiple data sources automatically
- You want continuous monitoring of workforce health, not just point-in-time snapshots
The strongest approach, and what Engagedly has built toward, uses both together. Marissa AI handles the generative layer (content creation, drafting, summarization) while the underlying agentic framework (specialized agents for goals, learning, performance, talent review, and hiring) handles the strategic, data-driven layer.
Looking at the Numbers: Why This Matters in 2026
The shift from generative to agentic AI isn’t theoretical anymore. Here’s where things stand:
- 61% of HR organizations have adopted AI in some form, up from 19% in 2023 (Gartner)
- 40% of enterprise applications are expected to include embedded AI agents by the end of 2026 (Gartner)
- 45% of global leaders are already using AI agents for HR functions, with another 39% planning to adopt soon
- 327% growth in agent adoption is projected by 2027 by CHROs surveyed by Salesforce
- Deloitte predicts that by 2027, half of companies using generative AI will have launched agentic AI applications that handle complex work with limited oversight
- Only 11% of enterprises currently run AI agents in production despite 79% having adopted them in some form (Deloitte), meaning the gap between experimentation and deployment is where the real competitive advantage sits right now
For HR teams, the takeaway is clear: generative AI has already proven its value for content and admin tasks. Agentic AI is where the next wave of impact comes from, and it’s moving from pilot programs to production deployments rapidly.
Wrapping Up
AI has already reduced a lot of the pressure on HR teams. Writing gets done faster. Processes feel lighter. Repetitive communication can be handled with fewer bottlenecks. That’s largely the generative AI layer doing its job.
But the real shift happens when AI moves from creating content on demand to actively monitoring your workforce and recommending actions before problems compound. That’s what agentic AI adds.
Engagedly has built both into a single platform. Marissa AI handles generative tasks like feedback drafting, OKR generation, and survey summarization. The Agentic Framework underneath, with its specialized agents for goals, learning, talent review, career planning, and hiring, does the heavier strategic work: pulling together context from across the platform, identifying patterns, and surfacing the actions that matter most.
The result is an HR platform that doesn’t just wait for you to ask the right question. It tells you what you should be paying attention to.
FAQs
1. What’s the practical difference between generative AI and agentic AI in HR?
Generative AI works off prompts. You ask it to write a job description or summarize a report, and it produces the content. Agentic AI operates continuously in the background. It monitors data from engagement surveys, performance reviews, goal tracking, and other systems, then surfaces patterns and recommends actions without being asked.
2. Can both types of AI work together in one platform?
Yes. Engagedly’s Marissa AI is a good example of this. The generative layer handles content creation, like drafting feedback using the SBI framework, generating goals, and summarizing 360-degree reviews. The agentic layer, built on a multi-agent framework, handles strategic monitoring and decision support across performance, learning, engagement, talent review, and hiring.
3. What does Engagedly’s Agentic AI Framework actually include?
The framework uses a Super Agent (Marissa AI) that coordinates multiple specialized agents. These include agents for goals, learning, talent review, career planning, and an HR helpdesk. Each agent is an expert in its domain and handles queries or tasks autonomously within that area, while Marissa manages the overall conversation and routes requests to the right specialist.
4. How is Engagedly’s approach different from using a standalone AI tool like ChatGPT for HR?
ChatGPT and similar tools require you to provide all context manually through prompts, and they have no connection to your HR data. Marissa AI is embedded directly in Engagedly’s platform, pulling from performance reviews, engagement surveys, goal data, learning records, and more. That means it can provide context-aware recommendations and generate content that’s already aligned to your organizational data, without copy-pasting information between tools.
5. What recent developments have expanded Engagedly’s AI capabilities?
In 2025, Engagedly launched the Agentic AI framework with Marissa as the Super Agent (March 2025), acquired HiringTool.co to bring GenAI-driven recruitment into the platform (May 2025), acquired Butterfly.ai for frontline engagement analytics (September 2025), integrated with Deel for global payroll sync (September 2025), and won Gold at the Brandon Hall Excellence in Technology Awards for Best Advance in an Integrated Talent Management Platform (December 2025).

































