Harnessing Intelligence of Agents by Utilizing Linked Data
Agentic AI, a burgeoning technology in the realm of artificial intelligence, is seeing increased traction as advancements further the development of autonomous business systems. Building upon the foundations of machine learning, Agentic AI operates as an independent entity, capable of making informed decisions based on multimodal data and algorithmic logic. This versatile technology can 'learn' and evolve through experience, and moreover, can act independently, making it distinct from earlier AI tools.
In the realm of supply chains, for example, AI agents can analyze market activity and historical demand trends to forecast inventory needs and suggest measures to avoid shortages, such as automating parts of the restocking processes. These agents adapt their behavior in response to changing market conditions, enhancing efficiency and performance. According to a survey, 26% of business leaders report their organizations are starting to shape strategic approaches around Agentic AI.
While the prospect of outsourcing such tasks to Agentic AI is enticing, concerns about trusting its actions and outputs are valid. As autonomous as it may be, the question of how to ensure that the decisions made by AI agents are truly grounded in the real world, or the enterprise's perception of it, remains. Much like our brains, AI agents require external sources and signals to enhance their reasoning capabilities, which can be provided by solutions and platforms that collect and present data in an accessible and retrievable form.
This need for reliable and accurate data is directly linked to the 'trust challenge' in autonomous AI systems. When agents must refer to multiple, dynamic external sources, the risk of errors increases. This is particularly true when it comes to conversational AI or task-based agents that plan and change actions depending on the context provided. By ensuring that data retrieval is explainable and accurate at each step of the decision-making process, users can have visibility into why the AI agent has made a particular decision and trust that the action is indeed the most effective and efficient.
This trust can be fostered through the use of graph databases. Graph databases are uniquely optimized to deliver accurate, transparent, and explainable insights, benefits that are essential for Agentic AI. By connecting data in a graph structure, previously hidden insights can be revealed, making it easier for AI agents to complete tasks in a more reliable and valuable manner.
The integration of Agentic AI and knowledge graphs has the potential to transform enterprise data. Connected data gives agents the context they need to think more clearly, generate smarter outputs, and have a greater impact. To unlock the full value of data and trust autonomous AI agents with sophisticated tasks, it is crucial to make enterprise data as rich, connected, and contextually aware as possible. As AI frameworks become more complex and AI agents more autonomous, making enterprise data accessible and manageable will become increasingly important.
- In the realm of data-and-cloud-computing, graph databases can serve as the external sources and signals that AI agents need to enhance their reasoning capabilities, providing reliable and accurate data necessary for Agentic AI to make efficient decisions.
- As the use of Agentic AI becomes more prevalent in businesses, the integration of these AI agents with knowledge graphs can transform enterprise data, enabling agents to think more clearly, generate smarter outputs, and have a greater impact by providing them with the context they need.
