Agentic AI vs. Generative AI: What’s the Difference?

What’s the Difference Between Generative AI vs. Agentic AI?

The main difference between generative AI and agentic AI is that generative AI creates content like text, images, or music based on input prompts, while agentic AI focuses on taking actions toward goals autonomously, making decisions without needing constant user direction.

Generative AI isn’t goal-driven, apart from striving to produce a response that aligns with the input prompt. Agentic AI, on the other hand, is designed to pursue explicit goals, make decisions, and take actions toward completing a defined task or outcome. It can also adapt its goals autonomously, depending on changing conditions. This makes generative AI more reactive, and agentic AI more proactive.

We summarized the main differences between generative and agentic AI in this table:

An Example of Generative AI

If you’re writing an email and need a catchy subject line, a generative AI tool, like ChatGPT, will serve you well. In that case, you could type in a short prompt like “Write a catchy subject line for a product launch email” and you instantly get a prompt-based response.

ChatGPT perfectly demonstrates how generative AI (traditional AI) works. It responds to your input by producing original content based on patterns it knows from vast databases.

While it’s fast, creative, and useful, it won’t execute any further actions for you. For example, it won't add this copy to the subject line of your email or send it for you. That’s where agentic AI comes in.

An Example of Agentic AI

If your underwriting team is swamped with incoming loan applications, AI Agents like Decision AI can help.

You can integrate Decision AI into your existing systems to provide it the context and capabilities needed to perform various actions. This can include evaluating applications, applying rules, checking for inconsistencies, and recommending next steps.

Decision AI is an example of agentic AI. It can make decisions on your behalf (for example, approve loan applications) based on your company’s internal guidelines and existing data, and continuously learns and improve outcomes. It also doesn’t wait for users' prompts. It already knows what to do, how to adapt, and how to move your workflows forward.

What Thought Leaders Say About Generative AI and Agentic AI

The AI market is evolving fast, and it can be hard to keep pace. Understanding how different types of AI actually work—and where they add value—isn’t always easy. To help you navigate this fast-moving landscape, we extracted key insights from our podcast on AI, the market, and the real differences between Agentic AI vs. Generative AI.

Here’s what business leaders, innovators, and technologists had to say.

Wayne Butterfield, ISG

“AI has become a bit of an umbrella buzzword that encompasses a lot of technologies.” — Wayne Butterfield

Wayne highlighted a key issue where the term “AI” is often used so broadly that it loses meaning. Also, in his episode, Wayne highlighted that many leaders still combine robotic process automation (RPA), basic automation scripts, and true AI technologies under the same label.

His advice is to understand the type of AI you’re working with and what it’s capable of so you can choose the right solution for your goals.

Mario DiCaro, Tokio Marine HCC Insurance Holdings

“The real impact of AI so far? Documentation. It helps us write faster, clearer, and with fewer errors.” — Mario DiCaro

Mario DiCaro pointed out that content creation is still the number one use case for generative AI. It’s great for speeding up tasks related to documentation, emails, and reports. But now with Agentic AI systems that can not only write but also act, decide, and follow up, we’re entering a new phase.

While documentation was just the beginning, the new wave of impact is mostly felt in decision-making, operations, and customer interactions.

Ankur Patel, Multimodal

“Generative AI becomes yet another toolkit, enabling new features and possibilities.” — Ankur Patel

Our founder reminds us that generative AI is not the endgame. It’s a powerful tool, but one piece of a larger system.

When embedded into workflows or used to power autonomous AI Agents, generative AI becomes far more valuable.

For example, instead of just generating a customer response, generative AI can be used within an agentic AI system that drafts, sends, and follows up, all while learning and improving from results.

Remington Rawlings, OneView

“For too long, we’ve equated ‘busy’ with ‘productive’. Generative AI challenges this notion. It’s not about how many hours you clock but the depth and impact of the work you do.” — Remington Rawlings

Remington’s insight gets at the cultural shift AI is driving. Generative AI helps reduce time spent on repetitive tasks, but productivity gains come when it’s part of a system that delivers results, not just drafts.

That’s where agentic AI comes in, as it helps shift the focus from tasks to mission execution, freeing up teams to focus on higher-value, strategic work.

Shivaji Dasgupta, Tad. Ventures

“We should not discount that there is not, at least still, no generative AI out of the box that we can just put in and use.” — Shivaji Dasgupta

Shivaji made an important point that, while generative AI may look like plug-and-play, enterprise use requires thoughtful configuration. Fine-tuning, aligning outputs with company objectives, ensuring compliance, all of which takes time and expertise.

This is especially true when integrating generative AI models into agentic AI systems that need to make decisions and act autonomously.

Suzanne Rabicoff, Multimodal

“The underwriter is trying to do more with less. The Agent becomes a digital teammate.” — Suzanne Rabicoff

“The Agent can structure, draft, and follow up. It’s not just task completion.” — Suzanne Rabicoff

Suzanne captured the essence of Agentic AI. It’s not a tool you have to micromanage, but a digital teammate that can manage full workflows on its own (or, preferably, with human oversigh).

If you think about underwriting, instead of human reviewing every document, cross-checking values, and drafting decisions, an AI Agent can do all of that and even initiate next steps.

It goes beyond executing tasks as it carries out missions according to goals, just like a capable, human team member.

Use Cases for Agentic AI

Decision Automation in Financial Services

AI Agents and agentic systems like Decision AI can autonomously evaluate and process data from multiple sources, assess risks, and make key business decisions without constant human supervision.

Instead of just generating reports, agentic AI also triggers actions, escalates issues, and closes loops.

Enterprise Document Automation

From reading unstructured documents to making sense of forms, contracts, and PDFs, agentic AI automates the entire lifecycle, from real time data classification and extraction to validation and routing.

Claims Processing in Insurance

With access to policy documents, client data, and claim forms, an AI Agent can validate claims, detect anomalies, and auto-approve or flag claims for human review–all while maintaining speed, efficiency, and scalability.

Compliance and Monitoring

Unlike generative AI, agentic AI can monitor transactions, internal communications, and documents in real time to detect compliance violations, taking preemptive action when needed and not just flagging issues after the fact.

Use Cases for Generative AI

Faster Documentation and Reporting

From policy summaries to meeting notes, generative AI tools can auto-draft clear, structured documents, saving teams hours of manual writing and editing.

For example, a claim adjuster can dictate findings in shorthand, and generative AI can generate a report formatted to the company’s standards.

Internal Knowledge Base Creation

Generative AI can convert raw notes, transcripts, and structured data into readable, searchable internal resources. This helps ensure teams have training data & guides, process documentation, SOPs, and FAQs ready at any time.

It’s also very useful in regulated industries like insurance and finance, where accurate knowledge sharing is critical.

Risk Assessment Narratives

Underwriters and risk analysts often need to write narrative justifications for approvals or denials. Generative AI can produce these based on structured inputs, making the process faster, more consistent, and audit-ready.

Marketing Content Generation

Generative AI uses machine learning to quickly produce ad copy, blog drafts, product descriptions, and social media content ideas. It can help marketers scale output while maintaining brand voice and tone.

How Generative AI and Agentic AI Can Work Together

Generative AI excels at creating content, but it cannot take initiative or understand contextually relevant content. That’s where agentic AI steps in.

Agentic systems are designed to pursue goals autonomously, meaning they can make decisions on what to do, when to do it, and how to adapt based on outcomes. When paired with generative AI, they not only produce content but also determine what happens next.

This makes them a powerful duo in industries like insurance and finance, where both communication and decision-making are critical.

Here’s how they can work together in a real-world insurance claims processing scenario:

1. Claim received - An AI Agent reviews the submitted claim form and provided documents.
2. Initial assessment - The AI Agent validates completeness and extracts key information.
3. Generative AI creates a summary report - Once validated, a generative model creates a claim summary report or adjuster note based on the extracted information.
4. Agentic AI makes a decision - The AI Agent reviews the summary, applies business logic or thresholds, and determines whether to auto-approve, escalate, or request more info.
5. Generative AI drafts response - If approved, generative AI drafts a personalized approval response and breakdown of the settlement. If denied, it creates a clear explanation with next steps.
6. Agentic AI takes action - The AI Agent sends the response, updates the claim status in the system, and notifies the relevant team, all without human intervention.

What’s Next After Agentic AI?

Agentic AI is a major leap from static tools to goal-oriented autonomous systems that can think and act.

However, that’s not the end of the road, it’s a foundation for what’s coming next, and it’s already happening.

The future points toward collaborative intelligence, where AI Agents not only work independently but also collaborate with each other and humans in dynamic environments.

These systems will negotiate priorities, coordinate tasks across departments and systems, and adapt continuously based on new data or shifting goals.

We’re also seeing early signs of multi-agent ecosystems, where specialized AI Agents handle different parts of complex workflows by communicating, handing off tasks, and learning from shared outcomes.

This opens the door to intelligent AI Agent orchestration at scale that requires minimal human oversight.

Another emerging frontier is self-improving AI Agents that can audit their own performance, learn from failures, and optimize strategies over time, without needing constant retraining from humans.

Therefore, we believe that Agentic AI isn’t the final destination but a stepping stone to truly autonomous, collaborative, and adaptive systems, so businesses that start experimenting now will be far better positioned to lead in the future.

That’s exactly what we’re working on by providing an agentic AI platform, AgentFlow, which allows businesses to make, orchestrate, and manage AI Agents in one platform.

Who Offers Agentic AI?

We do! We specialize in vertical agentic AI solutions designed specifically for insurance and banking. These industries are highly regulated, which means that AI Agents must be not only effective but also accurate, compliant, and auditable. All our AI Agents and our agentic AI platform, AgentFlow, are created with these criteria in mind.

Our AI Agents are built to operate independently within the real-world constraints of financial services, automate complex tasks, and work alongside your existing systems securely and intelligently. AgentFlow lets you configure them for your specific needs and deploy them in just 90 days, making agentic workflows easier to implement than ever.

Whether it’s underwriting, claims processing, compliance workflows, or customer operations, our agentic AI solutions can help your team do more with less friction and better outcomes.

To learn how our solutions can help your business, please book a demo with our experts. We'll discuss your needs, show you how our agentic AI works live, and explore how it can integrate into your business.

FAQs

Is ChatGPT Agentic or Generative AI?

ChatGPT is generative AI because it creates text based on patterns in data. It is not agentic AI since it cannot make autonomous decisions or pursue goals on its own.

Is Copilot Agentic or Generative AI?

Copilot is generative AI because it produces code suggestions from user input. It is not agentic AI since it does not act independently or pursue goals without human prompts.

Is Agentic AI the Next Big Thing?

Agentic AI is considered the next big thing by many experts. For example, Y Combinator predicts vertical AI Agents could be 10X bigger than SaaS, highlighting their potential to automate decisions, manage workflows, and operate with minimal human input.