Virtusa Recognized as Global Leader in Gen AI Services by ISG Provider Lens® - Read report
Artificial intelligence (AI) has long been part of the dynamic technological conversation. More recently, the emergence of generative AI (genAI) and agentic AI has joined the chat. While similar in some ways, these two AI models contain fundamental differences.
Generative AI channels the creative and imaginative side of artificial intelligence. GenAI systems are designed to support and create content, from text, images, music, code, and even video. Generative AI typically learns from existing data and uses this information to generate new and original outputs that mirror human imagination.
Examples of generative AI models include:
These AI models and systems rely on advanced machine learning and neural networks to analyze and replicate data patterns they are trained on.
While genAI can optimize creativity and content creation, its outputs are only as good as the data it’s trained on. If you feed genAI flawed, biased, or incomplete data inputs, its outputs will reflect those. Also, genAI can only predict what comes next based on the patterns it has seen or analyzed before.
Despite its flaws, generative AI is quickly transforming, optimizing, and reshaping creative industries—from music, entertainment, to design and marketing.
Where generative AI creates, Agentic AI acts. This AI model is designed to make decisions autonomously and solve complex tasks, most notably in a customer service and support role.
Agentic AI systems are designed to autonomously make decisions, solve problems, improve, and adapt to dynamic and evolving environments.
What sets agentic AI models apart is their ability to act with purpose. They don’t just react to inputs, they can consider objectives, decipher context, and make choices accordingly to achieve these requests.
For example, an autonomous or self-driving vehicle uses advanced software and hardware to navigate routes without human input. It uses sensors and cameras to perceive its surroundings, collect data, and process information through complex algorithms and machine learning models to make real-time decisions, such as detecting roadside obstacles and traffic conditions to determine the best path, all without human intervention.
However, the advanced autonomy of agentic AI also poses ethical questions: What happens if an autonomous AI model system makes a mistake? Who is responsible for the potential result or fall-out? How do we, as humans, ensure these AI systems align with human values? As agentic AI becomes more prevalent, we must answer these questions and challenges.
| Component | Agentic AI | Generative AI |
|---|---|---|
| Purpose | Automates decision-making and complex reasoning | Content generation – text, images, code |
| Function | Completes and execute tasks by understanding goals and context | Generates outputs based on patterns in training inputs and data |
| Strength | Autonomously handles complex, logic-based, and multi-step workflows | Produces creative, human-like outputs in text, images, code, etc. |
| Use case | Adaptive problem-solving, self-learning and improvement, and workflow automation capabilities | Content creation – brainstorming, writing, art |
| Examples | Autonomous troubleshooting system errors and customer support workflows, self-driving cars | Writing articles, social media copy, advertisements, designing graphics, coding prototypes |
Despite their key differences, generative AI and agentic AI can still work well together.
For example, an AI virtual assistant can benefit from agentic AI handling real-time customer interaction, such as receiving and answering inquiries, guiding the conversation, and making decisions based on user input. Then, generative AI can create nuanced responses to specific questions and steer the conversation accordingly.
Thriving in a world of creativity and design
To drive organic search traffic, companies are leveraging genAI to generate SEO-optimized content, such as blog articles, landing pages, and advertisement campaigns. GenAI tools can create tailored, target-audience-specific, and keyword-optimized blog posts or web pages to improve search engine optimization.
A fashion brand or company can leverage genAI to design new clothing lines and accessories tailored to consumer input, interest, and market data analysis.
Companies can leverage generative AI to generate responses to customer service inquiries, handle order status and processing inquiries, refund requests, and shipping questions. Meanwhile, sales teams can delegate administrative tasks to genAI through chatbots and virtual assistants, driving optimization for specific tasks and lead generation outreach.
The art of autonomously streamlining actions
These AI models can predictively assess a situation and troubleshoot issues without human intervention. Due to the real-time, instant response time, Agentic AI can significantly streamline and enhance customer experiences and troubleshooting support. Agentic AI can also automate time-consuming manual tasks by collecting and cleaning organizational data.
Agentic AI autonomously manages business processes and handles complex tasks such as invoice processing, supply chain operations, and internal workflows to help human employees save time and costs and maximize productivity. For instance, a logistics company might leverage agentic AI to adjust delivery routes and schedules based on real-time traffic conditions and shipment timelines.
Agentic AI can help financial companies optimize client objectives in real time by analyzing financial data and market trends to autonomously make decisions about credit risks, investments, and strategic financial decisions. Banks and financial institutions can leverage agentic AI capabilities to protect client investments and make smart decisions that result in higher ROI. Fintech firms may use agentic AI to monitor real-time economic, social, and political events that could impact market fluctuations and automatically adjust portfolio allocations and strategies.
As generative and agentic AI models continue to advance and innovate, the distinction between them will become increasingly blurred. Future technological and AI advancements will likely result in intelligent hybrid AI systems simultaneously creating and acting, merging content generation and autonomous deployment.
In the future, there may no longer be genAI and agentic AI as separate entities, but a combined super AI that not only conjures up innovative concepts but also places them into action, redefining what’s currently possible in the current market.
However, all powerful innovations come with challenges and heightened responsibility. Society must develop and deploy these technologies with robust transparency, accountability, and ethics. By understanding the key differences and values that generative AI and agentic AI bring, we can better appreciate and utilize their unique capabilities, working towards a future where AI doesn’t replace but enhances humanity to new transformative heights.
Virtusa Helio: Illuminating next generation of enterprise innovation with generative AI services