What Is Agentic Ai And How Will It Change Work

The way humans interact and collaborate with AI is taking a dramatic leap forward with agentic AI. Think: AI-powered agents that can plan your next trip overseas and make all the travel arrangements; humanlike bots that act as virtual caregivers for the elderly; or AI-powered supply-chain specialists that can optimize inventories... These are just some of the possibilities opened up by the coming era of agentic AI. Rewind a few years, and large language models and generative artificial intelligence were barely on the public radar, let alone a catalyst for changing how we work and perform everyday tasks. Today, attention has shifted to the next evolution of generative AI: AI agents or agentic AI, a new breed of AI systems that are semi- or fully autonomous and thus able to perceive, reason,... Different from the now familiar chatbots that field questions and solve problems, this emerging class of AI integrates with other software systems to complete tasks independently or with minimal human supervision.

“The agentic AI age is already here. We have agents deployed at scale in the economy to perform all kinds of tasks,” said Sinan Aral, a professor of management, IT, and marketing at MIT Sloan. Nvidia CEO Jensen Huang, in his keynote address at the 2025 Consumer Electronics Show, said that enterprise AI agents would create a “multi-trillion-dollar opportunity” for many industries, from medicine to software engineering. A spring 2025 survey conducted by MIT Sloan Management Review and Boston Consulting Group found that 35% of respondents had adopted AI agents by 2023, with another 44% expressing plans to deploy the technology... Leading software vendors, including Microsoft, Salesforce, Google, and IBM, are fueling large-scale implementation by embedding agentic AI capabilities directly in their software platforms. Agentic AI is quickly becoming pervasive, but it needs guardrails.

Image: Getty Images/iStockphoto Agentic AI marks a real shift in how work gets done inside an enterprise. We’re moving beyond systems that assist humans to systems that are trusted to reason, decide and act on their own. That change is already underway and it’s happening inside core business workflows - not in labs or pilot programmes. Let me be clear about why this matters. When AI systems are given autonomy, they become operational actors with authority.

They initiate actions, interact with tools and APIs and influence outcomes in real time. At that point, many of the assumptions organizations rely on - about control, oversight and accountability - no longer hold. This isn’t just a technology evolution. It’s a governance and security problem that enterprises need to address head-on. Cybersecurity will also harness and deploy agentic AI in more demanding ways, including in the security operations centre. Security operations are under relentless pressure, with analysts facing a dizzying volume of alerts, false positives and incident response demands.

The scale and velocity of threats have now nearly outpaced human capacity, leading to burnout, attrition and difficult trade-offs between security depth and business agility. In many organizations, security has become a resource game that humans alone can no longer win. Agentic AI promises to be the critical force multiplier to help solve this challenge. In this context, agents function as intelligent assistants that automate the monitoring, triage and management of security events. They classify alerts, enrich events with contextual data, correlate signals across disparate systems and escalate only what truly requires human judgment. Some are already being trained to dynamically adjust security and access policies as business contexts evolve, continuously monitoring for compliance, flagging anomalies in real time and taking limited corrective action autonomously.

Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration. Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully. Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments.

While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals. A generative AI model like OpenAI’s ChatGPT might produce text, images or code, but an agentic AI system can use that generated content to complete complex tasks autonomously by calling external tools. Agents can, for example, not only tell you the best time to climb Mt. Everest given your work schedule, it can also book you a flight and a hotel. Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think Newsletter, delivered twice weekly. See the IBM Privacy Statement.

Agentic systems have many advantages over their generative predecessors, which are limited by the information contained in the datasets upon which models are trained. Agentic AI is an autonomous AI system that can act independently to achieve pre-determined goals. Traditional software follows pre-defined rules, and traditional artificial intelligence also requires prompting and step-by-step guidance. However, agentic AI is proactive and can perform complex tasks without constant human oversight. "Agentic" indicates agency — the ability of these systems to act independently, but in a goal-driven manner. AI agents can communicate with each other and other software systems to automate existing business processes.

But beyond static automation, they make independent contextual decisions. They learn from their environment and adapt to changing conditions, enabling them to perform sophisticated workflows with accuracy. For example, an agentic AI system can optimize employee shift schedules. If an employee is off sick, the agent can communicate with other employees and readjust the schedule while still meeting project resource and time requirements. Here are the key features of an agentic AI system. Agentic AI acts proactively rather than waiting for direct input.

Traditional systems are reactive, responding only when triggered and following predefined workflows. In contrast, agentic systems anticipate needs, identify emerging patterns, and take initiative to address potential issues before they escalate. Their proactive behavior is driven by environmental awareness and their ability to evaluate outcomes against long-term goals. Master the design of AI-driven agents with LangChain and LangGraph. Automate workflows, implement RAG pipelines, and create interactive systems for knowledge management and application development. Ready to build production-grade AI?

This program equips developers to deploy reliable generative AI solutions. We'll move past theory and focus on the proven implementation patterns you need. You'll master production essentials like model selection, cost estimation, and reliable prompt engineering to build efficient apps. You'll also implement lightweight model adaptation using PEFT. Then, you'll build end-to-end RAG systems, using vector databases to connect LLMs to your data and evaluate quality with frameworks like RAGAs. Finally, you'll dive into advanced multimodal applications that process text, images, and audio.

You'll enforce structured outputs with Pydantic and implement system observability to build, trace, and debug modern AI apps. Start mastering AI-powered trading with this Nanodegree. Learn to build, backtest, and optimize sophisticated AI-driven trading models, gaining practical skills to succeed in dynamic financial markets. Agentic AI is an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention. This emerging technology has the potential to revolutionize various industries by automating complex processes and optimizing workflows.

Agentic AI systems are designed to operate with a higher degree of autonomy. It works by using AI agents, which are essentially autonomous entities designed to perform specific tasks. At its core, this technology is built on several key components: Google Cloud’s Vertex AI provides a comprehensive suite of tools for training, building, and deploying AI models, including pre-trained APIs for common tasks and custom training options for advanced use cases. Vertex AI also offers MLOps tools to manage the entire machine learning life cycle, from data preparation to model monitoring, which is crucial for the ongoing development and improvement of agentic AI systems. While both agentic AI and generative AI are forms of artificial intelligence and can be used together, they have distinct functionalities.

Generative AI, as its name suggests, is focused on the creation of new content, such as text, images, code, or music, based on input prompts. The LLM is at the heart of generative AI, and the value is generated by what the model can do and simple extensions of the LLM's capabilities. For example, you can generate or edit content, and even perform simple function calling and chain together various options.

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