Artificial intelligence (AI) has evolved rapidly, transforming industries and redefining the way we work. While traditional AI systems have been impressive, a new wave of AI, known as agentic AI, is poised to help revolutionize business processes. This blog post delves into the world of agentic AI, exploring its definition, potential business applications, and why it’s crucial for organizations to understand its significance.

What is agentic AI?

To understand agentic AI, we first need to define the term agentic, which is an emerging adjective. As an adjective, ‘agentic’ relates to or is characteristic of an agent, especially in terms of autonomy, self-direction, or the capability to take independent actions toward achieving specific goals and learn from feedback. As a noun, it refers to AI systems designed with agentic properties. Unlike traditional AI, agentic AI can continuously monitor data inputs and, based on those inputs and user instructions, autonomously initiate actions and make decisions, all while continuously learning from its experiences. In short, agentic AI is capable of autonomously performing complex real time tasks.

Agentic AI has the following characteristics:

  • Autonomy – Think of it like a highly capable AI assistant that can handle routine tasks, make day-to-day decisions, and help solve problems without needing constant supervision or detailed instructions for every step.
  • Goal-oriented: Similar to how a dedicated project manager stays focused on key objectives, these systems persistently work toward defined business targets, whether helping with optimizing operations, increasing sales, or improving customer service.
  • Learning and adaptation: Like an experienced professional who gets better at their job over time, these systems improve their performance through information gained by interacting with their environment and receiving feedback from those interactions.
  • Proactive: Rather than just responding to requests like traditional AI based on large language models (LLMs), these systems can identify opportunities and potential issues before they arise, such enabling detection of upcoming bad weather and rerouting ground transportation—much like a strategic business advisor who brings opportunities to your attention.

“Agentic AI solutions turn AI implementations upside down from narrow AI that is task focused to broader AI that monitors business status, develops actions and automates business decisions. With the ease of configuring varied business and market data, agentic AI provides the opportunity to not only automate specific business workflows, but also create opportunities for uncovering innovative business opportunities.” 
– Viji Krishnamurthy, VP AI Product Management, Oracle

Ultimately, agentic AI intends to combine the reasoning, execution, and course correction mechanisms that humans typically use to accomplish goals, as shown in Figure 1.

Figure 1: A simplified Agentic AI system operational flow
Figure 1: A simplified Agentic AI system operational flow

Why does agentic AI matter now?

In today’s fast-paced, data-driven world, companies are under constant pressure to innovate and stay ahead of the competition. Agentic AI solutions provide a way to meet these challenges head-on, offering numerous advantages for businesses that embrace it with the following features and benefits:

  • Increased efficiency: Agentic AI can help automate routine tasks and optimize complex processes, allowing organizations to reduce operational costs and employees to focus on higher-value activities.
  • Enhanced decision-making: With access to vast amounts of data, agentic AI can assist in analyzing patterns, generate insights that humans alone might miss, and act to achieve business objectives.
  • Improved personalization: Customers expect personalized, fast, and proactive service. Agentic AI can respond in real time to customer inquiries, anticipate their needs, and help provide tailored, more timely recommendations, leading to better customer satisfaction and loyalty.
  • Innovation: Agentic AI can help organizations uncover new business opportunities and foster innovation by finding untapped markets, identifying trends, and automating product or service development enabling companies to be more agile.

As the volume of data continues to grow, the business environment becomes more complex, and organizations may increasingly choose to deploy agentic AI solutions to remain operationally efficient and competitive. In this landscape where agility and intelligence are paramount, agentic AI is likely to become a favored option for organizations to thrive in the future.

What’s possible with Agentic AI

In smart grid management, AI agents could function as distributed, autonomous managers of energy distribution, with each agent overseeing specific aspects of the grid to maintain balance and efficiency. These agents would interact with extensive data inputs and stakeholders like utility managers and grid operators, and together, they form the agentic AI solution that orchestrates real-time adjustments across the grid with the following processes:

  • Energy demand forecasting: Agents would analyze historical and real-time energy consumption data with weather forecasts and event schedules to predict energy demand for different regions. This data helps the system anticipate peak usage times and adjust distribution accordingly.
  • Energy source management: For grids that integrate renewable energy sources, such as solar and wind, agents would monitor weather conditions that impact energy generation. They would then allocate available renewable energy based on predicted demand, adjusting as conditions shift.
  • Load balancing: Agents would autonomously reroute energy flow to stabilize the grid, preventing overloads and helping power to be directed to high-demand areas. If certain areas face high demand while others see a decrease, agents could autonomously reallocate energy to avoid blackouts or excess power loss.

Utility managers and grid operators could interact with the AI through a central dashboard, receiving real-time status updates and adjusting distribution parameters if needed, such as during emergencies. Over time, the agentic AI solution would learn to recognize seasonal and regional consumption patterns, making it increasingly efficient at managing resources autonomously. The result would be a stable, reliable grid that maximizes the use of renewable sources, reduces energy waste, and adapts to both expected and unexpected demand changes.

Conclusion                                                                                                                      

With the ability to operate autonomously, pursue complex goals, and continuously learn from real-world interactions, agentic AI has the potential to revolutionize industries by driving efficiency, enhancing decision-making, and enabling proactive customer service. As the volume of data continues to grow and become more complex, the need for intelligent, adaptive systems will increase. To remain operational and competitive, organizations can increasingly rely on agentic AI solutions to manage this complexity, extract valuable insights, and help provide seamless operations. Embracing agentic AI is a strategic move towards a future of smarter, more resilient business operations. 

Oracle recognizes the role that agentic AI can play and has embarked on making it real throughout its AI stack, from AI services that allow developers to create AI agents to agents integrated in software-as-a-service (SaaS) apps. Oracle has integrated 50 generative AI agents in our Fusion Cloud SaaS applications. Powered by the latest innovations in generative AI, these agents help organizations achieve new levels of productivity by successfully completing frequent, repetitive tasks and allowing employees and managers to focus their time on more strategic tasks and initiatives. The new agents will enable customers to fully automate end-to-end business processes while also delivering personalized insights, content, and recommendations in the context of specific business processes and in support of specialized user roles.

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