Anyone following the latest news in the computing sector knows that agentic artificial intelligence (AI) is a headline maker. The staff at dig.watch reports, “Organizations across sectors are turning to agentic automation — an emerging class of AI systems designed to think, plan, and act autonomously to solve complex, multi-step problems.”[1] They add, “Experts caution that success depends on identifying the right use cases and implementing proper governance. Still, the potential is clear: agentic automation could unlock entirely new capabilities and redefine how complex work gets done.” That observation contains good news for organizations and concerns for the workforce. In part one of this article, I will explore how agentic AI is changing (and can change) organizations. In part two, I will discuss how agentic AI is affecting the job and work environment.

What are AI Agents?

The Boston Consulting Group (BCG) defines AI agents this way: “AI agents are artificial intelligence that use tools to accomplish goals. AI agents have the ability to remember across tasks and changing states; they can use one or more AI models to complete tasks; and they can decide when to access internal or external systems on a user’s behalf. This enables AI agents to make decisions and take actions autonomously with minimal human oversight.”[2] How do AI agents accomplish their tasks? BCG explains, “AI agents observe their environment, leverage large language models for planning, and access connected systems to take action and accomplish goals.” They call this an observe-plan-act cycle:

Observe: “AI agents constantly collect and process information from their environment including user interactions, key performance metrics, or sensor data. They can retain memory across conversations, which provides ongoing context across multi-step plans and operations.”

Plan: “Using language models, AI agents autonomously evaluate and prioritize actions based on their understanding of the problem to be addressed, goals to be accomplished, context, and memory.”

Act: “AI agents leverage interfaces with enterprise systems, tools, and data sources to perform tasks. Tasks are governed by the plan delivered by a large language model or small language model. To execute tasks, the AI agent may access enterprise services (such as HR systems, order management systems, or CRMs), delegate actions to other AI agents, or ask the user for clarification. These intelligent software agents have the ability to detect errors, fix them, and learn through multi-step plans and internal checks.”

You have to admit, those are impressive capabilities. BCG concludes, “This observe-plan-act cycle is self-reinforcing because AI agent tools continuously analyze how the world has changed based on past interactions and learn how to be more efficient and effective over time.” As organizations become more familiar with agentic AI, they are refining how they are implemented. Alex Spinelli, Senior Vice President for AI Developer Platforms and Services at Arm, observes, “Traditionally, AI interactions have centered around a single, often large, model designed to perform a variety of tasks. However, with AI agents, this is changing. Instead of relying on one massive model to handle everything from start to finish, AI agents break down tasks into smaller, specialized components, each handled by different agents. Compare this to moving from a single craftsman to an intelligent network of specialist workers, making AI more specialized and efficient.”[3]

Transforming Enterprises

According to Misha Porwal, a marketing strategist at AgilePoint, agentic AI has the ability to transform business enterprises. She writes, “As artificial intelligence continues its rapid evolution from experimental technology to operational necessity, a new paradigm is emerging that promises to fundamentally transform how enterprises operate: Agentic AI. This advanced form of artificial intelligence goes far beyond the chatbots and recommendation engines that have dominated recent headlines, offering organizations the ability to create autonomous systems that can plan, decide, and act independently to achieve business objectives.”[4] Narendra Naidu, Group Head of Data and AI at Atos, agrees with Porwal. He explains, “Agentic AI is set to drive innovation in an even more intelligent and powerful way. The research firm Gartner predicts that by 2028, 15% or more of day-to-day work decisions will be made autonomously through agentic AI.”[5] Porwal and Naidu discuss four unique traits available in agentic AI. They are:

• Perception: Naidu observes that agentic AI is perceptive and can gather and process data from multiple resources. “These resources can be traditional databases like CRM or ERP but can also include ambient information from Internet of Things (IoT) sensors or digital interfaces.”

• Autonomy: Both Naidu and Porwal highlight the autonomous nature of agentic AI. Naidu notes that agentic AI can make “decisions on its own — acting, planning and deciding, based on sophisticated reasoning engines.” Porwal notes that agentic AI can “make autonomous decisions within defined parameters.”

• Adaptability: Naidu and Porwal also underscore the adaptability of agentic AI. Naidu notes that agentic AI can solve problems “dynamically within changing environments — learning, collaborating and iterating based on feedback loops.” Porwal also notes that agentic AI can “adapt and learn from changing conditions.”

• Persistence: According to Naidu, agentic AI is goal-oriented. It can pursue and achieve “user-defined business outcomes.” Porwal adds, “[Agentic AI] take actions that directly impact business operations.”

According to Naidu, “This combination brings positive changes and opportunities to the business.” Porwal adds, “Agentic AI is fundamentally about ‘bridging to actions’ in enterprise operations. The opportunity with AI extends beyond discovery to operational transformation — enabling organizations to turn AI insights into real business impact.”

Concluding Thoughts

Spinelli concludes, “The impact of AI agents will be widespread and cross-industry. Sectors like finance, insurance, healthcare, retail, logistics, and creative services are already exploring a variety of use cases where AI agents can be adopted, ranging from fraud detection to automated underwriting, and even content creation. The potential is staggering.” Although agent autonomy has been touted, Physicist and science writer Salvatore Salamone explains that oversight is a good idea. He reports, “In simulated experiments, testers showed how easily AI agents can adopt self-preservation tactics when placed in high-stakes scenarios. Specifically, similar to disgruntled human employees, AI agents might take unethical actions to complete tasks and survive.”[6] He adds, “It must be emphasized that the testing here was extreme.” McKinsey & Company analysts predict the future business enterprise will feature “both your current colleagues — humans, if you’re like most of us — and AI agents.”[7] How AI agents are changing the workplace will be the focus of the concluding part of this article.

Footnotes

[1] Staff, “Autonomous AI agents are the next phase of enterprise automation,” dig.watch, 13 May 2025.

[2] Staff, “AI Agents,” Boston Consulting Group.

[3] Alex Spinelli, “AI Agents: the next big phase of artificial intelligence,” TechRadar, 17 July 2025.

[4] Misha Porwal, “What is Agentic AI? The Complete Guide for Enterprise Leaders,” Customer Think, 25 May 2025.

[5] Narendra Naidu, “Agentic AI: The next wave of intelligent process automation,” Atos Blog, 22 May 2025.

[6] Salvatore Salamone, “Beware of Vengeful AI Agents,” RT Insights, 9 July 2025.

[7] Staff, “The future of work is agentic,” McKinsey & Company, 3 June 2025.