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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 without human intervention. The independent systems automatically respond to conditions, 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.

Overview

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The core concept of agentic AI is the use of AI agents to perform automated tasks but without human intervention.[1] While robotic process automation (RPA) and AI agents can be programmed to automate specific tasks or support rule-based decisions, the rules are usually fixed.[2][unreliable source?] Agentic AI operates independently, making decisions through continuous learning and analysis of external data and complex data sets.[3][unreliable source?] 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

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Main article: History of artificial intelligence
 
Graph from the nonprofit METR showing that the length of tasks frontier models are capable of executing at a 50% success rate has roughly doubled every 7 months from 2019 to 2024. The shaded region represents the 95% confidence interval.[5]

Agentic AI has roots in the 1990s “intelligent agent” paradigm and the BDI (belief–desire–intention) model, the intelligent‑agent paradigm became a unifying AI view in the 1990s: agents perceive, decide, and act to maximize success, shaping research across planning, decision theory, and learning.[6] The term agent-based process management system was used as far back as 1998 to describe the concept of using autonomous agents for business process management.[7] The psychological principle of agency was also discussed in the 2008 work of sociologist Albert Bandura, who studied how humans can shape their environments.[8] This research would shape how humans modeled and developed artificial intelligence agents.[9][unreliable source?]

Some additional milestones of agentic AI include IBM's Deep Blue, demonstrating how agency could work within a confined ___domain, advances in machine learning in the 2000s, AI being integrated into robotics, and the rise of generative AI such as OpenAI's GPT models and Salesforce's Agentforce platform.[10][unreliable source?][11][unreliable source?]

In the last decade, significant advances in AI have spurred the development of agentic AI. Breakthroughs in deep learning, reinforcement learning, and neural networks allowed AI systems to learn on their own and make decisions with minimal human guidance.[citation needed] Consilience of agentic AI across autonomous transportation, industrial automation, and tailored healthcare has also supported its viability. Self-driving cars use agentic AI to handle complex road scenarios, though with some limited success in certain scenarios.[12][unreliable source?]

In 2025, research firm Forrester named agentic AI a top emerging technology for 2025.[13][unreliable source?]

Applications

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Applications using agentic AI include:

  • Software development - AI coding agents can write large pieces of code, and review it. Agents can even perform non-code related tasks such as reverse engineering specifications from code.[13][unreliable source?]
  • Customer support automation - AI agents can improve customer service by improving the ability of chatbots to answer a wider variety of questions based on context, rather than having a limited set of answers pre-programmed by humans.[13][unreliable source?]
  • Enterprise workflows - AI agents can automatically automate routine tasks by processing pooled data, as opposed to a company needing APIs preprogrammed for specific tasks.[13][unreliable source?]
  • Cybersecurity and threat detection - AI agents deployed for cybersecurity can automatically detect and mitigate threats in real time. Security responses can also be automated based on the type of threat.[13][unreliable source?]
  • Business intelligence - AI agents can support business intelligence to produce more useful analytics, such as responding to natural language voice prompts.[13][unreliable source?]
  • Real-world applications - agentic AI is already being used in many real-world situations to automate complex tasks, across industries, and therefore has been successfully deployed in many departments and organizations. Some of the examples are:
    • Manufacturing and predictive maintenance - Siemens AG uses agentic AI to analyze real-time sensor data from industrial equipment, predicting failures before they occur. Following the deployment of agentic AI in their operations, they reduced unplanned downtime by 25%.[14][15]
    • Finance and algorithmic trading - At JPMorgan & Chase they developed various tools for financial services, one being "LOXM" that executes high-frequency trades autonomously, adapting to market volatility faster than human traders.[16]

Web browsing

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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.[17][18][19] 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.[20] Products integrating agentic web capabilities have been criticised for exfiltrating information about their users to third-party servers[21] and exposing security issues since the way the agents communicate often occur through non-standard protocols.[20]

MIT's study on AI business

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In 2025, MIT's study revealed that about 95% of enterprise generative-AI pilots fail to deliver measurable P&L impact[22]; a failure rate in business outcomes.[23][24] 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.[25]

The MIT NANDA report finds only about 5% of corporate generative-AI pilots are achieving rapid revenue acceleration, with the vast majority showing little to no impact on profit and loss statements. It also notes a mismatch in spending (heavy on sales/marketing tools versus higher ROI in back-office automation) and highlights “shadow AI” usage complicating measurement and governance.[26] One of the first reason on study was about "integration gap", that chatbot does not find enough time to deploy themselves and adopt the workflow environment, leading to little to no measurable impact. Over half of budgets go to sales/marketing tools, while bigger returns often lie in back-office automation that reduces outsourcing and agency costs.

Study also notes that while building internal tools is possible, parternership with external agency is often more profitable than not. The study explicitly reports that external partnerships have about twice the success rate of internal builds (~67% vs ~33%), often yielding faster time-to-value and lower total cost, so buying/partnering can be more profitable than building in-house given today’s high failure rates. With roughly 95% of enterprise GenAI efforts failing to reach measurable P&L impact, failed internal tools represent a significant sunk cost risk compared with proven vendor solutions that integrate and learn within workflows.[22] For example, building your own AI tool that may not work as expected can result in loss than to buy an externel tool that perform the tasks more easily, resulting in profit.

See also

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References

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  1. ^ a b Miller, Ron (December 15, 2024). "What exactly is an AI agent?". TechCrunch.
  2. ^ "Battle bots: RPA and agentic AI". CIO.
  3. ^ Leitner, Hendrik (July 15, 2024). "What Is Agentic AI & Is It The Next Big Thing?". SSON.
  4. ^ a b 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. ^ Kwa, Thomas; West, Ben; Becker, Joel; Deng, Amy; Garcia, Katharyn; Hasin, Max; Jawhar, Sami; Kinniment, Megan; Rush, Nate; von Arx, Sydney; Bloom, Ryan; Broadley, Thomas; Du, Haoxing; Goodrich, Brian; Jurkovic, Nikola; Miles, Luke Harold; Nix, Seraphina; Lin, Tao; Parikh, Neev; Rein, David; Koba Sato, Lucas Jun; Wijk, Hjalmar; Ziegler, Daniel M.; Barnes, Elizabeth; Chan, Lawrence (March 19, 2025). "Measuring AI Ability to Complete Long Tasks". METR Blog. arXiv:2503.14499.
  6. ^ McCorduck, Pamela (2004). Machines who think : a personal inquiry into the history and prospects of artificial intelligence (25th anniversary update ed.). A.K. Peters, Natick, Mass., ©2004. ISBN 9781568812052.
  7. ^ 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.
  8. ^ Bandura, Albert (October 15, 2020). "Social Cognitive Theory: An Agentic Perspective". Psychology: The Journal of the Hellenic Psychological Society. 12 (3): 313. doi:10.12681/psy_hps.23964.
  9. ^ Catherine, Moore (July 28, 2016). "Albert Bandura: Self-Efficacy & Agentic Positive Psychology". PositivePsychology.com.
  10. ^ "The Evolution of Agentic AI: From Concept to Reality". AI World Journal. January 22, 2025.
  11. ^ Devlin, Kieran (March 6, 2025). "Salesforce To Empower Employee Experience with AgentExchange Agentic AI". UC Today. Retrieved March 13, 2025.
  12. ^ Shinde, Yogesh (August 23, 2024). "AI Robots : Transforming Industries with Smart Robotic Solutions". RoboticsTomorrow.
  13. ^ a b c d e f "Agentic AI: 6 promising use cases for business". CIO. June 19, 2025.
  14. ^ Sweeney, Erica. "Siemens' AI tools are harnessing 'human-machine collaboration' to help workers solve maintenance problems". Business Insider. Retrieved June 21, 2025.
  15. ^ "Siemens introduces AI agents for industrial automation". press.siemens.com. May 12, 2025. Retrieved June 21, 2025.
  16. ^ Noonan, Laura (July 31, 2017). "JPMorgan develops robot to execute trades". Financial Times.
  17. ^ "I Switched to Perplexity's AI Comet Browser for a Week. Is It the Future or Just Hype?". PCMAG. August 5, 2025. Retrieved August 9, 2025.
  18. ^ "How Helpful Is Operator, OpenAI's New A.I. Agent?". February 1, 2025. Retrieved August 9, 2025.
  19. ^ "I loved Arc browser and was skeptical of its agentic Dia replacement - until I tried it". ZDNET. Retrieved August 9, 2025.
  20. ^ a b 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.
  21. ^ 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
  22. ^ a b Challapally, Aditya. "The GenAI Devide: State Of AI In Business 2025" (PDF). MIT: 3. {{cite journal}}: line feed character in |title= at position 33 (help)
  23. ^ "Why 95% Of AI Pilots Fail, And What Business Leaders Should Do Instead". www.forbes.com. Retrieved August 26, 2025.
  24. ^ Estrada, Sheryl. "MIT report: 95% of generative AI pilots at companies are failing". Fortune. Retrieved August 26, 2025.
  25. ^ "4 new studies about agentic AI from the MIT Initiative on the Digital Economy | MIT Sloan". mitsloan.mit.edu. June 17, 2025. Retrieved August 26, 2025.
  26. ^ "AI investments failing? 95 per cent of firms see no returns, says MIT". The Indian Express. August 21, 2025. Retrieved August 26, 2025.
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