Demystifying Agentic AI For Higher Education Professionals

Welcome to the world of agentic AI—where intelligent, collaborative systems are transforming higher education operations.

Generation AI hosts JC Bonilla and Ardis Kadiu explore the evolution of agentic AI and its transformative potential for higher education. The two most recent episodes break down the concept of agentic systems — AI that reasons, adapts, and acts — to demonstrate how this technology is reshaping student engagement, marketing strategies, and institutional operations. 

Whether you're familiar with AI or just starting to explore its applications in higher ed, this podcast series provides actionable insights on the frameworks and real-world examples of how multi-agent collaboration can create smarter, more efficient systems.

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Core Concepts to Know:

• Agentic AI Frameworks: Agentic AI represents a shift from traditional generative AI by integrating reasoning, adaptation, and action. These systems don't just respond—they execute tasks dynamically, leveraging context and tools to achieve measurable goals.
• Agents vs. Assistants: Agents are generic AI building blocks, while assistants are specialized agents tailored to specific higher education roles, such as admissions advising or financial aid counseling. Assistants leverage vertical solutions for hyper-specialization and faster ROI.
• Multi-Agent Collaboration: Multi-agent systems orchestrate complex workflows by delegating tasks to multiple specialized agents. This approach mimics real-world organizational structures and accelerates task execution.
• Practical Use Cases: From automating marketing campaigns to creating multi-step student engagement workflows, agentic systems unlock scalability and personalization for institutions.
• Measuring ROI in Multi-Agent Systems: AI impact can be assessed through three lenses: performance comparison (AI vs. human productivity), financial outcomes (cost savings, revenue generation), and organizational productivity (redefining benchmarks and workflows).
• Human-AI Synergy: In multi-agent systems, humans play a vital role in oversight, strategic input, and connection—focusing on high-touch interactions while AI handles repetitive tasks at scale.

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What is agentic AI, and how is it different from generative AI?

JC explains that agentic AI builds on generative AI’s ability to create content by adding reasoning, adaptability, and action. Instead of just generating responses, agentic systems use tools, memory, and context to dynamically solve problems and execute workflows.

What are agents and assistants in the context of agentic AI?

• AI Agents: Generic AI systems that perform specific tasks using reasoning, tools, and context.
• AI Assistants: Role-specific agents tailored to higher education domains (e.g., admissions, financial aid). Assistants are pre-configured with higher ed expertise, enabling faster deployment and higher impact.

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What is multi-agent collaboration?

Multi-agent collaboration refers to multiple AI agents working together to achieve complex goals. For example, a campaign manager agent might orchestrate tasks like content creation, segmentation, and email automation by delegating to specialized agents for copywriting, design, and analytics.

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How does agentic AI improve higher education workflows?

Agentic systems streamline repetitive tasks like application status checks, campaign planning, and student outreach. They allow institutions to scale operations, improve personalization, and free up human resources for strategic and high-touch activities.

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How do we measure ROI for agentic systems?

JC highlights three ways to measure ROI:

1. Performance Comparison: Evaluate AI’s output accuracy and speed against human benchmarks.
2. Financial Outcomes: Track cost savings, revenue growth, and productivity gains.
3. Productivity Metrics: Measure the increase in organizational capacity (e.g., more students reached, faster application processing).

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Why is verticalization critical for agentic AI success?

According to Ardis, vertical solutions are built specifically for higher education, allowing agents to act quickly and effectively. Unlike horizontal AI systems, which serve general industries, vertical AI focuses on the nuances of academic calendars, student records, and institutional workflows.

• Horizontal AI: The Generalist Approach

Horizontal AI systems are designed to serve a broad range of industries and use cases. They provide generic tools and platforms that can be applied to various sectors—such as retail, healthcare, education, and finance. The advantage of horizontal AI is its flexibility: organizations can customize these systems to fit their specific needs.

For example, a horizontal AI platform like ChatGPT can write emails, generate reports, or answer customer inquiries for any industry. However, its effectiveness in higher education may be limited without customization. Horizontal AI systems typically require significant manual effort to configure, as they lack the built-in knowledge of sector-specific workflows, terminology, and nuances.

• Vertical AI: The Specialist Approach

In contrast, vertical AI is built specifically for a single industry, such as higher education. These solutions are pre-configured to understand the unique challenges, processes, and language of that sector. Vertical AI systems are deeply integrated with industry-specific tools and data, such as Student Information Systems (SIS), Customer Relationship Management (CRM) platforms, and academic calendars.

For instance, a vertical AI solution designed for higher ed would natively understand concepts like academic years, semester breaks, application statuses, and enrollment trends. It could seamlessly interact with student records to personalize outreach or manage financial aid inquiries without requiring extensive customization. This targeted focus allows vertical AI to act faster, deliver better results, and achieve quicker ROI compared to horizontal solutions.

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What role do humans play in agentic systems?

Humans focus on strategic oversight, high-touch interactions, and managing AI workflows. They become orchestrators, leveraging AI’s capabilities to enhance productivity and personalization while ensuring outputs align with institutional goals.

Both JC and Ardis emphasize that humans remain critical to these systems. While AI handles repetitive tasks at scale, humans focus on strategic oversight, creative decision-making, and high-touch engagement—amplifying their unique abilities with AI’s support.

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To sum it up, agentic AI isn’t just an enhancement to existing tools—it’s a reimagination of how higher education operates. By leveraging multi-agent systems and domain-specific assistants, institutions can scale their impact, improve student experiences, and optimize workflows.

Ready to see how agentic AI can transform your institution? Dive into this two-part series and hear how Element451 is pioneering agentic AI solutions for higher education.

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FAQ: 

Q1: What makes agentic AI different from traditional AI systems?
Agentic AI can reason, adapt, and act autonomously, allowing it to execute complex workflows dynamically rather than relying solely on predefined instructions.

Q2: How does multi-agent collaboration benefit higher education institutions?
Multi-agent systems enable specialized AI assistants to work together, streamlining tasks like student outreach, campaign creation, and application management to boost efficiency and personalization.

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