AI Agents Transform IT into HR Department

Agentic AI is rapidly transforming enterprise microservices, moving beyond simple helper applications to become a fundamental power source. This shift implies a future where IT departments will manage AI agents much like HR handles human employees: acquiring, onboarding, and guiding them.

The Microservices Parallel and Agentic Evolution

Experts at the Mobile World Congress, in a panel hosted by Deloitte, highlighted how agentic AI mirrors the architectural revolution brought about by microservices. Just as microservices broke down monolithic applications into smaller, independent units, agentic AI offers a similar modular approach to problem-solving. Bryan Thompson, VP for GreenLake product management at HPE, explained, "Agentic AI is the next step in breaking apart and solving problems. There are opportunities to leverage these types of models and break them into almost like a microservice type of approach to tackle them, breaking them apart into specialised services."

Fred Devoir, global head of solution architecture for telco at Nvidia, added that agentic AI facilitates the integration of enterprise workflows. He noted, "We take componentry and put it together into a RESTful architecture. Nvidia was able to optimise those with our microservices, and then bring together those microservices into blueprints to give a very quick time to value or time to first results." This modularity allows for more agile and efficient system development, much like how AI creates a new "meaning" of work, not just the outputs by redefining tasks.

The Independent AI Agent: A New Paradigm

What sets agentic AI apart from traditional microservices is its capacity for independent action and ideation. Abdi Goodarzi, head of Gen AI products, innovations and new businesses for Deloitte, emphasised this critical distinction: "Until now, we've never had a technology that could ideate, or execute independently. Just think about that statement, and any other software package solution you've ever dealt with. None of them could independently execute any of it. That's really the power of AI." This capability fundamentally changes how businesses can approach automation and problem-solving, creating a "parallel workforce" managed by IT.

This new dynamic means IT departments will essentially become the "HR" for these AI agents. Devoir articulated this, stating, "Human capital management and agentic AI capital management are the same thing, but the difference is instead of an HR for humans, now you have an IT department that's acting as HR for all these agents." This includes curating, guardrailing, training, and fine-tuning AI agents, a sophisticated technical undertaking that requires significant effort. It's a clear indication that workers are using AI more but trusting it less, highlighting the necessity of robust IT-led management.

Organisational culture and talent strategies will also need to adapt. Goodarzi pointed out the significant difference between human and AI agents: "Humans have emotions. Agents don't have emotions. How do you incorporate the emotions that will be part of the execution of the work? When the work gets down in a different way, the culture has to be shifted, talent strategies have to be shifted, and how humans and machines work together have got to be changed." This requires a re-evaluation of human-machine collaboration, a topic explored further in discussions about your AI agent: 3 steps to effective delegation.

Overcoming Challenges: Data, Trust, and Talent

Implementing agentic AI across an enterprise presents several hurdles, particularly concerning data, trustworthiness, and talent. Enterprises have invested heavily in managing structured data through ERP systems and systems of record, often resulting in disparate data silos. Agentic AI offers a potential solution by moving the AI to the data, rather than vice versa. Devoir explained, "Instead of having to bring all your data to the AI, you're taking the AI to the data. When you make a service call, it actually asks all those data agents for a response and collates that data into a model." This approach can help overcome the challenges associated with data fragmentation.

The issue of trust is paramount. Goodarzi stressed the importance of verifying data and results from AI agents: "Am I dealing with the right data? Am I dealing with the right results? All the other previous technologies were designed around transactional activity. Agentic AI is designed around probabilistic technologies. So you get the best probable answer because you have trained agents with a lot of knowledge on how to digest the data and make a decision and make a recommendation." The probabilistic nature of AI outputs means organisations must grapple with questions of reliability and confidence, a concern echoed in broader discussions about AI ethics and accountability, as detailed in reports from regulatory bodies like the UK's Centre for Data Ethics and Innovation.

Despite these challenges, the capabilities of agentic AI are maturing rapidly. Goodarzi believes that "these are new concepts for enterprises. That's why it has slowed things down in terms of adoption. But the capabilities are real. The technology is advanced enough to leverage within enterprise production systems. And I do believe this is the year that it's going to take off."

What are your thoughts on IT departments becoming the "HR" for AI agents? Share your perspective in the comments.