The term Agentic AI is receiving a lot of attention these days. Gartner listed Agentic AI as a technology trend topic for 2025, and Microsoft introduced agentic capabilities in its Copilot Studio. 

But what exactly is Agentic AI, and how does it stack up against the popular AI Copilots?

While both utilize Generative AI to simplify tasks and enhance productivity, their core capabilities, functionalities, and applications diverge significantly. For enterprises looking to capitalize on these advancements, it is imperative to understand the differences between AI Copilots and Agentic AI.

In this AI Copilots vs. Agentic AI article, we’ve compared their capabilities, characteristics, key differences, and use cases and provided real-world examples to help you identify which approach aligns best with your organization’s needs.

Before we dive into the comparison, let’s start with the most fundamental part: the definition of AI Copilots and Agentic AI.

An AI Copilot is an AI-powered assistant that works alongside your employees to improve productivity and efficiency. It reduces your employees' workload by providing the right information, contextual support, and additional functionalities by fetching data from enterprise-grade systems in real-time.

Suppose a sales manager wants to compare five years of sales data. An AI Copilot analyzes the data in real-time, identifies trends, highlights key insights, and generates visualizations. This helps the manager make informed decisions about sales strategies. 

Agentic AI refers to AI systems that act autonomously to achieve complex goals and workflows with limited direct human intervention. These systems understand context and instructions in natural language, set appropriate goals, reason through subtasks, and adapt decisions and actions based on changing conditions. 

For example, Agentic AI can detect payroll discrepancies and send alerts to HR teams ahead of time to avoid errors. It also continuously learns and tracks news to update any changes in tax laws and policies, ensuring that payrolls are processed with compliance and accuracy. 

AI copilots excel at assisting and augmenting, while Agentic AI shines at autonomous task execution and decision-making. Let’s take a deeper look at their capabilities to understand the differences: 

AI Copilot

Think of AI Copilots as personal assistants to your employees who help with their day-to-day tasks and accelerate their work efficiency. AI Copilots process vast data sets and understand user intent to: 

• Generate content at scale. Whether an employee writes an email or subject matter experts create technical documentation, AI Copilots assist them by generating information. 

• Provide actionable insights from complex data. AI Copilots can extract data from your organizational data, such as annual sales reports, policy documents, or financial reports, and present information in an easy-to-read format. Their analytical capabilities go beyond human capabilities, helping employees unlock key insights for better decision-making. 

• Retrieve accurate information. AI Copilot makes API calls to enterprise systems to retrieve information and answer queries regarding healthcare benefits, leave balances, printer connectivity, etc.  

The goal of AI Copilots is to maximize operational efficiency and employee productivity by performing tasks within the bounds of human prompts. They are not designed to exceed human oversight.  

Agentic AI 

Agentic AI systems build on AI’s foundational abilities with a higher level of autonomy and operate independently with minimal human intervention. Their capabilities extend to:

• Planning and execution. Agentic AI systems orchestrate and execute multi-step workflows to provide end-to-end solutions to employees. 

• Complex problem-solving. Agentic AI can help diagnose the root causes of IT issues and devise workforce planning strategies by analyzing existing data and applying its reasoning frameworks. For example, it can recommend the best talent mix for an upcoming project or advise restructuring teams to fill skill gaps without waiting for manual input from HR managers.  

• Real-time adaptability. Agnetic AI can perceive its environment and adapt its decisions and learnings based on changing conditions. It can adjust workflows to determine the best action and remember it for the future. 

With Agentic AI, companies can scale their operations, save cost on resources, and reduce workload on employees.

Besides their capabilities, AI Copilots and Agentic AI clearly distinguish their characteristics. We’ve discussed the key characteristics of each below: 

AI Copilot

Assistive role

The AI Copilot augments human efforts and offers suggestions to achieve a goal, but the final choice of implementation would be under human control. For marketers, AI Copilot can assist in drafting taglines or planning marketing activities, but the ultimate decision of action depends on the marketer. 

Context awareness

For queries like “How do I connect to the wifi?” or “Is my family eligible for healthcare benefits?” Copilots understand the intent and provide personalized answers. Plus, they analyze past interactions, user behavior, and chat history to refine their answers. 

Task-specific automation

In terms of workflow automation, AI copilots operate within the parameters of pre-defined workflows and automate only specified tasks. If the company encounters an outage, AI copilots will alert IT leaders, offer potential fixes based on past incidents, and assist in retrieving relevant data. They will not take any action to fix the issue. The human agents will examine the suggestions, verify the data, and implement a fix.

Agentic AI 

Goal-oriented behavior 

Agentic AI systems are inherently goal-oriented and work continuously to achieve them. They plan strategies, allocate resources, determine the best course of action, and adapt to the changing environment to reach these goals.

Social intelligence 

Agentic AI systems are designed to seamlessly integrate with external tools and understand user intent. Based on the goals, they recognize the right tools to use and prompt communication with other platforms to execute actions without human intervention. This helps them navigate complex environments.

Scalability 

Agentic AI systems are highly scalable as they can autonomously execute operations across multiple dimensions without compromising performance. As your organization grows, Agnetic AI can dynamically adjust strategies to handle growing data, optimally distribute tasks and resources, and smoothly integrate with new tools.

So far, we’ve looked at the differences in the capabilities and characteristics of AI Copilots and Agentic AI. In this section, we break down the key use cases of each for businesses to understand how they can be applied:

AI Copilot use cases

Code generation

You can integrate the AI Copilot into your coding system to assist developers in generating code snippets, suggesting fixes, or improving the existing code base. 

With this, your dev team can review and implement code changes faster, reduce errors, and focus on solving complex problems.  

Sales enablement

AI Copilots offer real-time assistance to sales teams to optimize outreach strategies. Sales teams can use AI copilots to analyze their prospects' profiles, generate personalized outreach emails, and create sales call scripts or craft personalized pitch decks. They take the grunt of manually researching hundreds of prospects and help sales teams focus on building meaningful relationships with prospects. 

Marketing content

AI Copilots can analyze your marketing data, past campaigns, user intent, and industry trends to suggest engaging campaigns, social media posts, blog posts, and videos. They also offer real-time editing assistance to check grammar, punctuation, syntax, and more. This helps your marketing team create engaging and error-free content at scale. 

IT support

AI Copilots can assist in various IT support functions, such as generating new support documents and FAQ pages and helping employees and IT teams resolve issues by pulling relevant information from the knowledge base. 

For example, if an employee has issues connecting with the office printer, AI Copilots can provide step-by-step instructions or a link to a video tutorial. 

Agentic AI use cases

IT support

At an enterprise level, IT support teams receive nearly hundreds, if not thousands, of support tickets every day. Agentic AI helps organize and prioritize tickets based on urgency. It automatically resolves repetitive tasks like password resets, installs software updates, and escalates complex issues to the right IT support agent. 

Agentic AI monitors the organization's IT environment to detect anomalies. For example, if a server crashes, Agentic AI systems detect the issue, analyze logs to find the root cause, and autonomously implement actions to restore affected systems.  

HR support

Agentic AI offers end-to-end automation for repetitive but complex HR processes, such as onboarding new employees, managing leave requests, updating employee records, and processing payroll. 

Let’s say you want to onboard 10 new hires on the same day. Agentic AI collects necessary documents from the new employees, creates user accounts, logs their details into the HRSM, provides access to company systems, adds each employee to company communication channels, and schedules 1:1 orientation with their respective managers. All this without any human intervention. 

Customer support

Companies can incorporate Agentic AI into customer support functions to handle end-to-end customer queries and provide exceptional experience. For example, suppose a customer reports a delay in the delivery of their package. In that case, the AI can identify the root cause in the logistics system, reroute the package, and update the customer with a new estimated delivery time. 

Fraud detection

Banks deal with numerous hefty transactions daily. For them, enabling secure transactions is critical. Agentic AI continuously monitors transactions and account activity patterns to flag malicious activities. If any such activity is detected, Agentic AI systems take immediate actions like freezing accounts and escalating issues to the officials.

While both Agentic AI and AI Copilots can generate content and automate tasks, their underlying capabilities and approaches differ significantly.

So we’ve tried to simplify the explanation and highlight the key differences that will help you evaluate while choosing solutions:

Autonomy level

AI Copilot works under direct human control and assists employees with each task. 

In contrast, Agentic AI systems operate with a high level of autonomy and little human intervention. 

Goal-orientation

AI Copilots have a goal-supportive approach. Their primary purpose is to enhance human capabilities rather than pursue goals. 

Agentic AI embodies a goal-driven approach. It takes full ownership of the goals and proactively pursues them through strategic planning and execution. 

Decision-making capabilities

AI Copilots facilitate decision-making with their capabilities but do not take any independent action. 

On the other hand, Agentic AI systems are equipped with advanced decision-making frameworks that enable them to evaluate multiple options, determine the best course of action, and execute the tasks on their own. 

Reasoning capabilities

AI Copilots have basic to moderate reasoning capabilities. Their primary focus is to understand the context and provide relevant suggestions. If you want Copilots to think through a problem step by step or handle a complex task, you need to craft specific prompts guiding them every single time.

Agentic AI comes with a native cognitive architecture, meaning they’re designed with a built-in logic and reasoning framework that enables them to plan, think, and act independently to execute multi-step processes.

Learning and adaptability

AI Copilots learn from user interactions and feedback to refine their suggestions over time. Their adaptability is largely user-driven, improving their performance based on direct input and usage patterns.

Agentic AI systems exhibit self-directed learning and adaptability, continuously optimizing their strategies based on performance outcomes and environmental changes. They can reconfigure their operations autonomously to enhance efficiency and effectiveness. 

Interaction with environment

AI Copilots are confined to enhancing user interactions within specific applications. 

Agentic AI systems interact extensively with various external tools and systems. They can orchestrate actions across multiple platforms, integrating with APIs, databases, and other software to perform comprehensive tasks.

Many businesses have adopted AI Copilots, and now many others are making a move towards integrating Agentic AI into their workflows. But how do these organizations use these technologies? We’ve explained this with real-world examples for each. 

AI Copilot

Accenture, one of the leading professional services organizations, integrated GitHub Copilot at scale, transforming the work of its 12,000 developers. Initially tested with 20 developers, the pilot’s success led to a randomized controlled trial involving 450 developers using Copilot, compared to 200 without it.

The results were clear: Developers produced higher-quality code faster, generated more pull requests, and completed builds with fewer errors.

Accenture uses AI Copilot to automate routine tasks like writing unit tests and explaining legacy code, navigating unfamiliar codebases confidently, and accelerating internal collaboration through code sharing. This enables developers to address technical debt efficiently, reduce onboarding time, and boost productivity.

Agentic AI

Bud Financial, a leader in banking technology, implemented Agentic AI on the consumer side of its business to enhance customer financial well-being through proactive, autonomous money management.

Bud Financial’s Agnetic AI solution can analyze each customer’s financial history, current position, and goals to perform tasks that improve their financial health. 

For example, it can automatically transfer funds between accounts to prevent overdraft fees or capitalize on higher interest rates in savings accounts. This reduces the need for customers to micromanage their finances and incorporates smarter money management. For Bud Financial, it lowers operational costs while building trust and loyalty among its customers.

The choice between AI Copilots and Agentic AI depends on your business objectives, the complexity of your workflows, and the desired level of automation. 

AI Copilots act as intelligent assistants, ideal for enterprises that value collaboration and creativity while requiring precision and control. For example, AI Copilots can greatly benefit marketing teams in brainstorming ideas, employees working on R&D projects, or improving customer experiences. 

Agentic AI, on the other hand, is better suited for enterprises that aim to scale operations and automate complex processes in supply chain management, IT services, or manufacturing. Its ability to independently plan, decide, and learn allows it to handle dynamic environments efficiently and drive large-scale operational improvements. 

The success of either approach depends on how well the AI model aligns with the enterprise’s specific needs and risk tolerance. 

Dom Couldwell, head of field engineering EMEA at DataStax, explains: “Agentic AI is better suited for more complex processes that require support across multiple tasks to achieve that goal. The reason for this is that LLMs and generative AI applications can be trained to support copilot use cases by leveraging data for specific tasks using Retrieval Augmented Generation, or RAG, but they may not be able to switch and perform as well on multiple different tasks even where you have multiple RAG approaches and options in place.”

Note: RAG combines large language models with external knowledge retrieval systems, ensuring the AI provides accurate, context-aware insights from relevant data sources.

He further advises “...use a copilot to assist a person where the task is specific. Use Agentic AI, where the goal is more complex and can involve multiple tasks being completed. Use RAG in both to provide the context supplied to the LLM.”

Workativ stands out as a comprehensive solution that offers a balance between human-AI collaboration. Our platform offers tools to build AI virtual assistants and automate workflows to improve IT, HR, and employee support. 

For complex tasks, Workativ enables you to leverage Agentic AI to automate processes such as employee onboarding and the end-to-end ticket resolution of repetitive Level 1 (L1) support queries, including password resets and printer connectivity issues. These autonomous systems manage multi-step workflows with minimal human intervention, ensuring consistency, reducing errors, and freeing up employees for more strategic initiatives.

Workativ enables the creation of AI Copilots that empower your employees to lead strategic initiatives by delivering real-time insights, comprehensive analyses, and actionable suggestions.

If you want to embrace Agentic AI and elevate your enterprise operations, Workativ can help. Book a demo now.

What is the difference between Generative AI and Agentic AI?

Generative AI excels at creating content from scratch, like text, images, or videos, based on the prompts provided by a human. Agentic AI goes further by autonomously making decisions, planning actions, and achieving goals. 

Generative AI requires human guidance, whereas Agentic AI operates independently to complete complex, multi-step workflows.

What is Agentic AI?

Agentic AI refers to AI systems that act autonomously to achieve complex goals and workflows with limited direct human intervention. These systems understand context and instructions in natural language, set appropriate goals, reason through subtasks, and adapt decisions and actions based on changing conditions. 

Is Agentic AI the future?

Agentic AI represents the next wave of AI innovation. Gartner predicts that by 2028, organizations will employ Agentic AI to make at least 15% of day-to-day work decisions autonomously.

Its ability will significantly contribute to organizations looking to scale operational efficiency. 

How big is the Agentic AI market?

The agentic AI market is set to grow at a rapid pace. Research shows that the market size for Agentic AI was valued at $30.89 billion in 2024 and is expected to reach $47.1 billion by 2030. 

What are the use cases of Agentic AI?

Use cases of Agentic AI include:

• Managing end-to-end IT support, such as AI-driven ticketing, root cause diagnosis, and automated resolutions

• Streamlining HR operations, including employee onboarding and offboarding

• Enhancing financial services by detecting and preventing fraud through real-time transaction analysis.