In a recent LinkedIn post, Hung Lee offers a detailed analysis of how AI agents are beginning to reshape the talent acquisition workflow, focusing on the newly launched EQO platform by VONQ. Lee emphasizes that the future isn’t about a single, all-encompassing AI, but rather a coordinated team of specialized agents.
The Rise of Specialized AI Agents
Hung Lee highlights a significant shift in the development of AI for hiring. Instead of a monolithic AI trying to perform all recruiter tasks, the emerging trend points towards a team of AI agents, each narrowly focused on a specific function within the hiring process. Lee observes:
“We’re not seeing a single, general purpose agent do everything a recruiter does. What we are seeing are specific, task orientated agents narrowly focused on one aspect of the role: sourcing candidates, interviewing candidates, scoring candidates working together”
This modular approach, according to Lee, allows for greater efficiency and precision in each stage of talent acquisition. Different agents can be optimized for tasks like initial candidate screening, in-depth interviewing, or candidate scoring, contributing to a more streamlined overall process.
Commercial Validation and Proof of Concept
A key aspect of VONQ’s strategy, as pointed out by Hung Lee, is their approach to market entry. By deploying EQO commercially before extensive public discussion, they have gathered tangible proof of its effectiveness.
Real-World Data Over Demos
Lee praises this strategy, noting its advantage over traditional demonstration-heavy launches. He states:
“VONQ had a smart launch strategy – deploy it commercially before actually talking about it! This means that instead of ‘trust me bro’ demos they actually have real case studies, performance data and signed off testimony from the likes of Deutsche Bank, Adecco, SoftwareOne.”
This focus on real-world case studies and performance data from major clients like Deutsche Bank and Adecco lends significant credibility to the AI agents’ capabilities, moving beyond theoretical potential to demonstrated results.
Shifting to Outcome-Based Payment Models
Perhaps the most striking observation from Hung Lee concerns the innovative business models emerging around these AI agents, particularly the move towards paying for outcomes rather than processes.
The CPA+ Model
Lee discusses the CPA+ model, where employers purchase bundles of AI-screened, interviewed, and scored job applicants. This represents a fundamental change in how companies engage with recruitment services.
“Instead of posting a job, wading through the deluge and hoping for the best, you just buy the results you want – 20 qualified and interested candidates for that tricky role? Ready to interview, they are already in your ATS.”
According to Hung Lee, this outcome-based approach shifts the focus from the effort involved in recruitment to the tangible results delivered – a pool of qualified, pre-vetted candidates ready for the next stage. This model, he suggests, is a significant indicator of the future direction of AI in talent acquisition, offering employers a more direct and efficient path to securing talent.
Lee concludes by recommending the launch video for those seriously considering the future of AI in TA, highlighting specific timestamps that showcase the candidate journey, commercial deployments, and the innovative CPA+ business model.
📝 About This Content
This article is based on insights shared by Hung Lee on LinkedIn.
📅 Originally posted on January 20, 2026 | View original post on LinkedIn →