understanding AI agents: moving beyond RPA.

|15 minute read|

The recent hype around generative AI agents has been hard to miss — especially with major players like Microsoft and Salesforce competing to be the leaders in the space, not to mention the numerous HR-tech solutions rushing to incorporate agentic AI into their products.

You might be wondering what makes these “agents” different from robotic process automation (RPA) — and why there’s so much buzz about them.

The difference actually goes well beyond marketing hype and tech buzzwords. It’s the difference between a digital worker that follows a strict set of instructions, like an assembly-line robot, and one that thinks, adapts and even learns over time — more like a human colleague.

Let’s cut through the noise and see why this shift matters for your business, your team and the future of HR.

what makes AI agents different?

At a basic level, AI agents bring together six capabilities that go beyond what RPA can do:

(1) natural language understanding, processing and reasoning
(2) memory and data access
(3) tool integration
(4) planning and strategy
(5) task execution
(6) learning and adaptability

Let's take a look at each capability and imagine how they could be applied to sourcing, recruiting or HR:

01. natural language understanding and reasoning 
Unlike RPA, which follows clear, pre-programmed commands, agents understand human language and context. They’re capable of interpreting subtleties, "reading between the lines," and responding to instructions and questions with the same insight you’d expect from a colleague. Perhaps most importantly, they can reason about the information they process — connecting dots between different pieces of information, making logical inferences, and adapting their responses based on context and prior interactions.

Imagine: An AI agent managing candidate communications that go beyond simple Q&A. When a candidate asks about working hours, the agent reasons that they should also know about flex time policies and hybrid work arrangements. If someone inquires about professional development, the agent connects this to relevant details about mentorship programs and certification reimbursements. The agent pieces together comprehensive responses using information from multiple sources — your careers page, HR policies, job descriptions — all while maintaining a consistent tone and ensuring accuracy. Unlike basic chatbots, AI agents can track the conversation thread, remember previous questions and build on past responses to deliver coherent, informative candidate experiences.

02. memory and data access
While RPA only remembers what it’s told to, agents are capable of keeping a running memory of interactions and conversations, allowing the AI to retrieve and connect information from different sources.

Imagine: An agent can remember a candidate's details from previous interactions, recall preferences and reference conversations that happened months ago, creating a personalized experience for candidates and keeping everyone on the same page. For existing employees, this same capability could enable agents to serve as personalized career coaching guides, suggesting learning paths and internal opportunities based on employees’ unique skills, interests and growth trajectory.

03. tool integration
Agents can connect to and interact with the tools you’re already using and can even learn to use new ones on their own. You may have seen the buzz around Anthropic's "computer use" (If you haven't, watch some of the videos and you'll see how agentic AI can have very practical benefits when it’s able to interact with systems, just like you can).

Imagine: An AI agent that handles complex hiring workflows, such as moving candidate profiles between your ATS and your CRM, updating the CRM with outreach status, and coordinating with the calendar to book interviews — all without you needing to program each individual step. Agentic solutions like ConverzAI, Kwal, Braintrust AIR or Scotty AI can already be used to engage and prescreen candidates today, update candidate profiles within the ATS using all of the data gathered from their conversations (including skills, experience, preferences, motivations, aspirations, etc.), and recommend other open jobs that might be a good fit for the candidate. The early NPS and CSAT scores for these kinds of voice recruiter agents show that people don't mind talking to AI recruiters, in fact, they actually enjoy it.

04. planning and strategy
RPA can perform a sequence of tasks but doesn’t "think" beyond the script. Agents, on the other hand, can break down complex goals, adapt their approach when things change and even strategize to avoid potential roadblocks.

Imagine: An agent that manages the entire recruitment process for a high-volume role, adjusting timelines based on interview availability, candidate response rates or changing role requirements, ensuring an optimized and agile hiring process without constant human oversight. This is already happening to some degree in high-volume hiring with solutions that use traditional RPA, but they simply can't be as flexible as generative AI-powered agent solutions, where the capabilities and experiences can be more adaptable, responsive, personalized and "human."

05. task execution
Agents move beyond basic automation by making intelligent decisions based on context. They know when to handle exceptions, escalate issues or modify their actions to align with the situation.

Imagine: An agent handling interview scheduling that automatically reschedules with candidates in case of last-minute conflicts, or offers alternatives when availability is tight, reducing back-and-forth coordination and making the process smoother for both recruiters and candidates. LinkedIn's new Hiring Assistant is a good example of agentic AI which executes tasks — sourcing, sending personalized outreach messaging and prescreening candidates. For internal talent development, an agent could actively coordinate learning experiences — enrolling employees in relevant courses, scheduling mentorship sessions, setting up job shadowing opportunities and adjusting these plans based on progress and feedback.

06. learning and adaptability
While RPA requires constant updates and reprogramming to adapt to new tasks, AI agents can learn from experience, adapt to changing environments and apply lessons across different contexts.

Imagine: An agent that learns from its sourcing efforts, identifying which outreach strategies or channels yield the best responses for different roles, allowing it to adjust its approach to reach top candidates faster and with higher engagement. It might discover that software engineers respond better to messages that highlight technical challenges, while product managers engage more with impact-focused outreach. It can learn optimal timing for follow-ups, recognize which job description elements attract more qualified candidates, and adapt its communication style based on candidate seniority and role type. The agent can even spot emerging patterns — like noticing that candidates from certain companies tend to progress further in your process — and ask you if you'd like it to adjust its sourcing strategy accordingly. Best of all, these insights don't stay siloed; an AI agent could apply what it learns about engaging technical talent in San Francisco to improve its approach with similar candidates in Austin or Toronto. Unlike static RPA, which keeps doing the same thing regardless of results, these agents get smarter and more effective over time — just like your best recruiters do.

what could be better than an AI agent? multi-agent systems.

While individual agents are powerful, even greater things can be achieved when multiple agents work together in orchestrated systems. A good way to think of multi-agent systems is like assembling a specialized team where each agent has distinct capabilities but works in harmony with others.

Imagine: A coordinated talent acquisition system where different agents handle specific parts of the process:

• • • a sourcing agent continuously scans job boards and professional networks
    • a screening agent evaluates incoming applications against job requirements
    • a scheduling agent coordinates interviews and follows up on feedback
    • an engagement agent maintains personalized communication with candidates
    • an analytics agent monitors the entire process, identifying bottlenecks and opportunities
      
      

These agents communicate with each other, sharing information and coordinating actions to create a seamless end-to-end experience. When the sourcing agent finds a promising candidate, it automatically triggers the screening agent. If the screening agent approves, the scheduling agent takes over, while the engagement agent ensures consistent communication throughout.

The beauty of multi-agent systems is their ability to: 

• • handle complex workflows that span multiple domains
  • maintain consistency across numerous touchpoints
  • scale operations while preserving quality
  • provide comprehensive insights across the entire process 
    
    

Want to see a real-world example of multi-agent systems in action? Check out Microsoft's Magentic-One, "A Generalist Multi-Agent System for Solving Complex Tasks." The research article includes four short videos you can watch to see examples of the mutli-agent system in action.

At the core of Magentic-One is an "Orchestrator" agent that acts like a project manager, coordinating the efforts of specialized agents — each with their own particular skills. While one agent handles web browsing tasks, another manages files, and yet another writes and executes code; and the Orchestrator keeps everything on track. It's constantly planning, monitoring progress and adjusting strategies when things don't go as planned (because let's face it, they sometimes don't!).

Think of it as a preview of how future HR systems might work — with specialized agents handling different aspects of talent management while a lead agent ensures everything works together seamlessly.

now let's talk levels of agent autonomy

One of the most common questions (and concerns) about agents is: "How much should we let them do on their own?"

The answer depends on the use case, your comfort level and your risk tolerance. Agents can operate either as collaborative assistants requiring human guidance, or as independent workers handling entire processes on their own.

Here's how these different levels work in practice:

semi-autonomous agents (human-in-the-loop)

• • triggered manually by humans for specific tasks
  • examples:
    (1) a recruiter-activated agent that analyzes a batch of resumes, but reviews and approves the shortlist before candidates are contacted (think LinkedIn's Hiring Assistant)
    (2) a learning and development (L&D) agent that suggests personalized learning pathways but requires HR approval before enrolling employees in paid courses
  • best for: complex or high risk decisions or when human judgment is required or adds crucial value
    
    

autonomous agents (system-triggered)

• • automatically activated by predefined triggers or processes
  • examples:
    (1)an agent that monitors job boards 24/7 and automatically initiates outreach to qualified candidates based on predetermined criteria
    (2) an agent that automatically schedules follow-up interviews when candidates complete initial assessments, following predefined rules and availability parameters
  • best for: repetitive, well-defined, low/no risk tasks where speed and consistency are paramount
    
    

almost anyone can create agents

One of the most exciting aspects of AI agents is how easy they can be to create, modify and maintain — at least if you're using one of the major platforms like Microsoft or Salesforce.

Unlike traditional automation which often requires deep technical, or at least specific, tool expertise, AI agents can be created and deployed using natural language and no-code/low-code platforms. In essence, natural language has become the new programming language.

This democratization of the ability to create agentic automation means HR professionals (and practically anyone) can directly shape and deploy agent capabilities without being fully dependent on IT teams.

Whether you're a sourcer, recruiter, HR business partner or talent development specialist, you can now build and customize agents to support your specific needs (with appropriate oversight/governance, but more on that in a bit).

a quick summary: RPA vs. AI agents

Think of RPA as a super-efficient assembly-line robot:

• • follows precise, predefined steps
  • excels at repetitive tasks
  • stops working if it encounters something it's not trained for
  • requires technical expertise and coding for setup and changes
  • must be explicitly programmed for each task variation
  • can only handle scenarios it's been specifically programmed for
    
    

Agents, on the other hand, are more like knowledgeable colleagues, who are flexible, adaptable, easy to direct and can figure things out as they go:

• • understand goals and figure out how to achieve them
  • handle variability and exceptions without reprogramming
  • learn from experience and improve over time
  • can interpret intent and handle novel situations
  • can adapt to new tools and processes autonomously
  • can be created and modified using natural language instructions 
  • can be configured by business users without deep technical skills
    
    

why should you care about agents?

If RPA was about automating tasks, AI agents are about transforming how work gets done entirely.

This isn't just another tech upgrade — it's a fundamental shift that will reshape HR operations, talent strategies and even the nature of work itself.

Here's why agents are worth paying attention to:

productivity
Agents can handle complex knowledge-based tasks that were previously too complicated for simple automation. While RPA might help schedule interviews or send follow-up emails, agents can actively manage entire workflows: coordinating multiple interview panels, handling real-time scheduling conflicts, proactively engaging with candidates through personalized outreach and executing complex multi-step processes across different systems. They can parse through hundreds of resumes to match specific skill requirements, automatically generate tailored assessment questions based on job requirements and even manage entire onboarding documentation workflows. This opens new opportunities to streamline higher-value HR activities that traditionally consumed hours of manual effort but didn't necessarily require human judgment.

adaptability
When processes or environments change, agents can adjust without needing to be rebuilt from the ground up. If your hiring criteria evolves, job requirements change or you implement new tools, AI agents can adapt their approach automatically. They don't break when encountering new scenarios — instead, they apply their understanding to figure out appropriate responses, just like a human team member would.

scalability
With the ability to handle more sophisticated tasks, automation can scale across more areas of the hiring process, from sourcing to scheduling to onboarding to internal mobility to learning and development to performance management — there's really no limit. But it goes beyond just doing more — agents can maintain personalization and quality at scale. Imagine personalizing outreach to thousands of candidates while maintaining genuine engagement, or managing multiple complex hiring workflows simultaneously without losing track of individual candidate needs. This kind of intelligent scaling is impossible with traditional RPA.

system-level intelligence
When multiple agents work together, they can create powerful automated workflows that were previously impossible with standard RPA. Agents can collaborate, share information and coordinate complex processes across different domains. This enables end-to-end automation of sophisticated processes while maintaining consistency and quality at every step.

innovation
As agents learn from their work, they can suggest improvements, optimizations, and even new sourcing or recruiting ideas based on trends they've observed. For example, an agent might notice that certain types of outreach messages get better responses from specific candidate profiles, or identify emerging skills patterns in successful hires. This moves agents beyond just executing tasks to becoming strategic partners that help improve your hiring processes over time.

democratized automation
Unlike traditional RPA which typically requires technical expertise, agents can be created and customized using natural language today, and they will only get easier to create and configure in the future. This means HR professionals can directly shape and deploy automation without depending on IT teams. Natural language has become the new programming language, putting the power of automation directly in the hands of those who understand the work best.

workforce evolution
Unlike RPA, agents can handle cognitive work that has traditionally required human judgment. This shift will reshape roles, pushing humans toward work that emphasizes uniquely human skills like strategic thinking, relationship building and creative problem-solving. For HR leaders, this means rethinking workforce planning, focusing L&D initiatives on these higher-order skills and helping employees navigate the transition to more strategic roles.

AI agent reality check: critical challenges and concerns

While the potential for agents is amazing and exciting, we’re still in the early days: Agentic AI isn't all "sunshine and rainbows." To provide a balanced view, let's take a look at some of the limitations, considerations and challenges.

technical limitations

• • Agents can sometimes make mistakes or take unexpected actions, especially in novel situations.
  • The technology is evolving rapidly, which means both opportunities and challenges for early adopters.
  • Integration with legacy systems and security protocols could be complex.
  • Performance can be inconsistent across different types of tasks and contexts (the "jagged edge" of generative AI).
    
    

operational considerations

• • The level of supervision needed varies by task criticality and potential impact.
  • Agents might work best in collaboration with humans, where each can amplify the other's strengths.
  • Human oversight remains essential, particularly for decisions that impact people or carry significant business risk.
  • Critical decisions should still be made or validated by human experts.
    
    

regulatory and policy framework

• • Company AI policies may restrict certain types of automated decision-making.
  • Local and national regulations (like GDPR, AI Act) may limit agent autonomy in specific domains.
  • Privacy and data protection requirements must be carefully considered. 
  • Some decisions — including hiring, terminations, compensation and benefits, grievances, performance appraisals, DEI, workforce reductions, compliance and employment laws — should never be fully automated due to ethical or legal considerations.
    
    

implementation challenges

• • Organizations may need new skills and roles to effectively deploy and manage agents.
  • Change management is crucial for success (and we know change is never easy!).
  • Cost-benefit analysis must account for both direct and indirect impacts.
  • Clear governance frameworks need to be established. While no-code/low-code platforms make agent creation more accessible, organizations still need to establish governance around who can create and deploy agents, and what guardrails should be in place.
    
    

(still) interested in experimenting with AI agents?

Getting started with AI agents doesn't have to be scary or overwhelming. By following a few key principles, you can begin to explore the value of agents while avoiding common pitfalls.

Here's a practical guide to get you started:

1. 1. Start with a clear use case where an agent can provide immediate value with zero risk, like streamlining personalized candidate outreach or managing repetitive scheduling tasks.
   2. Consider focusing on human-agent collaboration where the two can work together to achieve better results than either could alone.
   3. Embed rules and constraints that align with your organizational values and compliance requirements.
   4. Clearly delineate between what agents can and cannot do autonomously.
   5. Build oversight mechanisms based on risk levels. Low-stakes tasks might be fully automated, but for high-stakes hiring decisions, human review and decision-making are still essential. 
   6. Consider setting defined triggers for human intervention based on confidence scores or risk levels. If possible, configure some sort of "kill switch" to immediately halt agent operations if issues arise.
   7. Make sure you have systems in place to track agent actions, decisions, and performance metrics. Ensuring comprehensive logging of all agent actions and decisions will serve you well for agent auditability and accountability.
      
      

automation has entered a new era with agents

Moving from RPA to AI agents isn’t just an upgrade in technology; it’s a fundamental shift in how and what work can be done by technology. If your organization is prepared to embrace this change carefully and thoughtfully, agents can become invaluable allies in driving improvements in experience, productivity, ROI/results and innovation.

If you wait for agentic AI to fully mature before experimenting with it, you may run the risk of falling behind competitors who are moving quickly to experiment with, learn from and benefit from AI agents today.

Consider small pilot projects and explore where agents could free up time for your team, provide more consistent and improved experiences and outcomes and get curious about what’s possible with this new wave of intelligent automation — one use case at a time.

For a more aggressive take on how you should be approaching the agentic AI opportunity, I'll leave you with these thoughts from Abhas Ricky, chief strategy officer at Cloudera, who recently shared this in the Forbes article "An Executive's Crash Course In AI Agents:"

"You can’t wait for ROI to come through on a test use case and then deploy a bigger budget. This is one of those technologies that requires a leap of faith. By the end of the year, companies investing in AI applications and agentic workflows will outpace those that aren’t. They’ll leverage agentic systems that manage multiplicity, respond to natural language, and work seamlessly with existing software tools and platforms — accelerating their benefits and beating the competition in a shorter time."

Want to learn more AI trends and how they impact your talent strategy? Contact us.