Agentic AI

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

The core concept of agentic AI is the use of AI agents to perform automated tasks but without human intervention. 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. Agentic AI operates independently, making decisions through continuous learning and analysis of external data and complex data sets. Functioning agents can require various AI techniques, such as natural language processing, machine learning (ML), and computer vision, depending on the environment.

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. 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.

History

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.

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. 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. The psychological principle of agency was also discussed in the 2008 work of sociologist Albert Bandura, who studied how humans can shape their environments. This research would shape how humans modeled and developed artificial intelligence agents.

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.

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. 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.

In 2025, research firm Forrester named agentic AI a top emerging technology for 2025.

Applications

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.
• 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.
• Enterprise workflows - AI agents can automatically automate routine tasks by processing pooled data, as opposed to a company needing APIs preprogrammed for specific tasks.
• 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.
• Business intelligence - AI agents can support business intelligence to produce more useful analytics, such as responding to natural language voice prompts.
• 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%.
  • 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.

Emerging Frameworks and Classification Systems

The rapid development of agentic AI has prompted efforts to establish classification systems and frameworks to distinguish these systems from traditional AI agents and guide their implementation.

Academic and Industry Taxonomies

Academic researchers have developed formal taxonomies to clarify agentic AI capabilities and levels of autonomy. Research distinguishes agentic AI systems from traditional single AI agents, highlighting characteristics such as orchestrated multi-agent structures, persistent memory systems, and proactive goal-driven behavior.

Industry Classification Systems

Several organizations have developed comprehensive frameworks for categorizing agentic AI capabilities and maturity levels:

Digital Twin Consortium Framework: The Digital Twin Consortium introduced the AI Agent Capabilities Periodic Table (AIA CPT) in 2025, defining 45 distinct capabilities organized into six core categories: Perception & Knowledge, Cognition & Reasoning, Learning & Adaptation, Action & Execution, Interaction & Collaboration, and Governance & Safety.

The framework establishes a five-level maturity classification:

Type	Description	Example Applications
0	Static Automation – Predefined responses without learning or adaptation	Rule-based control systems
1	Conversational Agents – Basic natural language interaction with simple context handling	Chatbots, voice assistants
2	Procedural Workflow Agents – Execute multi-step tasks using decision logic and tool integration	Workflow automation, RPA
3	Cognitive Autonomous Agents – Plan, reason, and learn from experience to make self-directed decisions	Predictive maintenance, optimization
4	Multi-Agent Generative Systems (MAGS) – Teams of collaborative agents with distributed reasoning and emergent behavior	Industrial orchestration, smart cities

The AIA CPT framework has been applied in industry testbeds, including automated negotiation systems and adaptive manufacturing process control.

Salesforce Maturity Model: Salesforce's Agentic AI Maturity Model outlines progression from rule-based chatbots through single-domain "co-pilots" to multi-agent orchestration systems where heterogeneous agents collaborate autonomously across enterprise technology stacks.

Enterprise Architecture Models: Enterprise architecture experts have proposed five-stage models ranging from "AI as a Tool" (basic deterministic automation) to "AI as an Autonomous Force" (AI-driven processes with near-complete autonomy), emphasizing the need for adaptive governance as organizations progress toward fully agentic AI.

Sema4.ai Enterprise Agent Maturity Model''': Sema4.ai has developed a five-level enterprise agent maturity model that provides a structured approach to evaluating and implementing AI agents within organizational contexts, focusing on enterprise readiness and deployment considerations.

Government and International Standards

NIST AI Risk Management Framework: The U.S. National Institute of Standards and Technology released the AI Risk Management Framework (AI RMF) in 2023, providing a voluntary framework for incorporating trustworthiness considerations into AI system design, development, and deployment. The framework establishes core functions to Govern, Map, Measure, and Manage AI risks while defining characteristics of trustworthy AI systems.

IEEE Standards: The Institute of Electrical and Electronics Engineers is developing multiple standards for autonomous and intelligent systems, including IEEE 7000 series standards for ethically aligned design, IEEE P3428 for Large Language Model Agents in education, and IEEE P2817 for verification of autonomous systems.

ISO/IEC Standards: The International Organization for Standardization has developed ISO/IEC 23053:2022, providing a framework for AI systems using machine learning, and is working on additional standards for AI system governance and risk management.

EU AI Act Standards: The European Union's AI Act implementation is supported by harmonized standards being developed by CEN-CENELEC Joint Technical Committee 21, focusing on AI trustworthiness, risk management, quality management systems, and conformity assessment procedures.

OECD AI Framework: The Organisation for Economic Co-operation and Development has established a framework for classifying AI systems across multiple dimensions including People & Planet, Economic Context, Data & Input, AI Model, and Task & Output, supporting policy-making and governance approaches.

Development Frameworks

Multiple frameworks have emerged for building agentic AI systems, each with distinct architectural approaches. Industry analyses highlight different design philosophies among frameworks such as LangChain's emphasis on modularity, CrewAI's focus on role-based collaboration, and Microsoft AutoGen's enterprise scalability features.

These frameworks provide predefined architectures, communication protocols, and integration tools to streamline agent development, with some specializing in particular domains such as industrial operations or conversational AI applications.

Industry Analysis

Market research firms have begun analyzing agentic AI as an emerging technology with significant disruptive potential. Gartner positioned agentic AI as a disruptive technology in their 2025 Emerging Tech Impact Radar report, placing it in the 6-8 year horizon for widespread enterprise adoption.

According to Gartner's analysis, agentic AI refers to "various architectures, techniques, and frameworks for creating single-agent or collaborative multiagent systems capable of unsupervised task execution." The research identifies enterprise concerns around trusted autonomy as a key factor affecting the adoption timeline.

The report identified several sample vendors working in the agentic AI space, including Adept, Beam, CrewAI, Induced.AI, Meta, Microsoft, Multi-On, Orby.AI, and XMPro, representing diverse approaches to agentic AI implementation across different industries and use cases.

Industry surveys indicate that reliability and oversight remain primary concerns for organizations considering agentic AI adoption, with many pursuing phased implementation approaches that maintain human supervision while gradually increasing agent autonomy.

Related concepts

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.

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.

Robotic process automation (RPA) describes how software tools can automate repetitive tasks, with predefined workflows and structured data handling. 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.

See also

• Intelligent agent
• Model Context Protocol
• Rational agent
• Software agent