Hiring Agents - a staffing & recruitment agency entirely operated by AI Agents 👥

CV Deep Dive

Today, we’re speaking with Jacob Claerhout, Co-Founder and CEO of Hiring Agents AI.

Hiring Agents is rethinking the entire hiring process by building a truly agentic staffing firm—one where AI agents handle everything from sourcing and screening to candidate outreach and interview scheduling. 

Unlike traditional recruiting models that rely on large teams of people, Hiring Agents operates with two core agents: Holly, who works on behalf of companies, and Hunter, who represents candidates. Together, they automate the hiring process end to end, making recruiting faster, more efficient, and more scalable. With a focus on enterprise hiring and VMS-driven workflows, Hiring Agents is building for the future of work—one where job applications as we know them may soon disappear. 

In this conversation, Jacob shares how they transitioned from an AI-first workflow to a fully agentic system, the technical and strategic challenges they overcame, and why he believes AI will fundamentally reshape the way people find and secure jobs.

Hiring Agents went live on ProductHunt earlier today - check out their launch here and explore their demo here.

Let’s dive in ⚡️

Read time: 8 mins

Our Chat with Jacob 💬

Jacob, welcome to Cerebral Valley! First off, introduce yourself and give us a bit of background on you and Hiring Agents AI. What led you to co-found Hiring Agents?

Hey there! I'm Jacob, CEO of Hiring Agents AI. A bit about my background - I’m an engineer by education and spent some time after college working at an early-stage startup—a dev tools company. Then I had the opportunity to become a VC for a bit, investing in early-stage startups with check sizes ranging from $200K to $2.5M. I backed some really cool founders, especially in the HR tech space. But the mission all along was to go out and build a business of my own, so that’s what I did. I teamed up with my co-founder, and we initially built something a bit crazy.

We launched a fantasy startup investment game infused with NFTs. It gained a lot of traction, made it into multiple newspapers, and had the whole startup world talking. It was a lot of fun, but apparently, the SEC deemed it illegal. So, as a two-person team who had built it in just a couple of months, we got a pretty insane call. Even though it was taking off, we refunded everyone and moved on.

That led us to the talent space. I had previously run a recruitment agency, and my co-founder had worked at DataCamp, a data science learning platform, so we both had experience in the space. We first started by building a marketplace and incorporating quality control elements. As ChatGPT came out, we began experimenting with large language models early on and quickly realized they would completely change our industry—not just our business model, but the entire sector.

Rather than waiting for change, we decided to be the ones driving it. That’s how we started building Hiring Agents AI. At first, we built productivity software for large staffing and recruitment companies. Then we pivoted and became a staffing firm ourselves. Today, we’re an AI-native staffing firm, built from the ground up to leverage AI in hiring.

How would you describe Hiring Agents to the uninitiated Founder or AI team?

Hiring Agents is essentially an AI-native staffing firm, meaning we handle both direct hire and contingent hires. Traditionally, placing people requires an army of staff—sourcers, interview schedulers, recruiters, and salespeople. We do all of that with AI agents.

We have two key agents. Holly is the company account manager, responsible for hiring on behalf of companies. Then we have Hunter, a career agent who works for candidates, engaging with them, making a case on their behalf, and submitting it to Holly. Essentially, we’re a staffing firm—without people.

Who are your users today? In a B2B2C model, who is capturing the most value from what you're building at Hiring Agents? 

Our customers used to be primarily staffing and recruitment companies, but now we serve large enterprises with high-volume hiring needs. That’s where we focus most of our efforts.

We specifically streamline one major way of submitting talent—through VMS (Vendor Management Systems). Large enterprises acquire talent through staffing partners who submit candidates via VMS, and that’s the space we operate in. But the cool part is that while we focus our sales efforts on these large companies, our AI agents don’t discriminate. We can just as easily work with mid-sized companies and even startups, making our solution flexible across different business sizes.

Companies who have VMS systems care about speed, quality, and consistency—and we’re really good at all three. Normally, humans have to do the grunt work, manually submitting candidates. It’s slow, inconsistent, and not always high quality. We can guarantee all of those things. Obviously, the aim is to do it much cheaper, but enterprises don’t necessarily care about that.

Walk us through Hiring Agents’ platform. What use-case should founders or companies experiment with first, and how easy is it for them to get started? 

I’d say that what makes Hiring Agents really exciting—and what I personally get hyped about—is that we’re truly AI agent-native. We have two agents with real agency on both sides: one for the company and one for the candidate. That means you don’t need to learn a new interface or platform to get started. Instead, you interact with them just like you would with any core service—your accountant, your lawyer, or your recruiter—through email. You can simply email Holly, and she’ll respond as if she were human, delivering results seamlessly. It’s pretty amazing.

Essentially, you just email Holly—each client gets a dedicated email address—and she’ll reply, asking how she can help. She’ll ask a few follow-up questions, sync with you on the position, and then start distributing the job posting. She’ll push it across multiple job boards, run social media campaigns to attract talent, tap into our existing database for potential fits, and send hyper-personalized outreach via InMail and email—all without you having to lift a finger.

We also vet candidates automatically to ensure they meet your criteria. When you instruct Holly on what you’re looking for, you can specify requirements like experience with Java or building consumer apps. Holly will check for these qualifications, and if a candidate applies through a job board, Instagram, or LinkedIn but doesn’t have all the necessary details on their resume, they’ll be onboarded by Hunter. Hunter’s job is to understand candidates better and advocate for their application. Hunter operates through text messages, WhatsApp, and phone calls. 

The second you apply, Hunter reaches out. It’s hyper-personalized—Hunter is an agent that will call you, ask how you’re doing, and say, “I saw you applied. Can I ask a few follow-up questions?” It will cover key details the company wants to know, like whether you have the right to work in the U.S. or if you have experience with consumer apps.

Hunter also asks if, in case this application doesn’t work out, we can help with other opportunities. This allows us to build a large candidate database and match people for future roles. If a candidate has a strong case, Hunter submits their full profile to Holly. Holly reviews it, and if she agrees the candidate is a good fit, she either adds them to the client’s ATS, submits them through the VMS, or emails the client directly with a full profile and reasoning on why they’re a great match.

The cool part is that Holly can be fully instructed on how you want her to operate. You can tell her when to send candidates, when to open and close roles, and how to prioritize submissions. She’s truly agentic—meaning she behaves just like a human hire would, following your instructions and adapting to your preferences.

Are there certain types of roles that Holly and Hunter are better at hiring for? For example, how do they assess between a technical role for a very advanced ML researcher versus someone who's in operations or in product?  

Great question. We focus on volume recruiting, not headhunting. If you’re looking for the Head of Research at OpenAI, that’s a headhunting job—too high-salary, with an applicant pool of maybe 10 or 15 people.

For us to be successful, we need applicant pools in the hundreds or even thousands. We specialize in IT staffing, which means we handle roles like full-stack developers, data engineers, QA, and front-end developers. But in general, we’re well-equipped for anything in tech.

We can acquire new candidates, but for every hiring mandate we get from a customer, we also grow our candidate database. 

We’ve seen a number of companies working on optimizing the recruiting pipeline using AI. What would you say sets Hiring Agents apart from a product or technical perspective? 

First off, our two-agent approach is a key differentiator. In the marketplace of the future AI agents will need to have one true owner. It’s really difficult to build agents that serve two parties at the same time when their interests sometimes conflict. That’s why we’ve structured our system with Holly working for the company and Hunter working for the candidate.

The second big differentiator is our focus on VMS and large enterprise hiring. Startup hiring is a very different beast from traditional staffing, and our expertise lies in large-scale hiring. That’s where we’ve been focused for the last 18 months to two years, and it’s what we do best.

We also have some really exciting data acquisition partnerships—which I can’t name—but they give us a major advantage in talent acquisition. In this business, you need to efficiently acquire both clients and candidates, and these partnerships help us do that at scale.

Could you share a little bit about how Hiring Agents actually works under the hood? What model architectures are you looking at most closely for your own systems?

It’s all of that, and that’s what makes this so exciting. I truly believe we’re one of the few companies—especially in the hiring space—that is genuinely AI-native. A lot of companies out there are just standard marketplaces with a thick AI flavored sauce on top, it is not the core of the bet they’re taking. You’re not really fooling anyone with that.

For us, AI is at the core. Our entire system is built around two agents who are equipped with tools and capabilities to operate independently. One of those capabilities is interacting with each other. Holly instructs Hunter, who then goes out and expands the candidate database. This means we function in the real world—Hunter can actively search for LinkedIn profiles and engage with them. He can manage marketing budgets on Meta and TikTok, create and personalize ads, and acquire candidates at scale. These are real agentic systems where judgment calls are made by Holly and Hunter, which is fascinating to watch in action.

Because of this, our system has more variability, but it also reduces the number of edge cases we need to build for. Take interview scheduling—there are entire companies built around solving that problem in the pre-AI era. For us, Hunter handles it out of the box. If a candidate doesn’t pick up, he’ll try again. If they don’t respond after two attempts, he’ll send a polite follow-up email.

What’s really cool is seeing how, by giving AI agents the right tools and framing their objectives clearly, they become incredibly versatile. Our architecture is built around two core agents that can interact with each other, utilize different tools, and operate in a multimodal way—they handle voice, process documents, and engage with the web. From a technology standpoint, it’s an incredibly exciting system to work on.

What has been the hardest technical challenge around building Hiring Agents into the platform it is today? 

Internally, I had to really push for the idea that AI agents should be the fundamental core of what we’re building. Even though it sounds like a detail, it’s easy to turn to predefined processes. Developers often default to certainty, by hard-coding the steps in process, but my stance was that we should lean into the agentic approach. That fight wasn’t easy. Back in the GPT-3.5 days, we built workflows that were unpredictable and slow. Our workflow had a lot of AI, but it was still very fixed.

The hardest part was transitioning from an AI-first workflow to a truly agentic infrastructure. But that shift is what puts us in a strong position to keep succeeding because these agents, at a foundational level, will only get better over time.

Another challenge was that candidates had some skepticism around interacting with AIs, especially in voice calls. We were waiting for real-time voice tech to improve, and now we’re excited to have that. We work with Vapi, for example, to help us with real-time voice capabilities. Transitioning from an AI-first workflow to a truly agentic system was a big and expensive push from a technical standpoint, but it was worth it.

How do you see Hiring Agents evolving over the next 6-12 months? Any specific product developments that your customers should be most excited about? 

For us, we’ve built these really cool agents, and obviously, it takes time to get everything right. But now it feels like we’re in an amazing position. Candidates enjoy the experience, clients make strong hirers, and we’re doubling down on improving it even further.

Right now, Hunter only engages with internal positions, but one of the big things we’re launching soon is expanding Hunter’s role beyond our network. That means Hunter will help candidates apply for jobs all over the web. One of my core bets is that traditional job applications will disappear sooner than people think.

Filling out a form and never hearing back will feel as archaic as writing and mailing a physical letter. In an agentic world, job applications won’t exist in their current form, and that will be a huge improvement. AI agents remove information asymmetries and enable much more precise matching. If the incentives are structured correctly, the agents representing companies and candidates can exchange information only when it makes sense for both parties—and they can do it with zero memory, which is incredibly exciting.

Right now, a lot of people don’t apply for jobs even if they’re open to new opportunities because they’re afraid someone will find out. Their resume gets stored in a database, and that creates unnecessary risk. That’s a broken system. It makes the talent market far less liquid than it could be. AI systems will fix this by making the job market more dynamic, helping people find better opportunities.

Even very smart people are notoriously bad at managing their own job searches. They struggle with the process. Soon, AI agents will handle it for them, and we’ll be leading the charge in reinventing that model.

Lastly, how would you describe the culture at Hiring Agents? Are you hiring, and what do you look for in prospective team members joining the Hiring Agents? 

Yes, we are growing the team. We’re looking for developers in San Francisco and staffing execs, though I’m guessing the audience here is more the former than the latter. We’re still a very small team, but we’re actively hiring, mainly engineers and salespeople.

That said, we have a high bar. We use our own software to attract talent, but there are a lot of moving parts, and our goal is to become one of those extremely valuable yet very small companies. So, we’re keeping the team as lean as possible while making sure we bring in the best people.

Conclusion

Stay up to date on the latest with Hiring Agents, learn more about them here.

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