Agentic AI

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] Agentic AI operates independently, making decisions through continuous learning and analysis of external data and complex data sets.^[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, while reinforcement learning (RL) is essential in assissting agentic AI in making self-directed choices by supporting agents in learning best actions through 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. 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. RL combined with deep learning thus supports the use of AI agents to adjust dynamically, optimize procedures, and engage in complex behaviors with limited control from humans.^{[citation needed]}

Some scholars trace the conceptual roots of agentic AI to Alan Turing's mid-20th century work with machine intelligence and Norbert Wiener's work on feedback systems.^[4] 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.^[5] The psychological principle of agency was also discussed in the 2008 work of sociologist Albert Bandura, who studied how humans can shape their environments.^[6] This research would shape how humans modeled and developed artificial intelligence agents.^[7]

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.^[4][8]

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 decision 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, while^[9] AI-powered robot technology boosts productivity by adapting the changes in real time. Similarly, banks and financial institutions deploy agentic AI in predictive analytics and algorithmic trading, which illustrates its expanding applications in high-stakes decision-making.

In 2025, research firm Forrester named agentic AI a top emerging technology for 2025.^[10]

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.^[10]
• Customer support automation - AI agents can improve customer service by improving the ability of chatbots to answer a wider variety of questions, rather than having a limited set of answers pre-programmed by humans.^[10]
• Enterprise workflows - AI agents can automatically automate routine tasks by processing pooled data, as opposed to a company needing APIs preprogrammed for specific tasks.^[10]
• 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.^[10]
• Business intelligence - AI agents can support business intelligence to produce more useful analytics, such as responding to natural language voice prompts.^[10]
• 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%.^[11]
  • 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.^[12]
  • Medical Diagnostics - Google partnered with Moorfield's Eye Hospital and detected eye diseases by analyzing 3D eye scans achieving 94% accuracy in trials.*Healthcare Dive*. 2018.^[13]
  • Retail and Customer service - Walmart uses AI chatbots to handle 80% of customer inquiries autonomously, including returns and inventory queries.

The development of agentic AI has triggered discussions about bias, accountability, and transparency because autonomous systems are making high-stakes decisions without human intervention. In 2024, the U.S. EEOC initiated an investigation against HireVue, an AI driven recruitment platform, after complaints were raised that its agentic AI discriminated against individuals on the gender and racial basis. The bias has been directly attributed to poor training data. In parallel, Tesla's Full Self-Driving (FSD) system was probed by the NHTSA after drivers using its agentic AI misclassified stop signs as 'go' signs under specific light scenarios, which resulted in risks and a number of near-misses. AI Critics are of the view that such cases establish that explainability frameworks (e.g., IBM's "AI FactSheets") must be deployed in order to hold systems responsible.

Possible solutions to these concerns include regulatory oversight as well as technical safeguards. The European Union's AI Act of 2025, the first comprehensive law on AI, mandates risk assessment for agentic AI in high-risk sectors like healthcare. The Nature Medicine (2024) review concluded that Google DeepMind's AI diagnostic tools were found to prioritize speed over accuracy in rare disease detection. Additionally, there has been a push by AI companies (e.g., OpenAI and Anthropic) towards "constitutional AI" frameworks, which involve thoroughly embedding ethical "guardrails" (e.g., fairness checks) within agentic AI systems. Experts warn, however, that without global standards, patchwork regulations could hinder accountability—as seen in 2023’s AI liability lawsuit against Zillow, where its agentic pricing algorithm allegedly exacerbated housing discrimination.

As Agentic AI grows, quantum computing will enhance its capabilities by processing massive volumes of data at unprecedented speeds. Quantum algorithms can improve reinforcement learning, optimization, and other machine learning tasks, allowing Agentic AI to process large datasets in real time. This could hasten advancements in financial forecasts, climate modeling, and drug discovery, making agentic AI significantly more powerful than traditional systems.^{[14] [15]}

Human-AI collaboration will become increasingly important as agentic AI advances. AI will assist human decision-making by providing data-driven insights and predictive analytics in areas such as healthcare, law, finance, and government. This partnership will help specialists make better informed decisions, especially in medical diagnosis, investment strategies, and legal analysis, where human judgment is essential.

Agentic AI has the potential to transform personalized medicine by evaluating patient data, such as genetic information, and providing individualized treatments. AI can alter medicines in real time, which improves patient care and reduces errors. This will allow for more precise therapies in disciplines such as oncology, cardiology, and genetic illnesses; nevertheless, issues such as data quality and privacy must be addressed to ensure ethical AI use.^[16]

The future of autonomous transportation is bright, with Agentic AI improving self-driving cars and drones. AI systems will improve real-time decision-making, resulting in safer and more efficient transportation. Autonomous fleets driven by Agentic AI will also improve logistics and supply chains, changing sectors that rely on transportation and delivery.

Agentic AI will make a greater contribution to creativity, producing original works in music, art, and product design. As AI systems progress, they may collaborate with human creators or even act as independent artists, pushing the limits of creativity by analyzing massive amounts of data and reproducing artistic processes.

In space exploration, Agentic AI will allow spaceships and planetary rovers to navigate unknown terrains with little human interaction. These autonomous technologies will aid in the exploration of new planets, the optimization of resource utilization, and the maintenance of life-support technology throughout long voyages, thereby considerably increasing humanity's reach into space.

Agentic automation, sometimes referred to as agentic process automation, refers to applying agentic AI to generate and operate workflows. In one example, large language models can construct and execute automated (agentic) workflows, reducing or eliminating the need for human intervention.^[17]

While agentic AI is characterized by its decision-making and action-taking capabilities, generative AI is distinguished by its ability to generate original content based on learned patterns.^[3]

Robotic process automation (RPA) describes how software tools can automate repetitive tasks, with predefined workflows and structured data handling.^[2] RPA's static instructions limit its value. Agentic AI is more dynamic, allowing unstructured data to be processed and analyzed, including contextual analysis, and allowing interaction with users.^[2]