Agentic AI vs AI agents: what’s the difference?

AI agents vs agentic AI: what’s the difference, and which is better suited for your needs? If you’ve been following AI trends, you’ve probably encountered both terms, often used interchangeably, yet they represent two distinct approaches to building intelligent systems.

AI agents are designed to perform specific, predefined tasks within set parameters. On the other hand, agentic AI operates with greater autonomy, pursuing goals, making decisions, and adapting to new information in real time.

In this blog, we’ll break down the core differences between agentic AI and AI agents, compare their capabilities, and explore real-world use cases across industries. You’ll also see how these technologies are evolving, and what that means for businesses, innovators, and everyday users.

What is agentic AI?

Agentic AI is a type of artificial intelligence that can work on its own instead of waiting for step-by-step instructions. It can set its own goals, make decisions, and change its actions when new information comes in.

This is different from traditional AI, which only does what it’s told. For example, a regular AI might create a report only when you ask for it. Agentic AI, on the other hand, might notice a problem in the data, decide the report is needed, prepare it, and even suggest ways to fix the issue, without being told to do so.

It’s like the difference between an assistant who waits for tasks and one who spots problems, plans the work, and takes action on their own.

What are AI agents?

AI agents are programs or systems that follow predefined instructions to complete specific tasks. They work within set boundaries and usually need a prompt or trigger to take action. While they can process information and make simple decisions, they don’t set their own goals or adapt beyond what they are programmed to do.

This is different from agentic AI, which can work autonomously and make its own choices. For example, an AI agent in customer service might respond to a customer query using a scripted workflow or pull information from a database when asked, but it won’t proactively investigate issues or take independent action.

It’s like the difference between an assistant who completes assigned tasks exactly as instructed and one who needs you to tell them what to do every time.

Agentic AI vs AI agents: a quick comparison

Here’s a quick side-by-side overview of the key differences between agentic AI and AI agents, covering aspects like autonomy, complexity, adaptability, and use cases. While this comparison focuses on technical traits, it also helps clarify broader distinctions, such as agentic AI vs conversational AI, where the differences lie more in goal-driven behavior versus dialog-oriented interaction.

In-depth comparison between agentic AI and AI agents

While both agentic AI and AI agents can automate tasks and boost productivity, they differ significantly in independence, adaptability, and strategic thinking. Understanding these differences helps businesses choose the right approach for their goals and operational needs.

1. Autonomy level

Agentic AI functions with a high degree of independence, capable of identifying what needs to be done without waiting for explicit instructions. It can initiate actions, plan multi-step processes, and adapt workflows on the fly based on evolving conditions. This autonomy allows it to operate in dynamic environments, where priorities and data inputs can change rapidly.

AI agents, on the other hand, operate with limited autonomy. They typically require a clear trigger, such as a user command, an API call, or a predefined event, before they act. While they can handle tasks efficiently once activated, they lack the ability to independently detect and pursue objectives outside their programmed scope.

2. Goal orientation

Agentic AI works toward achieving high-level objectives rather than simply executing discrete commands. It can break down a large goal into smaller, actionable steps, sequence them in the most efficient order, and adjust its plan when obstacles arise. This goal-first orientation makes it suitable for strategic, long-term initiatives.

AI agents focus on completing the specific task they are assigned, without understanding or aligning with a broader mission. If the overall goal changes, they will still execute their given instructions unless reprogrammed or retriggered. This makes them better suited for tactical, one-off activities rather than holistic goal execution.

3. Learning capabilities

Agentic AI continuously learns from its actions, feedback, and environmental changes. It can refine its strategies, update its knowledge base, and improve decision-making in real time. This adaptability enables it to handle novel situations and grow more efficient over time without requiring frequent manual retraining.

AI agents generally work on fixed rule sets or pre-trained models. Any learning or performance improvement typically depends on external intervention, such as developer updates or periodic retraining. As a result, their ability to adapt to unforeseen scenarios is limited compared to agentic AI.

4. Complexity

Agentic AI is built to handle complex, multi-layered challenges involving uncertainty, dependencies, and cross-system coordination. It can analyze multiple streams of input, identify interrelated factors, and decide how to prioritize and resolve them, even in fluid environments.

AI agents are better at handling predictable, clearly defined tasks with minimal dependencies. They perform best when the scope is narrow, the data inputs are consistent, and the workflow follows a repeatable pattern. Complex, ambiguous problems may require chaining multiple agents together or adding human oversight.

5. Decision-making

Agentic AI uses context-aware reasoning to evaluate multiple possible solutions, weighing trade-offs before choosing the best course of action. It can adjust decisions mid-process if new data emerges, ensuring outcomes remain aligned with the overarching goal.

AI agents rely on predetermined rules or fixed logic trees, selecting from a limited set of responses. While this makes their behaviour predictable and stable, it also means they can struggle with ambiguity, incomplete information, or situations that require weighing different options.

Real-world use cases of AI agents and agentic AI

From everyday convenience to complex problem-solving, both AI agents and agentic AI are already shaping how we work, shop, and live.

While AI agents excel at executing well-defined tasks efficiently, agentic AI thrives in scenarios requiring autonomy, adaptability, and multi-step reasoning. Below are examples of how each is applied across industries today.

Use cases of AI agents

1. Customer service automation

AI agents in customer service, such as Freshdesk’s Freddy AI, act as virtual representatives that assist with tasks like answering FAQs, routing tickets to the right department, and triggering automated workflows. By working within well-defined parameters, they ensure speed, accuracy, and consistency in resolving recurring queries, while freeing human agents to focus on more complex issues.

2. Smart homes

In smart homes, AI agents manage individual devices like thermostats, lights, or security systems based on user settings or sensor data. For example, a thermostat agent can adjust the temperature when motion sensors detect movement, but it won’t independently decide to optimize for energy savings unless programmed to do so.

3. Virtual assistants

AI agents like Siri, Alexa, or Google Assistant respond to direct user commands like setting reminders, sending messages, or playing music. Their functionality is triggered by voice prompts or specific requests, and they rely on pre-set integrations rather than autonomous problem-solving.

4. eCommerce recommendation engines

Recommendation agents in eCommerce analyze customer browsing and purchase history to suggest relevant products. They operate on algorithms that match user profiles with available inventory, making the experience more personalized without taking independent action beyond suggesting items.

Use cases of agentic AI

1. Autonomous ticket triage

In a customer service software like Freshdesk, agentic AI can autonomously read incoming tickets, classify them by urgency and category, and assign them to the best-suited agent without human intervention. It adapts routing decisions in real time based on workload, ticket complexity, and evolving customer priorities.

2. AutoGPT

Tools like AutoGPT showcase agentic AI’s ability to work toward high-level objectives without constant input. Given a goal such as “research top market trends and create a report,” it can break the task into steps, gather data, analyze findings, and produce a deliverable without requiring repeated prompts.

3. AI software engineers

Agentic AI models can plan, write, test, and debug code with minimal supervision. They’re capable of identifying the requirements of a project, sequencing development tasks, and making design choices, effectively functioning as autonomous collaborators in software projects.

4. Multi-agent collaboration systems

Here, multiple agentic AI systems work together toward a shared objective, each taking responsibility for different subtasks and communicating progress to the others. For example, in product development, one AI could handle market research, another could design prototypes, and a third could plan launch strategies, all coordinating automatically.

Meet Freddy AI—the smartest AI agent for customer service

Freddy AI, built into Freshdesk, is an advanced customer service AI agent and copilot designed to transform how businesses deliver customer support. It helps teams work smarter by automating repetitive tasks such as ticket routing, categorization, and response suggestions, while also offering omnichannel support across email, chat, WhatsApp, social media, and more for a consistent experience. The impact?

• <2 mins average conversational resolution time

• 83% reduction in response rates

• 60% higher agent productivity

Freddy AI can instantly answer FAQs, summarize conversations for faster context switching, process real-time actions like updating orders or fetching shipping details, and even suggest next best actions for agents. Beyond Freddy AI, Freshdesk’s AI capabilities extend into areas like intent detection, sentiment analysis, and proactive customer engagement.

Together, these tools empower support teams to anticipate customer needs, personalize interactions, and deliver consistent, high-quality service across channels, all while reducing operational effort. By combining the precision of AI agents with the intelligence of contextual insights, Freshdesk enables businesses to scale support without sacrificing the human touch.

See how Freddy AI can transform your customer service. Book a personalized demo today and experience AI-powered support in action.