Amid the buzz around AI, a new concept has taken center stage: the AI agent. Designed to autonomously plan and execute sequences of related activities, AI agents are suddenly everywhere, with companies large and small touting their agentic capabilities, roadmaps, and ambitions. From press releases to podcasts, the conversation has turned to agents that can plan our travel, manage our health, find us jobs, and handle countless other personal and professional tasks.

Beyond the hype, real progress is taking hold. Companies are piloting AI agents across a wide range of use cases—from enterprise security and supply chain management to drug discovery and military logistics.

Looking ahead, there is wide agreement that AI agents are poised to transform how we live and work—collaborating with one another to manage complex, time-consuming workflows—even those that may have once required entire teams. As the technology matures, AI agents are expected to enable organizations across nearly every domain to operate with unprecedented efficiency and adaptability.

At iseek.ai, we’ve been developing AI agents since long before it was trendy. In 2013, our parent company, Vantage Labs, trademarked Intelligent Agent® to describe LLMs operating in collaborative workflows—anticipating the value of AI systems that could think, sequence, and act.

Today, our suite of agents supports health professions education with intelligent, multi-step workflows that mirror how experts tackle complex tasks:

Curriculum Intelligent Agent™ makes academic content deeply searchable. It standardizes structured and unstructured materials, parses them down to the page or video timestamp, applies topic- and industry-based tagging, and generates intuitive facet filters—all automatically.

Analytics Intelligent Agent™ prepares programs for accreditation by executing an end-to-end analysis pipeline. It processes and quantifies content coverage, maps materials to competency frameworks, generates tailored accreditation reports, and formats them for direct submission—all from within the platform.

Student Progress Intelligent Agent™ gives educators deep visibility into student mastery. It tracks performance by licensing exam topics, flags individual learning gaps, maps progress to competencies, and surfaces opportunities for curriculum refinement or intervention.

Each agent goes beyond simple automation, handling sophisticated, sequenced tasks that free up faculty and administrators to focus on high-impact work.

As we expand into new sectors across education, government, and enterprise, we see immense opportunity for AI agents to bring clarity, structure, and autonomy to high-stakes, high-complexity environments. The era of AI agents is just beginning, and we’re proud to help shape its trajectory.

Over the past two weeks, we’ve explored what RAG is, its benefits, its applications for higher education, and how iseek.ai is innovating in this space. In this final post, we’ll bring it all together as we summarize the 7 ways iseek.ai’s RAG creates a superior LLM for professional and higher education.

1. Access to External Knowledge

iseek.ai’s RAG integrates large-scale retrieval systems into LLMs, enabling access to relevant information from external sources, such as a school’s proprietary curricular and assessment data, subscription databases, accreditation standards, competency-based education frameworks, and domain-specific ontologies. This augmentation ensures more accurate, context-specific responses tailored to a school’s needs.

2. Built-in Integrations

Through built-in integrations, iseek.ai provides off-the-shelf access to more than 30 platforms used widely in professional and higher education. The engine has extensive knowledge of the applications and the structure and type of data within them, as well as the necessary transformations to prepare the data for use as a knowledgebase for RAG LLMs.

In the first four posts of this series, we’ve covered a lot about Retrieval Augmented Generation (RAG)—how it works, why it matters, and the benefits it brings. Now, we’re shifting gears to show you how we’re taking RAG to the next level at iseek.ai. We’ve built a two-step retrieval process designed to deliver even more precise and relevant results.

Before the retrieval process even begins, iseek.ai converts source content—such as curricular materials or assessment data—into vector embeddings. We enhance these embeddings with domain-specific concepts, enabling the system to quickly group similar content or materials matching a particular topic-based query.

Step 1: The Search

The first step of the process is the search itself. iseek.ai starts by identifying the most relevant results based on the query. It creates a universe of content that’s deeply focused on a specific discipline, contextually appropriate, and closely aligned with the query and domain-specific needs.

We’ve had a few questions about how RAG compares to LLM fine-tuning. Here’s the breakdown:

How Are They Different?

Both RAG and LLM fine-tuning allow institutions to enhance their LLMs, but in different ways. Fine-tuning modifies an LLM’s internal parameters using additional training data. RAG, on the other hand, supplements the model’s internal memory with non-parametric data retrieved from external sources.

Which One Is Better?

Fine-tuning can work well for specific tasks that don’t need constant updates, but RAG is better when information changes frequently. For dynamic environments, RAG’s ability to retrieve up-to-date information in real time is a big advantage.