In the rapidly evolving world of artificial intelligence, two terms are increasingly dominating conversations: “AI agents,” or just “agents,” and “agentic AI.” While they sound similar and are often used interchangeably, they represent fundamentally different approaches to AI functionality. Understanding this distinction isn’t just academic. It’s crucial when making informed decisions about which AI solutions will serve your business needs.
The natural evolution of AI terminology
As with many emerging technologies, AI terminology evolves rapidly and often outpaces our collective understanding. The terms “AI agents” and “agentic AI” have naturally found their way into everyday business conversations, sometimes used interchangeably even when they represent distinct concepts. This linguistic evolution is neither unusual nor problematic—it’s simply how language adapts to new realities.
However, this natural terminology shift does create challenges for decision-makers. I’ve seen numerous organizations grapple with understanding what they truly need. There’s a substantial difference between requiring a sophisticated system that can independently assess risk, make recommendations, and potentially take actions versus needing a conversational interface to existing data. When these distinct capabilities get grouped under similar terminology, it can become challenging to specify the right solution for your specific needs.
What are AI agents?
As defined in ISACA's Artificial Intelligence: A Primer on Machine Learning, Deep Learning, and Neural Networks, “An agent is the entity or machine and, more precisely, the learning algorithm that makes decisions and performs actions.” But here’s the crucial point: being an agent doesn’t automatically mean being autonomous.
At Anthropic, all variations of AI systems that use LLMs to accomplish tasks are categorized as agentic systems, but there’s an important architectural distinction between workflows and agents: workflows are systems where LLMs and tools are orchestrated through predefined code paths, while agents are systems where LLMs dynamically direct their own processes and tool usage.
AI agents are essentially interfaces or entities that respond to inputs and perform specific tasks. The more complex the environment and the more AI systems can be instructed in natural language to act autonomously on the user’s behalf, the more agentic they become. They include:
- Enterprise AI platform “Agents”: Model selection interfaces that help you choose between different AI models in enterprise AI platforms.
- Traditional chatbots: Responsive systems that follow input → processing → output patterns
- Customer service bots: Rule-following systems that process routine inquiries
None of these are truly autonomous. They respond to human direction and operate within predefined parameters.
Understanding agency vs. agents
The key distinction here lies in understanding the difference between an agent (the entity) and agency (the capability):
- An AI agent can exist without having true agency
- Agency implies the ability to independently assess, decide and act based on goals
- Traditional chatbots are agents because they respond to inputs, but they lack agency because they don’t pursue independent goals
What Is agentic AI?
Agentic AI represents a more advanced form of AI that possesses genuine agency—the capability for independent action. Unlike traditional AI, agentic AI systems are designed to operate with a high degree of autonomy, allowing them to independently perform tasks such as hypothesis generation, literature review, experimental design and data analysis. These systems can:
- Set their own goals and priorities
- Plan multi-step actions to achieve objectives
- Adapt and learn independently from experience
- Take initiative without explicit human prompting
- Operate proactively rather than reactively
Real-world examples: the difference in action
Customer service scenarios
AI agent (without agency):
- Responds to member inquiries with scripted answers
- Routes tickets based on keywords
- Provides information lookup from knowledge bases
- Follows predetermined decision trees
- Escalates when it hits programmed limits
Agentic AI (with agency):
- Independently assesses member needs and context
- Proactively identifies solutions across multiple systems
- Makes judgment calls about priority and urgency
- Adapts communication style based on member profile and history
- Takes initiative to prevent future issues (e.g., “I notice you’re asking about certification renewals—would you like me to check your CPE status?”)
ISACA-specific applications
For organizations like ISACA, this distinction translates to fundamentally different capabilities:
Traditional agent approach:
- A system that answers, “When is my certification due?”
Agentic AI approach:
- A system that proactively manages the member’s entire certification lifecycle, identifies gaps, suggests relevant training and coordinates renewal reminders
Capabilities table
| Characteristic | Recalculation | Remote Audit Procedure |
|---|---|---|
| Autonomy Level | Operate within predefined frameworks and require more human intervention for complex decisions | Can function with limited supervision, make independent decisions and initiate actions without explicit instructions |
| Goal Management | Execute tasks based on pre-set rules or objectives, but typically do not set or redefine their own goals | Can set and pursue goals, planning multi-step actions to achieve them and adjusting plans as needed |
| Adaptability | Have limited learning capabilities and may require manual updates or retraining for significant changes in their environment or tasks | Continuously learn from experience and feedback, adapting to new situations and even setting new goals |
| Proactiveness | Tend to be more reactive, responding to specific inputs or triggers | Can be proactive, anticipating needs or potential issues and acting without being explicitly prompted |
| Integration and Scale | Often standalone tools or components focused on specific functions | Can function as an umbrella technology, integrating multiple AI agents and systems to achieve broader objectives |
Orchestration
One of the most significant differences lies in how these systems operate within larger frameworks. Traditional AI agents often work independently on specific tasks; however, agentic AI involves orchestrating multiple components to enable autonomous operation.
Though it’s a commonly used analogy, I like thinking of agentic AI as an orchestra conductor coordinating an orchestra, making sure that each musician plays their instrument at the right time and in harmony with others. The key components that are orchestrated include:
- AI agents as building blocks for specific tasks
- Perception and input processing modules
- Memory and knowledge management systems
- Reasoning and planning engines
- Action and execution modules
- Tool integration capabilities
- Communication layers for multi-agent collaboration
- Integration and orchestration frameworks
- Monitoring, feedback and governance systems
Why “Autonomous” doesn’t mean “Independent”
The term “autonomous” in AI contexts often causes confusion because it doesn’t imply complete independence from humans. Instead, autonomy for AI agents means the ability to operate, make decisions and take actions independently within predefined parameters without requiring constant step-by-step human guidance.
Here’s why advanced AI systems are called “autonomous” even when they need human interaction:
- They pursue high-level objectives, goal-oriented actions, set by humans, breaking them down into manageable tasks.
- Unlike rigid rule-based systems, they are flexible and adapt to changing situations and make real-time decisions.
- They learn and improve over time through experience and feedback.
- They minimize—but do NOT eliminate—the need for constant human supervision.
- They can use integrated tools and interact with external systems to achieve their goals.
Practical business implications
Understanding these differences has real-world implications for different business scenarios:
Compliance monitoring
- An AI agent would flag compliance issues based on predefined rules.
- An agentic AI system would independently assess risk levels, investigate context and recommend specific actions.
Audit processes
- An AI agent would answer audit questions by referring to a knowledge base.
- An agentic AI system would independently identify audit priorities, design testing procedures and adapt based on findings.
Risk assessment
- An AI agent would calculate risk scores using predetermined formulas.
- An agentic AI system would independently investigate emerging risks, correlate data from multiple sources and proactively suggest mitigation strategies.
Making the right choice
The key question isn’t whether one approach is better than the other – it’s about matching the right solution to your specific needs.
When building applications with LLMs, experts recommend finding the simplest solution possible, and only increasing complexity when needed. This might mean not building agentic systems at all.
Choose AI agents when you need:
- Efficient interfaces to existing data
- Consistent responses to routine inquiries
- Cost-effective automation of well-defined tasks
- Predictable, rule-based operations
- Workflows that offer predictability and consistency for well-defined tasks
Choose agentic AI when you need:
- Systems that can independently assess and respond to complex situations
- Proactive problem-solving capabilities
- Adaptive responses to changing conditions
- Integration across multiple systems and data sources
- Flexibility and model-driven decision-making at scale
When evaluating AI solutions for your business, it's crucial to consider not just accuracy but also cost, as the expense of running different AI systems can vary dramatically while delivering similar results. This means that simpler AI agent solutions will be more cost-effective than complex agentic AI systems for many business applications.
Know the difference to make better AI decisions
While AI agents and agentic AI are related concepts, they represent different levels of sophistication and autonomy. AI agents excel at specific, well-defined tasks and serve as excellent tools for streamlining routine operations. On the other hand, agentic AI represents a shift in thinking toward systems that can think, plan and act more independently.
The terminology overlap in the marketplace often reflects the natural evolution of language around emerging technologies. As AI capabilities advance and become more accessible, different stakeholders—from developers to business leaders to solution providers—naturally adopt terminology to communicate these concepts. By understanding these distinctions, you can navigate the evolving landscape more effectively and make informed decisions about which AI solutions will deliver value for your organization.
Remember: not all agents are agentic AI, and not all business problems require agentic solutions. The key is matching the right level of AI capability to your specific needs, budget and risk tolerance.
As the field continues to evolve, we’re likely to see hybrid approaches where different types of agents manage various tasks within larger systems. The important thing is to understand what you’re buying and implementing.
