Why is the importance of Agentic AI significant?

In a world where efficiency and innovation are paramount, agentic AI is revolutionizing how businesses operate. By combining autonomy and proactive problem-solving, this cutting-edge technology empowers systems to not only respond to challenges but anticipate and address them before they escalate. Imagine a workforce that never sleeps, constantly optimizing processes, increasing productivity, and enhancing decision-making with unparalleled precision. Agentic AI is not just a tool—it’s a transformative force, reshaping industries and unlocking new possibilities for growth and success. Join us as we explore how this groundbreaking technology is setting the stage for a smarter, more efficient future.

 

Examples of Agentic AI in Enterprise applications

For customers leveraging Oracle Cloud Infrastructure (OCI) and Oracle Applications, agentic AI is a game-changer, bringing autonomy, proactive problem-solving, and unparalleled efficiency to your operations. By integrating agentic AI into your OCI and Oracle ecosystem, you can unlock new levels of productivity, enhance decision-making, and future-proof your business. Here’s how agentic AI is transforming key areas of your Oracle-powered environment:

1. Personal Virtual Assistants:

Scenario: A personal assistant that goes beyond simple reminders.

Agentic AI Role:

Agents analyze your schedule, emails, and online activity.  

They proactively book appointments, manage travel, and even anticipate your needs (e.g., ordering groceries when you’re running low).  

Agents can negotiate with other agents (e.g., for meeting times) to optimize your schedule.

Agents can learn your preferences over time.  

 

2. Smart Manufacturing and Industrial Automation:

Scenario: Optimizing production lines and maintenance.  

Agentic AI Role:

Agents monitor sensor data from machines.  

They predict equipment failures and schedule preventative maintenance.  

Agents adjust production parameters in real-time to maximize efficiency and minimize waste.  

Agents can coordinate with robotic arms to handle complex assembly tasks.

 

3. Intelligent Supply Chain Optimization:

Scenario: Managing complex, global supply chains.  

Agentic AI Role:

Agents track shipments, monitor inventory, and predict demand.  

They can dynamically reroute shipments to avoid delays or disruptions.  

Agents can negotiate contracts with suppliers and manage procurement.  

Agents can notice, and react to, global events that will effect the supply chain.

 

Basic agentic AI architecture

Agentic AI workflow diagram
Architecture Flow of Agentic AI 


Primary components of the architecture:
 

  1. The user initiates a request to the application’s LangChain.  LangChain acts as a toolbox and a set of building blocks that developers can use to create LLM-powered applications more easily and efficiently. It handles the “plumbing” of connecting LLMs to external systems and orchestrating their actions, so developers can focus on the core logic of their applications.
     
  2. RAG

Retrieval Augmented Generation (RAG) empowers agents to handle complex queries by intelligently assessing the query’s nature. First, the agent analyzes the query to determine if it can be answered with its pre-existing knowledge. If not, it initiates external data retrieval. This process involves strategically routing the query to the most relevant retrieval tool or knowledge

source, ensuring accurate and up-to-date information is incorporated into the response, thus enhancing the agent’s ability to provide comprehensive and contextually relevant answers.

Tools Used: 

  • OCI Data Science is a comprehensive platform for data scientists to build, train, and deploy machine learning models
  • OCI Data Integration is a fully managed, serverless cloud service on OCI that helps you extract, load, transform, and orchestrate data across various sources and targets
  • OCI Search with OpenSearch is designed to provide powerful search capabilities for applications and data stored within OCI     

 

 

  1. The LLM
    Oracle Cloud Infrastructure (OCI) supports the use of Large Language Models (LLMs) by providing the necessary computing and data storage resources. Users can utilize OCI’s infrastructure, including GPUs, for training and deploying LLMs. OCI’s data management capabilities enable the handling of large datasets required for LLM operations. Additionally, pre-trained models and managed services are available, which can simplify the integration of LLMs into various applications.

 

Tools Used (Examples)

Database LLM such as HeatWave LLM: Oracle HeatWave GenAI provides integrated, automated, and secure generative AI with in-database LLMs

 

Pre-trained Models such as Cohere: Emphasizes enterprise-grade LLMs and prioritizes reliability, safety, and ease of integration into business workflows.

 

Opensource LLMs such as Meta Llama 2 and Llama 3.1 are a series of LLMs developed by Meta AI
 

  1. Agentic AI 
    The Agentic AI agents, designed to perform tasks autonomously, integrate seamlessly with the components described earlier. The agent’s core reasoning and decision-making capabilities are often powered by an LLM, which provides the natural language understanding and generation required for interaction and task execution. When faced with a complex query or task, the agent leverages LangChain to orchestrate the necessary steps. LangChain allows the agent to connect to external data sources and tools, forming action chains to achieve goals. For tasks requiring information retrieval, the agent utilizes RAG pipelines, also facilitated by LangChain. In this process, the agent’s LLM analyzes the query, LangChain routes it to appropriate retrieval tools, and the retrieved information is then used to ground the LLM’s response, ensuring accuracy and relevance. Essentially, the agent acts as a coordinator, using the LLM for intelligence, LangChain for tool and data integration, and RAG for knowledge augmentation.

    Agent coordination is the “brain” behind agentic AI systems. It’s what transforms a collection of individual agents into a cohesive and effective team. The Agentic AI agents, designed to perform tasks autonomously, integrate seamlessly with the underlying framework that manages their interactions, coordination, and shared objectives.


Additional services used:  

 

  • OCI Functions (Serverless) OCI Functions offer a serverless environment that can be utilized by Agentic AI systems for task execution. Agents can use these functions to divide complex processes into smaller, manageable units, interact with external APIs, and respond to events. This allows for scalable task handling, modularizing the agent’s actions and facilitating integration with external systems, supporting autonomous and efficient operations.
  • OCI Container Engine for Kubernetes (OKE) ) provides a platform for deploying and managing containerized applications, which is beneficial for agentic AI. Containerization allows for the packaging of agent components and their dependencies into portable units, simplifying deployment and scaling. OKE helps manage these containers, ensuring efficient resource allocation and orchestration. This is particularly useful for agentic AI systems that may require dynamic scaling or distributed processing. By using OKE, developers can create a flexible and scalable infrastructure for running their agentic workflows.
  • OCI Streaming data plays a vital role in enabling real-time responsiveness for agentic AI. By processing data streams, agents can react to changing conditions and user actions immediately. This allows for continuous learning and adaptation, as agents can incorporate new information as it arrives. For example, agents monitoring social media feeds or sensor data can identify and respond to trends or anomalies without delay. Streaming enables agents to maintain up-to-date knowledge and perform actions with minimal latency, fostering more dynamic and interactive experiences.
  • OCI Events are crucial for agentic AI, as they trigger actions and drive dynamic behavior. When an event occurs, such as a change in data or a user interaction, the agent can respond by initiating specific tasks or workflows. This event-driven approach allows agents to react in real-time to changes in their environment, enabling them to adapt and perform tasks based on current conditions. By monitoring and reacting to events, agents can automate processes, provide timely responses, and maintain up-to-date information, enhancing their ability to perform autonomously and efficiently.
  • OCI Data Flow enables agents to perceive, reason, and act within their environment. Agents continuously receive data from various sources, which is then processed to understand context and make informed decisions. This processed information guides their actions, which in turn generate new data, creating a feedback loop. Efficient data flow, including collection, processing, and storage, facilities agents access to the necessary information to perform tasks effectively. This cyclical process allows agents to learn and adapt, continuously refining their understanding and actions based on the information they receive.
  • OCI API Gateway acts as a central point of control for agentic AI interactions with external systems. It manages incoming requests, routing them to the appropriate services or functions, which can be crucial for an agent needing to access various data sources or tools. This allows for streamlined communication, security enforcement, and traffic management, simplifying the agent’s ability to interact with diverse APIs and ensuring a more organized and secure flow of information.

 

 

Conclusion

 

Agentic AI on Oracle Cloud Infrastructure offers a paradigm shift in enterprise operations. By leveraging the power of LLMs, orchestrated by LangChain and augmented with RAG, these systems enable autonomous, proactive problem-solving. From personalized virtual assistants to intelligent supply chain optimization, agentic AI automates complex tasks, enhances decision-making, and drives efficiency. OCI’s robust infrastructure, including Data Science, Data Integration, Functions, and Search, provides the necessary tools and resources to build and deploy these sophisticated systems. The integration of various LLMs, both proprietary and open-source, further empowers businesses to tailor solutions to their specific needs. Ultimately, agentic AI on OCI transforms isolated agents into a coordinated, intelligent team, paving the way for a future where systems not only react to challenges but anticipate and prevent them, unlocking new levels of productivity and innovation.

 

Ready to dive into the future of work with AI?

 

Oracle’s comprehensive documentation offers a wealth of information on their cutting-edge agentic AI technologies. Whether you’re looking for a high-level understanding of what AI agents are and their potential benefits, or a deep dive into the Oracle AI Agent Studio that empowers you to build and customize your own intelligent assistants within Oracle Fusion Applications, the resources are readily available. Explore the Introductory Guide to Oracle Fusion AI Agents to grasp the fundamentals and real-world applications, and then delve into the specifics of the Generative AI Agents service on the Oracle Help Center to understand the technical underpinnings. Don’t miss the announcements about the Oracle AI Agent Studio and the integration of AI agents across various Oracle Cloud services to see the transformative power in action. Start your journey today and unlock new levels of productivity and innovation by exploring these pivotal Oracle documents on agentic AI.

 

Generative AI Agents

https://docs.oracle.com/en-us/iaas/Content/generative-ai-agents/overview.htm

Oracle Introduces AI Agent Studio

https://www.oracle.com/news/announcement/oracle-introduces-ai-agent-studio-2025-03-20/

Fusion AI Agents guide

https://www.oracle.com/sa/a/ocom/docs/applications/fusion-ai-agents-guide-ae.pdf