Agentic AI explained: Key concepts and enterprise use cases

How does agentic AI differ from traditional AI?

Traditional AI systems are designed to solve specific, predefined tasks, although many do display adaptability within that purview. Traditional AI includes everything from simple rule-based AI algorithms to complex systems, such as generative AI (GenAI) models like ChatGPT that rely on advanced natural language processing (NLP) techniques. Traditional AI primarily responds to user input or environmental stimuli, and it lacks truly autonomous, goal-driven behavior. Its task-specific boundaries require at least some human input and oversight.

In contrast, agentic AI systems are designed to take the initiative in pursuing objectives. Rather than executing commands or routines set by humans, agentic AI systems adjust their strategies and explore their environments in real time. Agentic AI extends the scope of what AI can do, encompassing levels of reasoning, planning and autonomy that traditional AI -- even highly advanced forms of it -- can't match. This independent, goal-oriented behavior gives agentic AI its "agency."

Agentic AI vs. GenAI and LLM chatbots

Agentic AI and GenAI differ mainly in their autonomy and scope. Both technologies are sophisticated, but their purposes, capabilities and degrees of independence differ.

Generative AI tools, such as image generators and chatbots based on large language models (LLMs), create content in response to users' prompts. In this way, they're fundamentally reactive. But they can behave in seemingly proactive ways -- for example, asking follow-up questions to continue a conversation or searching the internet to generate outputs. But GenAI is ultimately dependent on user input and unable to set its own goals or reason independently.

Agentic AI systems, in contrast, are actively engaged in their tasks and environment. Unlike GenAI tools, they're not restricted to responding to user prompts. Even if agentic AI systems are guided by goals that humans initially define, they can act alone and are capable of free-form interactions with the real world.

Core capabilities of agentic AI

Agentic AI systems consist of complex workflows: multiple AI agents, resources and services that work together to complete goals. Tasks are divided into subtasks and allocated to appropriate AI agents for completion.

Agentic AI systems can use various types of agents. Each agent type interprets input and completes tasks in unique ways. Simple reflex agents, for example, use predefined rules to determine task execution, whereas learning agents use previous actions to learn and correct behavior when approaching new problems.

AI agents possess the following capabilities to complete goals effectively:

• Perception. AI agents can perceive real-time aspects of their environment through interactions with users, APIs, databases and sensor data. Perception enables agents to collect the necessary information to execute tasks effectively.
• Knowledge retrieval. AI agents can access external data and information -- often including domain knowledge through agentic RAG capabilities -- enabling them to make informed decisions and better answer user queries and complete actions.
• Reasoning and goal setting. Using a range of techniques, such as NLP and computer vision, AI agents can analyze collected data to understand context, identify patterns and set goals.
• Decision-making and execution. Once an AI agent sets its goal, it can evaluate possible tasks and subtasks using various techniques, such as machine learning (ML) algorithms and probabilistic models, to decide and execute optimal actions.
• Orchestration and collaboration. In complex agentic AI systems, multiple AI agents often collaborate to achieve their goals. Agents, therefore, can possess orchestration and collaboration capabilities, enabling them to delegate subtasks to other agents and work together to achieve their goals.
• Learning and adaptation. Some AI agents can store and access short- and long-term memory. This ability, along with using gathered feedback and ML techniques like reinforcement learning, helps agents continuously learn from past actions to guide future ones, so they can refine task planning and execution over time.

With so many capabilities, businesses often turn to benchmarking agents to monitor their performance and productivity. The benchmarking of agents differs from benchmarking LLMs because agent evaluation must account for the dynamic, independent actions agents can take. Agent benchmarks, such as AgentBench, ALFWorld and several others, are leading the charge to help organizations evaluate agentic capabilities and business outcomes, including cost-effectiveness and time to completion. However, benchmarks for AI agents are still in their infancy.

Key benefits of agentic AI in the enterprise

Agentic AI offers numerous business benefits, primarily related to its ability to operate independently and adaptively. The main advantages of agentic AI include the following:

• Autonomy. The defining feature of agentic AI systems is their ability to function without constant human oversight. Agentic automation saves time and manual effort in a wide range of use cases.
• Flexibility. AI agents' ability to adapt to changes in their environment likewise offers significant benefits in terms of time and labor savings. Since agentic AI systems dynamically adjust their behavior in response to environmental changes, they can handle new or ambiguous information without requiring human guidance.
• Problem-solving. With its planning, reasoning and goal-setting abilities, agentic AI can solve problems well beyond what traditional AI can handle. Teams of AI agents can also work together to address complex scenarios.
• Creativity. Agentic AI can generate new insights and research ideas by analyzing data sets in ways that humans do not. AI creativity differs from human creativity. It is driven by pattern recognition and data extrapolation rather than abstract thought or intuition. This can produce unusual perspectives and experimental setups in areas like scientific R&D.
• Time and labor efficiency. AI agents often work much more quickly and efficiently than humans, improving productivity. Although AI systems make their own types of mistakes, they can reduce human error by automating repetitive tasks and ensuring consistency. Agentic AI systems can even self-optimize by analyzing their own behavior and taking steps to fine-tune their workflows.

Risks, challenges and ethical considerations of agentic AI

Agentic AI raises significant concerns, primarily surrounding its autonomous nature. Major risks and drawbacks of agentic AI include the following:

• Security. If hacked or compromised by malicious actors, agentic AI systems pose serious threats. Highly autonomous AI systems introduce new security vulnerabilities due to their independent decision-making and lack of monitoring. A compromised AI agent could be manipulated to make harmful decisions. Agentic AI systems could also be vulnerable to adversarial attacks, when minor manipulations in data inputs lead to significant unintended outcomes.
• Unexpected problematic behavior. Even traditional AI can behave unpredictably or counterproductively. Agentic AI's autonomy increases this risk. Since AI agents operate without real-time human oversight, they might carry out tasks in ways that their creators and users didn't anticipate. Moreover, if a model's decision-making or goal-setting processes aren't transparent, it's challenging for humans to comprehend or rectify problematic behavior.
• Energy and resource costs. Complex agentic AI systems that require extensive resources consume significant amounts of energy, resulting in environmental damage and high operational costs. Although not every AI agent necessarily requires massive resources, large-scale AI systems require substantial power and data, creating concerns about sustainability as AI's demand for energy and water grows.
• Compliance and governance issues. Agentic AI introduces new levels of autonomy across enterprise environments, touches multiple layers of infrastructure and data, and often interacts directly with users. With this level of agency and complexity comes new compliance and regulation concerns. Agentic AI systems can expose sensitive data, infringe on copyrighted material or violate other regulatory agreements.
• Ethical and social concerns. Agentic AI raises profound ethical and social questions. If an AI agent makes a decision that results in harm, determining responsibility can be difficult. Should the blame fall on the developers, the organization using the AI or the AI system itself? Another concern is bias. If agentic AI systems make decisions in areas like law enforcement or hiring, they could reflect any biases present in their training data, leading to unfair or discriminatory outcomes. Furthermore, if agentic AI automates many tasks traditionally performed by humans, it could lead to significant job displacement.
• Lack of human control. Reducing the need for human oversight can increase efficiency, but it can also make it difficult for humans to monitor and stop system behavior in real time. Proposed safety measures such as kill switches aim to enable human operators to stop agentic AI systems, but developing such mechanisms is complicated. This concern might be speculative, but some AI researchers worry that autonomous AI agents could develop goals misaligned with human values, posing a threat to humanity.

Agentic AI use cases

Agentic AI use cases span consumer, research and business applications, including the following:

• Customer service. AI agents are particularly useful for customer service tasks such as orchestrating call centers or other forms of communication. AI agents, for example, can analyze queries, offer personalized responses and trigger workflows to aid customer experiences.
• Healthcare. AI agents can complete various healthcare tasks, such as scheduling appointments, processing insurance claims and even working with electronic health records for diagnostics. 
• Manufacturing. AI agents can enhance certain manufacturing tasks. Adding agentic capabilities to manufacturing robots, for instance, further enhances their ability to respond to unexpected scenarios such as anomalies on a conveyor belt.
• Multimedia. Agentic AI's ability to handle detailed tasks makes it well-equipped for multimedia generation. AI agents can manage and complete subtasks such as research, image selection and text generation, making the output more refined than that of simpler generative AI tools.
• Research. AI agents can accelerate research measures, particularly in areas like scientific discovery, by orchestrating various aspects of the research process. AWS, for example, is pairing with pharmaceutical companies to streamline the R&D phases of drug discovery with AI agents.
• Sales and marketing. Agentic AI can aid sales and marketing campaigns by automating routine tasks, connecting to CRMs to monitor customer behavior and providing real-time insights and forecasting.
• Security. AI agents can orchestrate security operations by scanning for threats, investigating unusual activity and automatically responding to issues.
• Supply chain management. AI agents can manage supply chain workflows. Agentic AI, for example, can predict demand, optimize inventory and coordinate shipments while dynamically responding to disruptions such as weather conditions or transportation delays.

Enterprise considerations for agentic AI

Agentic AI has numerous potential applications in enterprise settings. The introduction of agentic AI to these use cases -- many of which are already partially automated -- significantly minimizes the need for human involvement. But humans must still take charge of the development and oversight of agentic systems.

Businesses should consider their options when it comes to agentic AI development strategies and automation alternatives. Agentic AI isn't the only way businesses can automate workflows and certainly not the first. Automation techniques, such as robotic process automation (RPA), can sometimes offer a simpler and more straightforward approach to specific tasks.

However, if a business decides that agentic AI is the optimal path for its use case, its next consideration is whether to build an agentic system from scratch. That involves strategic planning and development of agent elements, such as architectural components, agent memory and error handling, which require significant technical expertise and resources. Alternatively, organizations can use AI agent frameworks and platforms to assist in building agentic systems.

Agentic systems also require comprehensive management, including oversight of system elements such as architecture, data planning, workflows and tool integrations. To ensure humans remain in the loop, businesses often need to identify new responsibilities and dedicate teams to different aspects of agentic system management.

AI orchestration tools can help oversee aspects of agentic AI environments to increase system efficiency and lower costs. Some enterprises also turn to AgentOps -- a technique for managing the tools, systems and practices needed for agentic AI, mirrored after other methods in the Ops vernacular, such as MLOps and DevOps.

Lastly, businesses must adopt strong governance strategies for their AI agents and workflows. Measures can include implementing agent permissions, data retention, lifecycle management, transparency, explainability, compliance assessments and staff training.

System oversight and governance for agentic AI initiatives can improve agent reliability, performance, integration, security, regulatory compliance, resource use and cost effectiveness.

Agentic AI frameworks and platforms

The complexity of agentic AI often warrants the need for frameworks and platforms, which serve different functions depending on an organization's needs and skill levels. Agentic frameworks are toolkits designed for developers to create agentic AI systems. In contrast, agentic platforms are business-oriented applications for developing and managing agentic AI and designed for those with less technical expertise.

Agentic frameworks provide customization, modularity, orchestration and tool integration flexibility. Framework options include the following:

• AutoGen is a highly customizable framework for developers looking to build multi-agent applications. It enables agentic system orchestration through conversational exchanges, event-driven architecture and built-in tracing capabilities.
• CrewAI enables developers to define agent roles, goals and tools, and can assemble agents into teams for task completion. CrewAI is particularly helpful for team-based agentic workflows, such as problem-solving and content creation.
• LangChain connects LLMs with external components and data sources, offering an extensive range of integrations and tools to aid developers in building AI agents.
• LangGraph extends LangChain capabilities, using graph-based architecture to aid agentic AI development. LangGraph is well-suited for complex, multi-agent coordination, hierarchical agents and systems that require precision control.

Agentic platforms often provide low-code and no-code interfaces, along with business-facing needs such as enterprise integrations, scalability, governance and compliance. Platform options include the following:

• Agentforce specializes in customer service and sales use cases, enabling businesses to build AI agent workflows and systems for various tasks.
• Bedrock AgentCore provides enterprise capabilities for developing and managing AI agents at scale, with seven core services and a serverless architecture.
• Copilot Studio is integrated with the Microsoft 365 ecosystem, enabling businesses to build and manage AI agents for automation tasks. It's well-suited for knowledge management and department automation.
• Vertex AI Agent Builder provides integration connectors and customer-managed encryption keys for businesses to build and manage agentic systems. The platform is suited for document processing, enterprise search and multi-agent workflow orchestration.
• WatsonX Orchestrate enables businesses to simplify the building, integration and management of AI agent workflows. It prioritizes natural language interaction, hybrid cloud automation and governance, among other capabilities, making it especially helpful for businesses that need flexible deployment options.

The landscape for agentic frameworks and platforms is still in its early stages of development. More business tools are emerging, including OpenAI Agents SDK and AWS Multi-Agent Orchestrator.

Future of agentic AI in the enterprise

From boardrooms to machine learning conferences, 2025 has been marked by rampant conversations about and adoption of agentic AI. In a 2025 survey, consulting firm PwC found that 79% of surveyed executives were adopting AI agents into their organizations, with 66% of them reporting productivity increases.

Yet many leaders still struggle to determine how to integrate AI agents into their workflows or services, and many more are trying to identify how their pilot projects return actual business value. Very few full-scale agentic AI initiatives exist today.

Gartner noted in a recent press release that current agentic AI supply actually exceeds demand. Analysts forecast that the agentic AI market will therefore consolidate soon, marking a natural transition period to reach a steady state. Moving forward, industry leaders will develop targeted products to meet specific business needs, thereby facilitating the widespread adoption of agentic AI.

"Large providers will establish expansive, integrated ecosystems that significantly improve agentic performance, leading to more reliable products targeted at specific business outcomes," said Gartner analyst Will Sommer in the press release.

As the maturation process continues for agentic AI in the enterprise, look for these developments to shape the future of agentic AI:

• Specialized agents. General-purpose agents could become obsolete as businesses opt for specialized, vertical AI agents fine-tuned for specific industry verticals such as finance, healthcare and law. With deep expertise and the ability to use domain knowledge to complete tasks, they are well-equipped to handle complex tasks that general-purpose agents can't.
• Multi-agent ecosystems. With the introduction of frameworks and platforms, along with increased technical expertise, many businesses are beginning to brainstorm complex multi-agent ecosystems that can solve a variety of tasks. As multi-agent ecosystems become the norm, businesses might require new skills as well as the resources and products that meet orchestration needs.
• Increased task complexity. As with any technology, innovation remains at the forefront for agentic AI. As companies urge their teams to dream bigger when it comes to use cases, AI agents will increasingly be tasked to take on more complex objectives, requiring agents to have more significant collaborative skills and integration abilities to meet growing complexity.
• Greater emphasis on governance and compliance. Increasingly complex use cases will foster growing interest in regulations and governance policies geared toward agentic systems. Agentic governance could encompass explicit agentic regulations and platforms that prioritize effective governance management.
• Agentic AI-focused employee upskilling. Agentic AI raises concerns about whether AI agents are displacing human jobs and what businesses can do to address this issue. One emerging avenue is employee upskilling. Businesses will increasingly invest in -- and many have already done so -- teaching their workers new skills and responsibilities when AI automates parts of their roles. Agentic-focused upskilling might include creating new roles to build and manage systems, developing AI education programs as more workflows become hybrid human-AI teams and reskilling employees whose jobs become partially or entirely obsolete.
• Agentic AI integrating with the physical world. Agentic AI's automation capabilities could increasingly be applied to physical AI use cases. Self-driving vehicles, smart cities with AI-powered infrastructure, and healthcare advancements in robotics and AI-powered tools could use agentic AI capabilities to enable enhanced autonomous decision-making and movement through real-world environments.  

Olivia Wisbey is a site editor for Informa TechTarget and has experience covering AI, machine learning and software quality topics.

Lev Craig covered AI and machine learning as a site editor for Informa TechTarget.