As we progress through 2025, we're witnessing a paradigm shift in Artificial Intelligence with the emergence of Agentic AI systems - autonomous entities capable of perceiving, reasoning, and acting with minimal human oversight. In 2024, we saw a proliferation of "Co-Pilots," first as code development assistants and then for pretty much any simple task under the sun.
This year, we see an evolution to "Agents" and "Agentic AI," which represents not merely an incremental advancement but a fundamental reimagining of how work is done. Naturally, this is an enormous opportunity for enterprises to approach risk intelligence differently across the multidimensional landscape of threats they face daily.
The original concept of AI Agent has been around for at least 3 decades now — the foundational textbook on AI, used by all credible University courses on AI around the world, first published in 1995 (30 years ago) already stated that "The main unifying theme [of the book] is the idea of an intelligent agent.", with Chapter 2 being entirely dedicated to defining Intelligent Agents. What we see today is that the recent developments in AI with Large Language Models (LLMs) have given a new impetus to this field as they’re being used to implement a good portion of the agent’s processing loop.
The distinctive capability of agentic AI
The distinctive capability of Agentic AI lies in its autonomous planning and decision-making abilities while operating at machine scale and speed. Thanks to LLMs, all of this now augments human-like contextual understanding of text.
Traditional AI applications use only small pockets of AI models, typically for some very well-encapsulated decision-making or value estimation - e.g., an automatic email triage system that classifies the email as SPAM, Customer Complaint, Request for Information, etc. In these traditional applications of AI, the whole end-to-end process is typically designed and implemented by humans (despite running it could possibly be 100% automated), with localised application of AI components. Still, we have to design the process end-to-end, thinking not only of the happy path but also of the myriad of ways things can go wrong and accounting for them — needless to say, chances are, sooner or later, such a process will encounter an unhappy path that no one foresaw.
With Agentic AI, the game changes. No longer do we have to design the process, for it is the AI Agent itself that, using its reasoning capabilities, understands the user’s ask, breaks it down, outlines a plan, and reflects on that plan to assess if it really would solve the user’s request, corrects the plan if needed, and then proceeds to execute it — of course, allowing for in-flight course correction of the plan if something unexpected happens. This combination of on-the-spot planning, reasoning, and real-time adaptability to circumstances is the game changer. That’s why we see a plethora of Agentic AI applications throughout the world, essentially allowing people to create apps in a few hours (in some cases, minutes!), co-creating detailed reports, and solving many other problems.
Agentic AI for risk professionals
For risk professionals and the Risk Intelligence community, this self-reflective capability transforms how organizations can accelerate the detection, assessment, and mitigation of risks across industrial, environmental, cyber, and regulatory domains.
Signal AI's Ask AIQ Agent is rapidly evolving in this direction, already understanding the user's request, and planning and acting accordingly to fulfill it. Ask AIQ already can use most of the functionalities in our platform as basic tools to respond to the user’s ask, but even more advanced capabilities are in the forge, such as the ability to learn new skills directly from the end user - in the future, you’ll be able to train your own personalised version of Ask AIQ!
Consider how this manifests in practice: AI agents embedded within enterprise systems can provide risk alerts and soon automatically propose mitigation strategies across multiple risk categories simultaneously. For cybersecurity threats, they autonomously identify anomalies and could propose corrective measures before human analysts even become aware of potential breaches. In regulatory compliance, these systems can dynamically adapt to evolving requirements, dramatically reducing non-compliance exposure.
However, implementing agentic AI is not without challenges. Organizations must address expanded attack surfaces, data privacy concerns, and the fundamental question of accountability in autonomous systems.
How could your organization's risk posture evolve if its entire threat detection and response infrastructure were powered by a sophisticated AI Agent? As we navigate this autonomous frontier, the question becomes not whether to embrace agentic AI, but how to architect it responsibly to transform enterprise risk intelligence.
Alexandre Martins Pinto is the SVP of Artificial Intelligence at Signal AI, spearheading AI advancements for Signal AI’s SaaS platform, enhancing capabilities for reputation and risk intelligence through cutting-edge AI innovations. You can find him on LinkedIn here.