4 Aspects of Agentic AI in Hiring Nobody is Talking About

In HR, Agentic AI is intended to improve the hiring process. Rightly so, with many potential benefits, but what considerations should hiring teams make before diving in the deep end?

The risks are talked about far less than the upside of AI agents. As the current regulatory climate tightens around automated HR decisions, this post gives a sober look at agentic AI in the HR landscape, surfacing key areas that are often overlooked and have the potential to perpetuate risk for the organization. Let’s look at what an AI agent is broadly, and then talk about how HR can proceed while protecting their process and bottom line.

What is Agentic AI?

With approximately 50% of the world’s organizations using this tech, agentic AI isn’t new; however, the plane is still being built while it’s in the air. Through the ongoing evolution of generative AI (which can facilitate the capabilities of agentic AI) the boundaries of any process are being stretched.

But first, take a few steps back. What is agentic AI? Forbes defines an AI agent as follows:

“AI agents are autonomous software systems endowed with reasoning capabilities to process large volumes of data, comprehend context, reason logically, design complete workflows by interfacing with other systems and undertake actions including executing tasks. When multiple AI agents are put together to form a multiagent system, these AI agents can help revolutionize how processes are automated and analyzed, often leading to faster processes.”

The main benefit of using AI agents in any process, as stated above, is speed. Additionally, AI agents allow for interaction with complex, tailored processes, and similarly, can improve efficiency around sophisticated tasks.

With so many users and so many benefits, the first thought may be how to implement this technology. On the other hand, Agentic AI makes it easier for applicants to present polished, but sometimes inauthentic materials. Before proceeding, let’s pull over and think about a few of the lesser-known considerations. Without further ado, here are the top 4 agentic AI considerations no one is talking about.

1 “Runaway” Agent Workflows Across the HR Tech Stack

Modern agents can take actions in other systems. Think about emailing candidates, updating ATS stages, and scheduling meetings. When agent technologies are advertised, what’s rarely covered is containment. AKA guardrails for when an agent loops, escalates, or updates the wrong record at scale. Agents that message candidates at scale raise consent, frequency, and content risks. They can damage a brand if the tone or timing misfires. 

That being said, Agentic AI guardrails can change depending on the nature of the organization. Altexsoft explains: 

“The exact process of applying guardrails can vary depending on the use case and system design. But in general, it involves setting up validation checks and constraints at key points in the agent’s workflow. These are known as intervention points—stages where the system can review, block, modify, or guide the agent’s behavior.” 

2 Agents Can Exacerbate Volume Problems

With one-click auto-applications everywhere online, candidates volume is overwhelming. LinkedIn says 11000 applications per minute are sent via their platform. And it’s estimated that at least 45% are using AI to create their resumes, adding to the problem. Resumes pile up, and having an agent helps, but it won’t necessarily solve the problem by intelligently filtering out candidates like a human would. 

Agentic AI is great at coordination (answering, qualifying, scheduling, nudging), but most of the pain in frontline hiring comes from structural bottlenecks (supply, eligibility, compliance, manager ops, show-rates) that agent software alone can’t fix. 

Auto-apply tools generate volume without signal. At scale, an agent responding to all of these applicants can create even more work for recruiters. A recent Forbes article explains:

"Enterprises eagerly launch pilots, but employees end up spending so much time double-checking outputs that the promised efficiencies never materialize."

Yes, an agent can reply faster, but there's more to the story and recruiters still need top-of-funnel tools to separate the truly qualified applicants from the rest. To solve this, consider pushing more screen-in assessments to the top of the hiring funnel. 

3 Audit-ready Logs for Agent Decisions are Needed

Bias-audit and high-risk obligations don’t stop at the model; they cover the entire agentic workflow that screens and advances people. In New York City, if you use an automated tool to assess or screen candidates, Local Law 144 requires an independent bias audit every year and public posting of a summary (including selection/scoring rates and impact ratios by sex and race/ethnicity) plus the tool’s distribution date, along with 10-business-day candidate notice before use (NYC DCWP overview and the city’s FAQ detailing scope, publishing, data requirements, and notice timing. Here’s a list of what hiring teams should aim to capture in decision logs and data lineages, in order to reconstruct “why this person advanced” if needed in the future:

• Agent/automation trail: each tool or action invoked (e.g., ATS stage change, email, assessment trigger), timestamps (start/end), request/response IDs
  
  
• Inputs & sources: resume/assessment artifacts, features used, data source and version (e.g., model, prompt/system config), and any reference dataset search
  
  
• Outputs & thresholds: raw scores, rankings, cutoffs, filters applied, and rationales produced
  
  
• Human oversight: reviewer identity, approvals/overrides, escalation notes
  
  
• Candidate rights & notices: when/where notice was delivered (for NYC), accommodations offered, and consent status

These artifacts give HR the bread crumbs they need to prove compliance (NYC bias-audit transparency, EU high-risk logging) and support ongoing adverse-impact monitoring under U.S. civil-rights law. 

4 PII Sprawls Because Agents Need Tools

Agents often need access to other systems to be useful. That creates PII propagation and vendor risk that employers have to control. In high-volume hiring, agentic AI typically plugs into calendar, email, CRM, and ATS to auto-reply, schedule, and move candidates through stages. Each integration can copy, cache, or transform candidate PII (names, contact details, IDs, assessment results), creating a trail of duplicates across multiple vendors. That “PII sprawl” amplifies third-party risk and complicates basic controls like access revocation, retention, and deletion. 

A practical way to rein it in is to apply a data-flow view to your agentic workflows, map where PII travels, log each automated action, and enforce least-privilege scopes. These approaches that directly encouraged by the NIST AI Risk Management Framework for governing AI systems and their third-party dependencies (NIST AI RMF).

On the compliance side, think privacy-by-design for recruitment: minimize data, limit purpose, set short retention by stage, and keep auditable decision logs. Sector regulators provide concrete expectations for HR specifically. For example, the UK ICO’s guidance on recruitment and selection details transparency, lawful bases, retention, and deletion duties for candidate data flowing through hiring tools, which are directly applicable when agents orchestrate multiple systems.

Evaluation Beyond Accuracy

Agent success isn’t only ‘was it done faster?’. Ensure proper oversight before bringing in an AI agent that could make hiring outcomes worse. 

Track candidate-experience metrics like first-reply time, drop-off after agent contact, and the number of complaints. Consider monitoring agents from implementation by assigning business owners to each agent. Give them the authority to stop an AI agent and a test plan before any new capability goes live. Add candidate experience KPIs to weekly dashboards. Tie them to compensation or OKRs, not just hires-per-recruiter. Your organization could even adopt a lightweight “AI change advisory board” with the task of aligning your organization to key legislation like LL144 or the EU AI Act.

As a bonus, here is a list of technical considerations for hiring teams interested in implementing AI agents related to what was discussed above. Try to enforce as many as possible when adopting AI agent workflows:

• Publish rules for each agentic step with consideration for when failure rates breach thresholds. 
• Require per-action approvals for system-of-record writes. Set role-based scopes and rate limits for outbound communications. Consider “circuit breakers” that pause agents on anomaly spikes. 
• Add throttles (per-candidate/day) and duplicate-detection.
• Have exportable action logs and score explanations. Map each automated step to its legal basis and human-in-the-loop. 
• Enforce consent checks before every outreach, set quiet hours, and A/B test agent tone. Add human approval for any offer-adjacent message. 
• Treat agents like well-monitored services, utilizing runbooks and incident response procedures. 
• Consider the success rate of intended outputs like scheduled interviews or median time-to-first-response.
• Flag sudden spikes in “perfect” resumes from the same IP/time window. 
• Require least-privilege OAuth scopes, tool whitelists, and data-retention SLAs; log every third-party call an agent makes. 
• Set per-task cost caps, show “cost to complete” estimates to users, and auto-halt on low value-per-action.

Weigh risk versus reward when implementing new AI agent technology, and if you choose to proceed, it’s always better to cover your bases. Consider what other tools are available to help HR teams save time, like soft skills assessments used at the screening stage. This ensures recruiters with high-volume mandates only spend time on those who have actually demonstrated they have the skills to do the job.

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Image Credits

Feature Image: Unsplash/Austin Distel

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