Artificial Intelligence is evolving rapidly, with Generative AI tools like ChatGPT dominating headlines for their ability to create text, images, and other content. However, a new concept is emerging—Agentic AI. Let’s explore what agentic truly means, how it differs from Generative AI, and why it could be the next major leap in artificial intelligence.
What does “agentic” mean?
Agentic AI is an artificial intelligence system designed to operate with a high degree of autonomy, enabling it to make decisions and take actions independently to achieve specific goals. Unlike traditional AI models that require explicit instructions for each task, agentic AI possesses the capability to assess situations, plan strategies, and execute tasks without continuous human oversight.
Agentic AI “uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems.” This involves integrating advanced AI techniques, such as large language models (LLMs), machine learning algorithms, and reinforcement learning, to create systems capable of dynamic decision-making and adaptability.
This technology is currently transforming industries:
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Financial Services: AI-powered agents can autonomously execute tasks such as transferring funds between accounts. For example, instead of answering, “What is the policy for creating new accounts?”, the agent completes actionable requests like, “I would like to transfer $100 from account A to account B.”
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Insurance: AI systems can assess damage through image recognition, validate claims against policies, and automatically approve payouts—all without manual review—saving time and reducing operational costs.
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Telecoms: AI agents autonomously detect service disruptions, reroute network traffic, and restore connectivity, minimizing downtime and improving user experience.
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Public Sector: In social welfare programs, AI agents autonomously validate eligibility criteria, process applications, and allocate benefits, ensuring faster delivery of public services while reducing administrative burdens.
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Healthcare: Agentic AI systems can analyze patient data, monitor real-time health metrics, and autonomously recommend tailored treatment regimens, improving patient outcomes with minimal human intervention.
The difference with Generative AI
To understand the distinction between Agentic AI and Generative AI, we need to look beyond what these systems produce and focus on how they behave and operate. While both rely on advanced AI models, their roles and capabilities differ fundamentally:
Generative AI - the creative powerhouse:
Generative AI refers to systems that create content—text, images, videos, or music—based on input prompts. These models, such as GPT-4 or MidJourney, leverage extensive datasets to predict and produce outputs.
Use cases:
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Writing marketing copy, blogs, or product descriptions.
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Creating images, videos, and other creative assets.
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Developing code snippets or summarizing lengthy content.
Agentic AI - the autonomous achiever:
Agentic AI, on the other hand, takes AI capabilities further by executing actions in underlying systems to achieve goals on behalf of the user. These systems plan, execute, and adapt without ongoing human direction, behaving more like autonomous agents.
If Generative AI is like a talented musician playing a song on request, Agentic AI is the composer, orchestra leader, and performer all in one—organizing the entire concert.
Proactivity vs. Asynchronous Behavior
It is important to clarify that proactivity in Agentic AI does not equate to asynchronous behavior. While asynchronous systems perform tasks in the background or after some delay, proactivity refers to the AI autonomously taking actions and completing tasks as needed.
For example:
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The traditional Conversational AI response to a request like “I want to transfer $100 from account A to account B” would be: “Please log in here, press this button, and follow these steps.”
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The Agentic AI response would instead be: “This is now done. $100 has been transferred from account A to account B.”
The proactive nature of Agentic AI lies in its ability to execute the request directly in the relevant systems, eliminating the need for step-by-step instructions or human intervention.
Agentic AI vs Generative AI in customer service:
Imagine a bank’s virtual assistant helping customers with routine requests. With Generative AI technology, a customer would ask “What’s my account balance?” or “Can you tell me my last three transactions?” and the AI would quickly provide the information. While efficient, this AI remains reactive—it only responds to specific queries and does not go beyond the immediate task.
Now consider an Agentic AI agent in the same financial context. The agent not only answers questions but proactively monitors account activity and takes necessary actions:
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Fund Transfers: A user requests, “Transfer $100 from account A to account B” and the agent immediately executes the transfer, confirms it, and provides a summary.
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Remortgaging: The agent handles the entire remortgaging process on behalf of the user. It initiates the application, runs credit checks, evaluates current loan terms, gathers required documentation, and identifies the best available rates. Once complete, it presents the final options and facilitates the transfer seamlessly.
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Financial Advice: The agent recognizes overspending trends and proactively suggests a budget plan, automatically creating a report for the user.
In these examples, generative AI acts as a helpful assistant answering queries, while agentic AI executes actions proactively, ensuring smoother and more efficient operations.
The evolution from Generative to Agentic AI The shift from generative to agentic AI represents a paradigm change. While generative AI democratized content creation and creativity, agentic AI pushes boundaries by making systems capable of reasoning, decision-making, and autonomous execution.
This evolution is key for industries requiring end-to-end automation and problem-solving, where human involvement is minimized. Agentic AI does not replace generative AI but builds on its foundation, unlocking smarter, proactive systems to meet the demands of an increasingly complex digital world.
