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 (APMS), when applied to process automation. Applications include software development, customer support, cybersecurity and business intelligence.

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]

Specifically, while reinforcement learning (RL) is essential in facilitating agentic AI in making autonomous choices by supporting agents in learning best actions through trial and error. Agents using RL continuously explore their surroundings, being given rewards or punishment for their actions, which better 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 (APMS) 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 rise of agentic AI has ignited discussions about bias, accountability, and transparency as autonomous systems make high-stakes decisions without human oversight. In 2024, the U.S. EEOC launched an investigation into HireVue, an AI-driven hiring platform, based on claims that its agentic AI displayed bias against individuals based on gender and race. The bias has been traced directly to improper training datasets. Similarly, Tesla's Full Self-Driving (FSD) system was under investigation by the NHTSA after drivers using its agentic AI misclassified stop signs as 'go' signs under specific light scenarios, resulting in risks and near-misses. Critics assert that these cases demonstrate a need for explainability frameworks (e.g., IBM's "AI FactSheets") to hold systems accountable.

Possible solutions include regulatory oversight and technical safeguards. The European Union AI Act (2025), the first comprehensive AI law, mandates risk assessments for agentic AI in critical sectors like healthcare. A review in Nature Medicine (2024) found that Google DeepMind's AI diagnostic tools prioritized speed over accuracy in rare disease detection. Companies like OpenAI and Anthropic (company) have pushed for "constitutional AI" frameworks with embedded ethical guardrails.^[14] However, experts warn that without global standards, patchwork regulations could hinder accountability, as seen in a 2023 lawsuit against Zillow, where its agentic pricing algorithm allegedly exacerbated housing discrimination.

As Agentic AI advances, it is believed that the incorporation of quantum computing will be a critical area of growth. Quantum computers have the ability to process massive volumes of data at unprecedented speeds, greatly improving the capabilities of agentic AI systems. Quantum algorithms may improve reinforcement learning, optimization, and other machine learning tasks, allowing agentic AI to make more correct decisions and handle complex datasets in real time. This could expedite progress in fields such as financial forecasting, climate modeling, and drug discovery, making agentic systems far more powerful and efficient than their traditional counterparts.^[15]

Agentic AI in personalized medicine has the potential to transform healthcare by evaluating patient data such as genetic information and medical history to give highly individualized treatments. AI systems might automatically change therapies in real time depending on fresh data, improving patient care and perhaps lowering human error. This would enable more effective and personalized precision medicine, perhaps leading to advances in areas such as oncology, cardiology, and genetic disorders. However, concerns such as data quality assurance and privacy protection must be solved in order to assure ethical and accurate AI-driven medical advice.^[16]

The future of autonomous transportation looks promising, with agentic AI playing a key role in self-driving cars and drones. These AI systems will continue to improve, becoming more capable of traversing complex settings, controlling traffic, and dealing with emergency situations. The incorporation of more powerful decision-making algorithms will enable autonomous vehicles to make more accurate real-time decisions, enhancing road safety and transit efficiency. Furthermore, autonomous fleets driven by agentic AI have the potential to streamline logistics and supply chains, transforming sectors that rely on transportation and delivery.

Agentic AI is expected to play an increasingly important role in creativity and design, generating original material in fields such as music, art, and product design. AI systems are already capable of producing creative works; but, as these systems get more complex, they may collaborate with human creators or even function as fully independent artists. Agentic AI's ability to analyze massive amounts of data and replicate creative processes has the potential to push the boundaries of creativity, resulting in previously imagined forms of artistic expression and product design.

Finally, ethics and governance will be critical to agentic AI's future growth. As AI systems become more autonomous, ethical concerns about transparency, accountability, and justice will have to be addressed. There is an increasing need for legislation and procedures to ensure that AI judgments are consistent with human values and social expectations. Ethical governance is critical for preventing biases, protecting data privacy, and maintaining public trust in autonomous systems. Research will continue to concentrate on developing transparent AI models and developing guidelines for the ethical application of these technologies in industries such as healthcare, transportation, and finance.

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]