Recruitment professionals are constantly bombarded with new technological terms and promises of AI solutions that will solve all their hiring challenges. Yet many are left wondering what actually works and what's just marketing hype.
One term you've likely heard is "Agentic AI." But what exactly does it mean, and how is it different from the AI tools you may already be using?
In a recent webinar, "Smart Tech, Smart Hires: How Science Drives Daxtra Technology," our CTO Don Tomlinson and VP of Customer Success Colleen Barraclough broke down the science behind AI in recruitment technology. They explained what makes truly intelligent systems different from basic automation that's simply labeled as "AI."
This blog post distills key insights from that discussion to help you understand what Agentic AI really is, how it’s impacting recruitment search technology, and why it all matters for your daily work.
In this post:
AI has evolved dramatically over the decades. From simple Boolean searches to keyword matching to semantic understanding, recruitment technology has made significant leaps in functionality. But Agentic AI represents something fundamentally different.
Don Tomlinson explains: "Agentic AI has the ability to act on behalf of users. It's proactive, it's context aware, and it's continuously improving through feedback loops."
Unlike basic automation tools that follow predefined rules, Agentic AI can:
Many products claim to use AI when they're really just automated tools following simple rules. This practice, called "AI washing," can make it difficult to identify solutions with genuine AI capabilities.
Here's how to tell the difference:
Automation:
True AI:
Let's test your understanding with a few practical examples. Are these hypothetical tools leveraging Agentic AI, or using basic automation? (Answers below)
Answers:
Recruitment search technology has come a long way in the last 20 years. Colleen Barraclough, VP of Customer Success, has seen this firsthand:
"After 25 years in recruiting and 10 years at Daxtra, I've watched our industry evolve through multiple generations of search technology,” she said. “What we're seeing now with vector embeddings and Agentic AI feels very different. It's the breakthrough we've been waiting for."
Previous generations of search technology can be broken down into three categories:
Boolean Search
Keyword Search
Semantic Search
This path has led us to the latest iteration of search technology: conversational search.
Conversational Search
The science behind modern AI recruitment tools involves a concept called vector embeddings. Don Tomlinson explains this with a helpful visualization:
"Imagine every candidate resume represented by a pin on a world map. The east-west position might represent work experience, while north-south represents skills. Resumes with similar skills and experience would be clustered close together on this map."
Vector embeddings work by:
Keyword matching relies on exactly matching keywords, while vector matching matches similar content in a 3-dimensional space.
This approach means a search for "software engineer" can find relevant "full stack developers" even if those exact words don't appear, because their vector representations are similar.
You’ve probably seen this tech at work in your day-to-day life - for instance, it’s similar to how Netflix recommends movies. As Colleen puts it:
"Netflix uses vector embeddings for their movies and everything associated with them. They've assigned numbers to various movies, so when you watch one, it becomes the input query with all its associated numbers. When it makes suggestions to you, it's using vector embeddings to present that information back,” she said.
When looking at how accurate these systems are, Daxtra uses multiple metrics:
Agentic AI and vector embeddings are transforming recruitment workflows in several ways:
Conversational Search
Instead of building complex Boolean strings, recruiters can simply describe what they're looking for: "I need a software engineer in Orlando with 5 years of Java experience in financial services, and no job-hopping."
Intelligent Candidate Matching
The system understands that a Java developer with AWS experience might be an excellent match for a "backend engineer" role, even without exact terminology matches.
Automated Screening
An Agentic AI can create custom screening questions based on gaps between a resume and job description, then conduct the screening automatically.
Candidate Summaries and Explanations
AI can generate summaries of why a candidate scored well for a position, making the matching process transparent and explainable.
Don Tomlinson emphasized the importance of explainability: "When people ask me when should I trust AI and when should I not, for me, I can trust AI when I can explain it. Part of that is giving each of you explainability within why we're a match."
The practical benefits of these technologies address common recruitment challenges:
In particular, these technologies excel at finding candidates who might have been overlooked by traditional methods. As Colleen pointed out, "We're able to bring candidates that meet your criteria, then also suggest candidates based off of what we know from a technological perspective with the AI that is going to be important to you."
For example, when searching for a VP of Engineering, conversational AI can understand the context of what you're looking for beyond just the job title. It might identify candidates who have the necessary experience but haven't held that exact title yet.
Vector embeddings and Agentic AI represent significant advancements in recruitment technology, but their real value comes from how they solve everyday problems for recruitment professionals.
The evolution from Boolean to keyword to semantic and now to conversational search reflects a growing sophistication in how we match people to opportunities. Each generation has built upon the last, with Agentic AI representing the most recent step forward.
For recruiters working with multilingual candidates, there's good news too. As Don Tomlinson explained: "A Java developer in English has basically the same numerical vectors as a Java developer in German, and it has the same vectors as a Java developer in French."
These technologies don't aim to replace human recruiters but rather to handle repetitive tasks and surface insights that might otherwise be missed. The goal is to let recruiters focus on what they do best: building relationships and making nuanced hiring decisions.
Understanding the difference between true AI and "AI washing" is crucial as you evaluate recruitment tools. By knowing what genuine Agentic AI can do, you can make better decisions about the technology you use and avoid solutions that promise AI capabilities but deliver only basic automation.
Whether you're sourcing candidates, screening applicants, or matching people to roles, these technologies can help you work more effectively while finding better matches for your open positions.