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AI is full of buzzwords, but not all of them have the same meaning.
Generative AI and Agentic AI are often used interchangeably, but they are two different things. One creates content and improves productivity, and the other takes action.
Confusing the two can lead to mismatched expectations, wasted resources, and missed opportunities. Below, we’ll show you the key differences between Agentic AI vs. Generative AI and show you examples so you know which AI fits your business the best.
The main difference between generative AI and agentic AI is that generative AI creates content like text, images, or music based on input prompts, and improves overall productivity by supporting research for example, while agentic AI focuses on taking actions toward goals autonomously, making decisions without needing constant user direction.
If you’re writing an email and need a catchy subject line, you would open an AI tool like ChatGPT and type in a short prompt such as: “Write a catchy subject line for a product launch email” and you would instantly get a list of creative options.
That’s 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 send the email for you. That’s where Agentic AI comes in.
If your underwriting team is swamped with incoming loan applications, instead of reviewing each file manually, you can deploy Decision AI.
Once this AI Agent is integrated into your system, it doesn’t just summarize documents, but it also evaluates applications, applies rules, checks for inconsistencies, and recommends next steps.
Decision AI is an Agentic AI Agent, it can make decisions on your behalf (such as approve loan applications), based on your company’s logic and existing data, and it continuously learns to improve outcomes. It doesn’t wait for a prompt as it knows how to act, adapt, and move your workflows forward.
AI is evolving fast, and it can be hard to keep pace with it sometimes.
Here’s what business leaders, innovators, and technologists who have been on our Pioneers podcast have said about artificial intelligence and crucial differences between GenAI and Agentic AI.
“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.
“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 Generative AI helps well with content creation. It’s great for speeding up tasks like 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.
“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 paired with autonomous AI Agents, generative AI becomes far more valuable.
For example, instead of just generating a customer response, it can be used within an Agentic AI system that drafts, sends, and follows up, all while learning and improving from results.
“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.
“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.
“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, yet it’s a digital teammate that can manage full workflows.
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 member of a team.
Agentic AI Agents and systems like Decision AI can autonomously evaluate and process data from multiple sources, assess risks, and make underwriting or investment decisions without constant human supervision.
Instead of just generating decision reports, Agentic AI also triggers actions, escalates issues, and closes loops.
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.
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.
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.
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.
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.
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.
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.
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 why it’s needed and 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:
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.
If your next question is who offers Agentic AI or what the AgentFlow platform is, you’re in the right place!
We specialize in vertical Agentic AI solutions designed specifically for insurance and banking, some of the most regulated industries in the world, where accuracy, compliance, and speed aren’t optional.
Our AI Agents in the AgentFlow platform are trained to operate independently within the real-world constraints of financial services, automate complex tasks, and work alongside your existing systems securely and intelligently.
Whether it’s underwriting, claims processing, compliance workflows, or customer operations, our all-in-one Agentic AI platform, AgentFlow, can help your team get more done with less friction and better outcomes.
Are you curious how implementing Agentic AI could look in your company? Please book a free demo call where our experts can discuss your needs and show you how our Agentic AI works live and how it can seamlessly integrate into your business.
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.
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.
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.
Explore how our agentic AI can automate your workflows and boost profitability.
Get answers to all your questions
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