Agentic AI

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Agentic AI is a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks with or without human intervention. The independent systems automatically respond to conditions, with procedural, algorithmic, and human-like creative steps, to produce process results. The field is closely linked to agentic automation, also known as agent-based process management systems, when applied to process automation. Applications include software development, customer support, cybersecurity and business intelligence.

Contents

Overview

The core concept of agentic AI is the use of AI agents to perform automated tasks with or without human intervention. [1] While robotic process automation (RPA) systems automate rule-based, repetitive tasks with fixed logic, agentic AI adapts and learns from data inputs. [2] Agentic AI refers to autonomous systems capable of pursuing complex goals with minimal human intervention, often making decisions based on continuous learning and external data. [3] Functioning agents can require various AI techniques, such as natural language processing, machine learning (ML), and computer vision, depending on the environment. [1]

Particularly, reinforcement learning (RL) is essential in assisting agentic AI in making self-directed choices by supporting agents in learning best actions through the trial-and-error method. Agents using RL continuously to explore their surroundings will be given rewards or punishment for their actions, which refines their decision-making capability over time. All the while deep learning, as opposed to rule-based methods, supports agentic AI through multi-layered neural networks to learn features from extensive and complex sets of data. Further, multimodal learning enable AI agents to integrate various types of information, such as text, images, audio and video. [4] As a result, agentic AI systems are capable of making independent decisions, interacting with their environment and optimising processes without a human directly intervening. [4]

History

The term 'agent-based process management system' was first used in 1998 to describe autonomous agents for business process management. [5]

Applications

Web browsing

AI agents can be used to perform small tedious tasks during web browsing and potentially even perform browser actions on behalf of the user. Products like OpenAI Operator, Perplexity Comet and Dia (from The Browser Company) integrate a spectrum of AI capabilities including the ability to browse the web, interact with websites and perform actions on behalf of the user. [6] [7] [8] In 2025, Microsoft launched NLWeb, a agentic web search replacement that would allow websites to use agents to query content from websites by using RSS-like interfaces that allow for the lookup and semantic retrieval of content. [9] Products integrating agentic web capabilities have been criticised for exfiltrating information about their users to third-party servers [10] and exposing security issues since the way the agents communicate often occur through non-standard protocols. [9]

MIT's study on AI business

In 2025, MIT's study revealed that about 95% of enterprise generative-AI pilots fail to deliver measurable P&L impact. [11] [12] The report titled "The GenAI Divide: State of AI in Business 2025," based on 150 executive interviews, a survey of 350 employees, and analysis of 300 deployments, and it attributes the failures largely to integration issues. [13]

See also

References

  1. 1 2 Miller, Ron (December 15, 2024). "What exactly is an AI agent?". TechCrunch.
  2. ↑ Colson, Abby (July 22, 2025). "AI agents vs. RPA: A guide for accountants". Tax & Accounting Blog Posts by Thomson Reuters. Retrieved August 27, 2025.
  3. ↑ Hosseini, Soodeh; Seilani, Hossein (July 1, 2025). "The role of agentic AI in shaping a smart future: A systematic review". Array. 26 100399. doi:10.1016/j.array.2025.100399. ISSN   2590-0056.
  4. 1 2 Hosseini, Soodeh; Seilani, Hossein (July 1, 2025). "The role of agentic AI in shaping a smart future: A systematic review". Array. 26 100399. doi: 10.1016/j.array.2025.100399 . ISSN   2590-0056.
  5. ↑ O'Brien, P. D.; Wiegand, M. E. (July 1998). "Agent based process management: applying intelligent agents to workflow". The Knowledge Engineering Review. 13 (2): 161–174. doi:10.1017/S0269888998002070.
  6. ↑ Circelli, Ruben (August 5, 2025). "I Switched to Perplexity's AI Comet Browser for a Week. Is It the Future or Just Hype?". PCMAG. Retrieved August 9, 2025.
  7. ↑ Roose, Kevin (February 1, 2025). "How Helpful Is Operator, OpenAI's New A.I. Agent?". The New York Times. Retrieved August 9, 2025.
  8. ↑ Wallen, Jack (July 7, 2025). "I loved Arc browser and was skeptical of its agentic Dia replacement - until I tried it". ZDNET. Retrieved August 9, 2025.
  9. 1 2 Warren, Tom (August 6, 2025). "Microsoft's plan to fix the web with AI has already hit an embarrassing security flaw". The Verge. Retrieved August 9, 2025.
  10. ↑ Vekaria, Yash; Canino, Aurelio Loris; Levitsky, Jonathan; Ciechonski, Alex; Callejo, Patricia; Mandalari, Anna Maria; Shafiq, Zubair (June 10, 2025), Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistants, arXiv: 2503.16586
  11. ↑ Challapally, Aditya (July 2025). "The GenAI Devide: State Of AI In Business 2025" (PDF). MIT: 3.
  12. ↑ Estrada, Sheryl (August 18, 2025). "MIT report: 95% of generative AI pilots at companies are failing". Fortune. Retrieved August 26, 2025.
  13. ↑ Walsh, Dylan (June 17, 2025). "4 new studies about agentic AI from the MIT Initiative on the Digital Economy | MIT Sloan". MIT Management Sloan School. Retrieved August 26, 2025.
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