The first of a series of OCI GenAI Agents, the RAG Agent provides out-of-the-box RAG capabilities, enabling customers to get started while avoiding manual processes such as agent planning, retrieval, reranking, generation, and integration. It also provides a self-check to reduce hallucinations, enabling customers to adopt RAG technologies to streamline their business processes without spending cycles on research and development. OCI GenAI Agents enable customers to access Oracle Database 23ai AI Vector Search and run fast similarity queries on enterprise data stored in the database. For customers who have a subscription to Oracle Database 23ai on OCI, the GenAI Agents service adds an automation layer to execute RAG and similarity search functions without having to move data to a separate vector database.

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For organizations looking for open source solutions for their generative AI workloads, OCI GenAI Agents service also supports OCI Search with OpenSearch.

“AI is driving breakthroughs and efficiencies at an unprecedented pace, leading to new business models, applications, and innovation across industries,” said Greg Pavlik, executive vice president, AI and Data Management Services, Oracle Cloud Infrastructure. “With the OCI Generative AI Agents service, Oracle Database 23ai customers on OCI can subscribe to a fully managed RAG service that enables them to talk to their data. It’s AI designed for companies looking to put AI into their real-world production environments quickly—by bringing AI to their data.”

SOURCE: PRNewswire

The post Oracle Launches Generative AI RAG Agent to Solve Business Issues appeared first on ITDigest.

One-size-fits-all solutions can often inadvertently sweep important characteristics of unstructured data environments under the rug. Granted this short-term, simple solution is easier than putting in the work to customize the best-fit solution per each organization’s data set. However, anyone undertaking an unstructured data management project should be forewarned, this methodology can create a longer-term detrimental effect known as “insider knowledge.” Insider knowledge is what is left when a group of people spend time setting up data management systems within an organization and then move on from their roles. It makes things incredibly difficult for those who inherit the agglomerated system and, chances are, the organizations who currently have this problem don’t yet realize they have it.

The Beginning of the End – Creating Insider Knowledge

Today’s storage administrators have a lot on their plate and are perpetually spread thin. Their daily responsibilities typically include:

• Creating shares and exports

• Designing a filesystem layout

• Assigning devices for each app or dataset

• Hunting down unused data

• Organizing and pairing workflows to datasets

Often, with their busy day-to-day environment, the IT team’s decisions and actions are being made with a siloed approach. And as the project progresses, a unique language known only to the IT professionals overseeing the project is created. They may create their own tags and place files by a certain organizational system. However, most of this is not written down, meaning it is left to be deciphered like a code without an answer key. This language they created is the “insider knowledge.”

The Issue with Insider Knowledge in the Time of the Great Resignation

Why is this an issue, as long as someone knows what is going on in the overall picture of the data? Solely depending on a team of IT professionals who created the workflow means that they must stick with the company for their systems to make sense. After all, once the people with that knowledge leave, it is largely lost forever to the company.

It is not a bet worth taking that the employees of a company will merely stick around to see the data management project to completion. The threat that insider knowledge brings to organizations is that only the people with an idea of how the system works can properly onboard the next hire. This is a great position to be in for the IT professionals who know everything about the system, as there is a sense of job security, but it’s not great for the organization.

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For organizations, 44 percent of employees in the workforce are active job seekers. The Great Resignation has showcased that job seekers have the upper hand in the market today, and that good talent can be bought and poached from companies without a moment’s notice. When—not if—an organizations’ IT team members move on, it is imperative to have a well-documented, organized foundation for the unstructured data management system so that the next team is able to pick up where they left off.

Otherwise, when team members leave, they will inevitably leave knowledge gaps and questions will arise such as:

• Are you hosting 20 TB of dead application data on your NAS?

• Is this 20 TB of data something the storage administrator was planning on deleting, or is it important?

• Are you going to pay to keep that data on tier 1 disks?

• Do you have old employee files that were archived to other storage devices, but were never deleted?

The implications of insider knowledge are important to remember before beginning to tackle a data management project. It truly is as simple as future casting and deciding not to give a few people the control over an entire enterprise’s unstructured data.

Leave it to the Technology – What to ask when finding a solution

To manage unstructured data, there are now many tools on the market which were built with insider knowledge, and the pre-existing codes within organizations, in mind. By automating the process as much as possible, organizations can avoid a headache when their IT team moves on. Some questions to ask to aid in finding the proper tool include:

• Location of data: How many storage devices are there? How many data centers are there? And how many countries are the data centers spread across? Do any live in the cloud?

• Scanning capabilities: Do the aforementioned locations of the data affect the tool’s ability to scan it? What functions can be performed, from just scanning to analyzing with big data abilities?

• Presentation: How do you receive the information from the tool? Is it digestible and usable? Will it easily highlight the forgotten data sets?

• Aftermath: When you have the information at your disposal and have the insider knowledge in front of you, how can it help you move forward to promote a healthier unstructured data environment?

• Time: How long will it take? How much data can the engine scan? And will this affect business-as-usual?

These are inquiries IT leaders must find answers to before deciding on the best solution for their organization’s long-term success. Regardless of how understaffed and overworked IT teams may be, in today’s modern data center, the last thing that should be done is give a few IT professionals complete control over an enterprise’s unstructured data management system. Staff turnover is never fun. In IT, it can severely impact an entire organization if IT teams are forced to start from scratch on projects because no one is left to translate the system’s language that was built on insider’s knowledge.

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With the amount of data created each day, the time to clean up and automate the process as much as possible is now, as the time to decide on a solution that works for today was yesterday. IT leaders who take time to evaluate the new technologies and comprehensive data management solutions available will be poised to spend their budgets wisely to reduce the risk of setbacks, delays, cost overruns, and even IT team turnover.

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