Solving for these challenges has traditionally required substantial analytic work to extract the full value out of the available data, but this is all set to change with the advent of agentic AI.
What is agentic AI?
Agentic AI refers to the capabilities that allow AI systems to have agency – the ability to perceive their environment, reason about it and act independently to achieve a goal. The ‘hands’ that perform this work are AI agents – intelligent software entities that can operate independently within a defined set of guidelines.
In his keynote speech at Equifax Innovation Day, our global Chief Data and Analytics Officer, Harald Schneider, describes AI agents as “software systems that use generative AI, but oftentimes also lots of other components... and these agents then function as autonomous co-workers."
Unlike a traditional software program that waits for a command, an AI agent takes the initiative. It can perceive its environment, formulate a plan, take action, and even learn from the results to get better over time.
How agentic AI differs from generative AI
Agentic AI is a significant evolution from the simple chatbots and generative AI tools we've become familiar with. While generative AI (GenAI) has captured the public’s imagination by creating text, images, and code, it is fundamentally reactive, producing output only in response to a human’s specific, step-by-step instructions.
Agentic AI, by contrast, is proactive. It understands a high-level goal, breaks it down into a series of tasks, and orchestrates the tools and data needed to achieve it, adapting its strategy in real-time without constant human intervention. It’s the difference between a brilliant assistant that executes your commands and a self-directed colleague that anticipates your needs.
AI agents are becoming the most common manifestation of AI, with 25% of businesses using Gen AI expected to have agentic AI proof of concept or pilots in 2025.
A paradigm shift
Agentic AI technology is advancing rapidly, delivering us to a point where “it’s really the first time in history that human-level intelligence is available through machines”, says Schneider.
In just four years, AI has gone from an elementary school child to a PhD-level professional. "We have never in our lifetime seen any technology develop at this speed”, states Schneider. “This unprecedented leap requires us all to rethink not just where AI can be deployed, but how it can redesign business processes entirely.”
It’s no longer just about using AI for a single, isolated task, but about fundamentally changing how you approach complex problems. This transformation requires a new mindset. "We have to think differently around how we run our business," noted Schneider. "We have to revisit how we do things like architecture in a world where the flexibility of the components is going to change much more frequently than in past systems."
The business value of AI agents
Leading firms are already building and deploying agentic AI into their platforms and workflows. At Equifax, we're also actively working on developing AI agents, with “more than two dozen in production now across our organisation”, says Schneider. “The agentic AI architecture we’re building will enable faster innovation, broader scaling and robust governance as we embed this capability into our solutions”.
He explains that this architecture includes a perception layer to feed the models with knowledge from various data sources, a cognition layer where the core models and logic reside, and an action layer that enables the AI to perform complex tasks.
“We want to get to a world where I can just ask questions in plain English and the AI agent gives me the answer”, says Schneider. He gives the example of the Equifax Insights Agent platform, which is currently in development. It’s being designed to provide customers with an intuitive, agent-assisted experience to gain insights, formulate strategies and deploy them into production quickly and confidently.
Transparency, trust, and the human element
The immense power of agentic AI also comes with a critical responsibility: ensuring it operates ethically and transparently.
Schneider explains that agentic AI is a technology that comes with its own risks, some of which are complex and not part of existing control frameworks. “And so we want to make sure that as we try to drive adoption and scale quickly, that we have the right controls in place, and that these controls are standardised and built into our platform.”
He emphasises the importance of AI systems not becoming unexplainable black boxes as they become more autonomous. “We believe that explainability is very important as we’re building these sophisticated systems,” says Schneider. He gives the example of the Equifax Lending Brain, currently in production, a virtual credit risk analyst that uses AI to help businesses manage their lending portfolios. It analyses a company's data to identify key issues like rising delinquencies and their root causes, such as affordability problems or fraud. The tool then suggests and even builds optimised lending policies to help improve performance and reduce risk.
Importantly, the Lending Brain includes an explanation layer which clarifies why the system recommended a particular policy or decision, providing customers with explainable reports for greater transparency.
The Lending Brain is just one example of how agentic AI is reshaping the future of work, enabling businesses to move with speed and achieve a clarity of insight previously unattainable.
Please reach out to Equifax to learn more about how our Agentic AI solutions can help solve your business challenges.