How Agentic AI Is Reshaping Recruiting — and How to Apply It Effectively

Unlike generative AI, which generates outputs based on user prompts, agentic AI can plan, act, and run processes autonomously without user input. If you think of generative AI as a single machine that can run one process at a time with a user pressing the buttons, agentic AI is like the conveyor belt that connects each of those machines into one, seamless process.

“The way I break it down is that you need to treat it like any other workflow,” says Matt Bradburn, founder of HR-AI counsultancy, PeoplexAI. “So, let’s say a workflow is a fixed, predefined sequence of queries to the AI with a known number of steps. 

“For example, I want to ask ChatGPT to analyze my candidate experience survey. Then, I want it to give me some clear indiciations of focus areas and a roadmap with actions. My next prompt might be to turn it into a report for the CEO and CFO. Then, I need it to rewrite that in my tone. It’s the same process I would do if I’m just the human sitting there with all of that data — but it turns 20 hours of labor into one.”

In simple terms, agentic AI means handing over all of those repeatable, manual steps from your workflow that you’d run through ChatGPT to an AI system that can run them end-to-end without your intervention.

When you strip the talent acquisition process right back to its bare bones, it becomes a series of interlinked, repeatable steps that often take up time and resources: sourcing candidates, screening applications, and generating offers. 

These processes are just part of the bread and butter of hiring. But agentic AI gives you a new way to optimize them for efficiency.

“These are the same issues that have been there for 15 years,” says Bradburn. “In TA, it’s always a case of too many applicants we don’t reply to, hiring managers not giving feedback… A lot of them come from busy work that can be easily automated. That’s where AI can make the biggest difference.”

The key part, says Bradburn, is finding where your processes connect up, and building systems that can take care of the repetitive, high-volume tasks. 

When implemented well, agentic AI can help talent teams create a better candidate experience, improve hiring speed and efficiency, and scale a customized, personalized hiring experience at scale. 

So, what can this actually look like in practice?

• – Hiring kits: Instead of the talent team manually drafting job descriptions and interview questions for each new role, they can create a custom GPT that enables hiring managers to run this process, says Bradburn. This then packages this information, generates the documents, and sends them via Slack for approval. 
• – Sourcing: Once TA and hiring managers have clear criteria for a role, an agent can continuously source talent from approved external sources and internal talent databases, compile a draft hiring slate, and draft outreach to be approved by a human.
• – Screening: An agent can review and screen applications, cross-references candidates according to your role rubric, and flags shortlists for recruiter validation. 
• – Scheduling: Agents manage diaries across interviewers, candidates, and time zones, identifying availability, scheduling meetings, and updating calendars.
• – Contracts and offers: Once a candidate accepts an offer, an AI agent can generate contracts, upload them, coordinate signatures from key stakeholders, file them, and update the HRIS to trigger an onboarding workflow.

Agentic AI is already showing promise when it comes to efficiency, resource allocation, cost reduction, and scalability. But like any new tech-driven approach in hiring, Bradburn says seizing this opportunity must come with a hint of caution.

Because if you hand over the keys to the kingdom and just let your agents run free, you could end up unintentionally jeopardizing proprietary or candidate data, or influencing decision-making. 

“TA teams need to be really careful how they integrate AI agents into their processes,” he says. “Because if you’re letting an autonomous system decide its own actions with your hiring data and personally identifiable information, then you’ll end up in data breach hell, fast.”

In other words, once candidate or organizational data starts moving between systems without a human in the loop, the risks can stack up quickly — leading to a whole host of ethical and compliance challenges that can inflict widespread reputational damage.

In addition to data privacy, TA teams also need to consider how their usage of agentic AI impacts internal policies around bias and hiring equity — and set out clear guidelines for exactly how and where agentic AI can be used. At a broader level, teams will also need to consider how this use fits in with organizational policies. 

The key thing, says Bradburn, is making sure that any AI agents you do implement aren’t in the driver’s seat for the decisions that matter most — where there could be human collateral damage. Like who gets into the interview room — and who ultimately gets the job.

Before plugging any agents into your processes, you have to start by framing the problems you’re trying to solve for your end users — because that’s where you’ll get the biggest bang for your buck.

“If we think about what we’re trying to do with agentic AI, it’s really that we’re trying to solve problems for the end users,” Bradburn says. “People-as-a-product thinking helps you rethink how you’re solving those challenges. In TA, your end users are candidates, hiring managers, and the business as a whole. Your decisions about where to implement agentic AI don’t start with the tasks — they start with those key challenges you experience every day.

“Those challenges aren’t likely to be anything new — it’s more like ‘Are we hiring the right people into the right roles at the right time? Are we giving candidates a good experience? You have to identify your biggest process challenges and work backwards.”

To take a user-focused approach to identify your key challenges, treat your end-to-end processes across the whole of talent acquisition like a discovery process. Get curious about hiring data and outcomes, and ask:

• – What are our biggest challenges as a department? Which are the challenges we need to solve right now?
• – Where do candidates and hiring managers experience the most friction?
• – Which of these challenges could be considered low-hanging fruit, requiring minimal investment or enablement?
• – What are our success indicators and KPIs?
• – How will implementing agentic AI in this workflow/process serve our end users?

Once you’ve framed the right problems, it’s time to focus on how you bring the TA team along with you and build the right capability to drive outcomes — while using agentic AI responsibly. This, says Bradburn, is where teams can start to stumble.

“As a talent leader, you’ve got to think across three stratas of enablement, strategy, and tooling for the team, and for yourself,” Bradburn says. “Start by breaking down all of the workflow challenges you identified across TA — developing a deep understanding of every teeny tiny step of every process that happens within your function. Use that analysis to build a roadmap that embeds your tooling and enable the team over the next 12 to 18 months. You need to map out the tooling you’re going to use to solve your challenges, and identify exactly where your team needs to develop and grow.”

To surface enablement needs, focus less on the specific tools, and more on capabilities:

• – How do we ensure recruiters and hiring managers have the critical thinking skills to challenge AI outputs?
• – How do we plan to develop data fluency so that the TA function can identify patterns and make better decisions?
• – How and where will we develop curiosity and experimentation, and encourage testing and learning?
• – How will we embed core capabilities into day-to-day hiring for TA roles so they become embedded into how the function operates?
• – Does everyone understand where agentic AI can and cannot be used in talent processes?
• – Does everyone understand the steps of each workflow well enough to see where agents can and can’t be trusted to act?