Agentic AI vs Generative AI: Which One Powers Automation?

Generative AI creates content based on patterns, while agentic AI autonomously executes tasks, handles multi-step workflows, and makes decisions. The blog highlights their differences, emphasizing how agentic AI, which often incorporates generative AI, transforms business operations by enabling conversational, self-service data analytics. It explains use cases, capabilities, and the value each AI type offers to businesses.

Generative AI produces content. Agentic AI completes tasks. Generative models create text, images, and summaries on demand, while agentic systems plan, decide, and act across tools to achieve specific outcomes. 

They’re connected because most agentic systems use generative models as a reasoning layer, but only agentic systems can execute tasks autonomously.

As AI technologies evolve, this distinction is reshaping how businesses use data and automation.

With 78% of global companies adopting AI tools in at least one function by 2025, it’s essential to understand how generative AI and agentic AI differ, and how each can drive value for your organization. 

This post explains the real differences between these technologies, why they matter, and how to decide which one fits your needs. 

We’ll explore their unique capabilities, dive into specific use cases, and look at how modern AI systems like askEdgi by OvalEdge bring these ideas to life in enterprise settings, enabling conversational access to governed data and automated insights.

Agentic AI vs generative AI: How do they work?

Agentic AI vs generative AI refers to two distinct approaches in artificial intelligence. Agentic AI is designed to execute predefined tasks autonomously, often optimizing processes or making real-time decisions. It operates with clear objectives and interacts with its environment to solve specific problems. 

In contrast, generative AI focuses on creativity, generating new content based on learned patterns. It excels in tasks like content creation, from text to images, by mimicking human-like creativity. 

While agentic AI is used for automation and decision-making, it often incorporates generative AI as a component, such as a human-like response generator, within its broader autonomous workflows. 

In this sense, agentic AI can be seen as an evolution or extension of generative AI, integrating its creative capabilities into more complex, goal-oriented systems that execute multi-step processes and make real-time decisions. Both play critical roles in advancing AI technologies, with agentic AI taking a more comprehensive, autonomous approach.

What is generative AI? Core concepts

Generative AI is built on advanced machine learning models, such as Large Language Models (LLMs), diffusion models, and multimodal systems. These systems generate text, images, audio, and more, all from a user prompt. The core idea is that these models are trained on vast datasets to recognize patterns, make predictions, and output content based on statistical likelihoods.

Generative AI operates in a reactive pattern, meaning it waits for a user to provide a prompt, then generates an output. This output could be anything from a paragraph of text to an image or code snippet, depending on the model's design.

For example, when you ask a generative model to write an article or summarize a document, the system draws from learned patterns and contextual embeddings to create meaningful text. It doesn't make decisions or plan ahead, but simply reacts to what you ask for.

What is agentic AI? Key characteristics

Agentic AI represents a step forward from traditional generative systems. These AI systems don't just generate content; they plan, execute, and act autonomously to achieve specific goals. Unlike generative AI, which is reactive, agentic AI is proactive. It can handle multi-step workflows, make decisions in real-time, and interact with various tools and systems to complete tasks.

According to McKinsey’s State of AI report, 62% of organizations have experimented with agentic AI systems, and only 23% have scaled them to production, highlighting the early stages of adoption in many industries.

Key features of Agentic AI include:

• Decision-making autonomy: Agentic AI systems can assess situations, decide on actions, and execute them without continuous user input.

• Tool and API integrations: These systems can integrate with existing tools and services, like CRMs or data sources, to complete tasks automatically.

• Multi-step workflow execution: From gathering data to analyzing it, making decisions, and taking actions, agentic AI handles complex processes.

• Memory tracking and reflection: Some agentic AI systems use stateful memory to track actions across sessions, enabling adaptive behavior.

In a typical agentic AI workflow, the AI might gather data, analyze it, decide what action to take, and execute that action, whether it’s sending an email, generating a report, or updating a database. Unlike generative AI, which is limited to creating outputs, agentic AI systems can complete entire tasks autonomously.

Comparison table: Agentic AI vs generative AI

Comparing agentic AI and generative AI is essential to choosing the right solution for your business needs. The table below provides a quick side-by-side comparison of their core functionalities to help you navigate which approach is best for your business.

Key differences between agentic AI and generative AI

While both agentic AI and generative AI are groundbreaking technologies, they serve very different purposes and excel in distinct areas. Understanding these differences is key to leveraging each tool effectively in your business.

1. Purpose and primary function

The primary difference between generative AI and agentic AI lies in their purpose. Generative AI is focused on creating content based on patterns it has learned from large datasets. It can generate text, images, and even videos, but it does not make decisions or take actions on its own.

Agentic AI, on the other hand, is designed to autonomously execute tasks. It can plan, make decisions, and complete multi-step processes like scheduling, reporting, and interacting with external systems.

2. Autonomy and decision-making

Generative AI lacks autonomy and only produces output in response to specific user prompts. It doesn’t have the ability to make decisions or determine the next steps in a task.

An academic study published on arXiv shows that generative AI can increase retail productivity by up to 16.3%, while agentic AI is projected to enhance productivity through automation and multi-step workflows. 

It can break down complex tasks into smaller actions, decide what needs to be done next, and then carry out those actions. It’s like a digital assistant that not only understands the task but can also take steps to complete it on its own.

3. Workflow and task execution

Generative AI operates on a one-time prompt-response basis. Once you give it a prompt, it generates a response. There’s no continuity between requests. 

In contrast, agentic AI handles multi-step workflows. For instance, if you need to research a topic, generate a report, and schedule a meeting, agentic AI can automate the entire process. It retrieves data, analyzes it, decides on the next steps, and takes actions, essentially handling the full workflow autonomously.

4. Context awareness and memory

Generative AI typically operates with session-bound context, meaning it processes the current prompt to generate an output without retaining memory of previous interactions. 

Agentic AI, however, often relies on persistent memory to track past actions, allowing it to adjust its behavior over time. 

While traditional agentic AI systems use memory to handle complex tasks and adapt to new inputs, askEdgi operates differently. It focuses on providing real-time insights through conversational interactions rather than using a long-term memory structure. 

5. When generative AI and agentic AI overlap

Generative AI and Agentic AI can complement each other in certain systems. While agentic AI is not a "better" version of generative AI, it does expand its capabilities.

For instance, askEdgi leverages generative AI to process natural language queries and provide insights, but it does not autonomously execute tasks like generating reports or updating systems. Instead, it enables data-driven conversations to help users derive insights without manual query-building.

Why agentic AI matters? Use cases and applications

Agentic AI is already making a significant impact in various industries by automating tasks, improving decision-making, and streamlining workflows. Let’s take a closer look at real-world use cases of agentic AI and explore how it’s transforming business operations across different domains.

Business workflow automation

One of the most valuable applications of agentic AI is its ability to automate business workflows, saving time and improving operational efficiency. Here are a few key examples:

• Customer support follow-ups: Automatically sending personalized follow-up emails or notifications after customer interactions, ensuring no query goes unanswered.

• Report generation: Seamlessly pulling data from multiple systems, analyzing it, and generating comprehensive reports with minimal human intervention.

• Email sequencing: Automating the process of sending personalized emails to customers based on specific criteria, enhancing engagement and improving customer relationships.

• CRM updates: Keeping customer records up-to-date automatically, so your team can focus on high-value tasks instead of routine data entry.

These applications showcase how agentic AI can streamline repetitive tasks, improve accuracy, and free up time for more strategic work.

Complex task orchestration

Where agentic AI truly shines is in managing complex, multi-step processes that require thoughtful decision-making and orchestration. Here’s how it can enhance efficiency:

• Sales research: Identifying potential prospects, reaching out through email or other channels, and logging results, all handled autonomously.

• Financial analysis: Analyzing large volumes of financial data, updating spreadsheets, and summarizing findings, ensuring your team can focus on interpreting insights rather than gathering data.

• IT operations: Diagnosing and resolving technical issues quickly, reducing downtime, and ensuring smooth business operations with minimal human involvement.

These use cases highlight how agentic AI goes beyond simple automation to handle sophisticated workflows that demand context, decision-making, and continuous management.

Emerging and advanced domains

The capabilities of agentic AI extend into more specialized and cutting-edge domains, where its autonomy and precision can make a significant impact:

• Robotics: Enabling robots to perform a range of tasks autonomously, from manufacturing to delivery, improving operational efficiency in industries like logistics and healthcare.

• Autonomous research assistants: Automating the process of gathering, analyzing, and synthesizing research materials, allowing researchers to focus on deeper insights and innovation.

• Multi-agent systems: Coordinating multiple agents to solve complex simulations or handle decision-making processes that require input from diverse data sources, such as supply chain management or crisis response scenarios.

These emerging applications are pushing the boundaries of what AI can do, unlocking new efficiencies and capabilities in both traditional and advanced industries.

Data-driven support

Data-driven support is one of the most impactful applications of agentic AI, enabling businesses to unlock valuable insights quickly and make smarter, faster decisions. 

For instance, askEdgi takes this a step further by offering a conversational, self-service platform built on governed data, empowering users to interact with data directly without needing technical expertise.

Key benefits of using askEdgi for data-driven support include:

• Instant access to insights: It enables users to query data conversationally and receive real-time, actionable insights without needing to build complex queries or rely on static dashboards.

• Simplified data analysis: It processes unstructured data from various sources and compiles it into easy-to-understand summaries, recommendations, and trends.

• Self-service for non-technical users: No need for specialized technical expertise; askEdgi is a self-service platform that empowers business users to explore data and make decisions on their own.

• Governed and secure data: As a platform built on governed data, it ensures that all insights are drawn from secure and compliant sources, making it reliable for decision-making in regulated industries.

By automating the data analysis process and providing actionable intelligence in real time, Agentic AI tools eliminate the delays and complexities associated with traditional analytics methods. 

How to choose the right agentic AI tool

Selecting the right agentic AI tool is crucial to ensuring that it meets your specific business needs and delivers the desired outcomes. With the variety of AI solutions available, choosing the right tool can significantly impact your operational efficiency and the scalability of your business processes.

When selecting an agentic AI tool, consider the following:

1. Define your goal: Are you looking to automate workflows, generate content, or tackle complex tasks? Clearly outlining your objective will help you choose a tool that aligns with your primary needs and expected outcomes.

2. Evaluate complexity: If your tasks involve branching logic or require integration with multiple systems, agentic AI is ideal. Complex workflows demand a solution capable of adapting and automating decision-making across various stages.

3. Check integration requirements: Make sure the AI tool integrates seamlessly with your existing tools and systems. Effective integration is key to enabling smooth operations and reducing the friction of adopting new technology.

4. Assess oversight needs: Consider how much autonomy your agentic AI tool should have. If your tasks require human intervention at critical stages, ensure the system allows for real-time oversight and approval.

5. Measure scalability: Choose a solution that can evolve as your business grows. A scalable tool will accommodate future expansions, additional tasks, or more complex workflows without a complete system overhaul.

6. Review governance and safety: Ensure the tool meets compliance requirements and follows best practices for data governance. Security and regulatory compliance are critical, especially when working with sensitive or enterprise-grade data.

Choosing the right agentic AI tool can drive significant improvements in your business operations. askEdgi by OvalEdge, for example, offers a user-friendly, governed, and scalable solution for businesses looking to empower users to make data-driven decisions without the need for complex technical setups.

Conclusion

In summary, generative AI is an incredible tool for creating content, while agentic AI shines when it comes to executing tasks, making decisions, and achieving goals autonomously. While each technology has its own strengths, they can work together to provide unique advantages depending on the specific needs of your business. 

As more companies adopt agentic AI solutions, those who strategically implement these systems will unlock new levels of efficiency, decision-making, and growth.

askEdgi isn’t just another analytics tool; it’s a conversational platform that empowers users to interact with data in a more intuitive, self-service way, making complex decision support accessible to all team members, without the need for technical expertise. 

Ready to take your data operations to the next level?

 Schedule a demo with OvalEdge today and discover how it can streamline your workflows and help your team unlock actionable insights with ease.

FAQs

1. What is the main difference between agentic AI and generative AI?

Agentic AI focuses on executing tasks and making decisions autonomously, while generative AI is designed for creating content, such as text or images, based on user inputs.

2. How does agentic AI enhance business automation?

Agentic AI automates multi-step workflows, integrates with existing systems, and makes real-time decisions, significantly improving efficiency in tasks like report generation and customer support follow-ups.

3. What industries can benefit from agentic AI?

Industries such as healthcare, finance, manufacturing, and customer service can benefit from agentic AI by automating complex processes, improving decision-making, and enhancing operational efficiency.

4. Can generative AI be used for decision-making tasks?

Generative AI is primarily focused on content creation and lacks decision-making capabilities. For decision-making tasks, agentic AI is the better choice, as it can autonomously execute tasks and manage workflows.

5. How does askEdgi by OvalEdge help with data analysis?

askEdgi uses conversational AI to allow users to interact with data in plain language. It simplifies data analysis by providing real-time, actionable insights, enabling business users to make informed decisions without technical expertise.

6. What are the key benefits of using askEdgi for businesses?

askEdgi empowers businesses with self-service data analysis, provides actionable insights, and ensures data governance and compliance, all while simplifying decision-making with an intuitive conversational interface.