Agentic AI is one of the latest concepts in artificial intelligence, now gaining real traction beyond its early buzz. Ongoing advancements in Agentic AI are accelerating the development of autonomous business systems, building on the achievements of machine learning.
Operating as an independent ‘agent’, this technology is equipped to make informed decisions based on the multimodal data and algorithmic logic, and can then ‘learn’ and evolve through experience.
Even more exciting is its capacity to act independently. It’s this unique ability to adapt, plan, and carry out complex tasks without human oversight that distinguishes Agentic AI from earlier generations of AI tools.
In supply chains, for instance, AI agents can track market activity and historical demand trends to forecast inventory needs and implement measures to avoid shortages, such as by automating parts of the restocking processes. These agents shift their behavior in response to changing market conditions, boosting efficiency and performance. It’s therefore no surprise that 26% of business leaders report their organizations are beginning to shape strategic approaches around Agentic AI.
However, as great as it sounds to outsource such tasks to Agentic AI, we also need to err on the side of caution. For all its autonomous power, how can the actions and outputs of AI agents be fully trusted? If we rely on Agentic AI to complete sophisticated tasks on its own, how do we ensure its decisions are truly grounded in what’s happening in the real world, or in the enterprise’s view of the world?
In the same way our brains use observation and extra inputs to draw conclusions, AI agents need to rely on a lot of external sources and signals to enhance their reasoning capabilities.
This need can be met by solutions and platforms that collect and present data in a way that’s accessible and retrievable. Here’s how:
The trust challenge in autonomous AI systems
As discussed, what sets Agentic AI apart from other AI systems is its ability to act autonomously, not just engage in a linear conversation. The complexity of the tasks agents complete typically requires them to refer to multiple, dynamic external sources. As a result, the risk of something going wrong automatically increases. For example, you might trust a chatbot to provide you with an update on the status of a claim or refund, but would you feel as trusting when giving an AI agent your credit card details to book a flight for you?
Away from conversational AI, task-based agents plan and change actions depending on the context they’re given. They delegate subtasks to the various tools available through a process often referred to as “chaining” (the output of one action becomes the input for the next). This means that queries (or tasks) can be broken down into smaller tasks, with each requiring access to data in real-time, processed iteratively to mimic human problem-solving.
The chain effect (in which decisions are made) is informed by the environment that’s being monitored, i.e., the sources of data. As a result, explainable and accurate data retrieval is required at each step of the chain for two reasons. Firstly, users need to know why the AI agent has landed on a particular decision and have visibility of the data source it’s based on.
They need to be able to trust that the action is, in fact, the most effective and efficient. Secondly, they need to be able to optimize the process to get the best possible result each time, analysing each stage of the output and learning from any dissatisfactory results.
To trust an agent to complete sophisticated tasks based on multiple retrieval steps, the value of the data needed to support the decision-making process multiplies significantly.
The need to make reliable enterprise data available to agents is key. This is why businesses are increasingly recognising the power of graph database technology for the broad range of retrieval strategies it offers, which in turn multiply the value of the data.
How graph technology strengthens AI reasoning
As Agentic AI drives decisions from data, the insights underpinning these decisions must be accurate, transparent, and explainable – benefits that graph databases are uniquely optimized to deliver. Gartner already identifies knowledge graphs as an essential capability for GenAI applications, as GraphRAG (Retrieval Augmented Generation), where the retrieval path includes a knowledge graph, can vastly improve the accuracy of outputs.
The unique structure of knowledge graphs, comprised of ‘nodes’ and ‘edges’, is where higher-quality responses can be derived. Nodes represent existing entities in a graph (like a person or place), and edges represent the relationship between those entities – i.e., how they connect to one another. In this type of structure, the bigger and more complex the data, the more previously hidden insights can be revealed. These characteristics are invaluable in presenting the data in a way that makes it easier for AI agents to complete tasks in a more reliable and useful way.
Users have been finding that GraphRAG answers are not only more accurate but also richer, speedier, more complete, and consequently more useful. For example, an AI agent addressing customer service queries could offer a particular discounted broadband package based on a complete understanding of the customer, as a result of using GraphRAG to connect disparate information about said customer. How long has the customer been with the company? What services are they currently using? Have they filed complaints before?
To answer these questions, nodes can be created to represent each aspect of the customer experience with the company (including previous interactions, service usage, and location), and edges to show the cheapest or best service for them. A fragmented and dispersed view of the data could lead to the agent offering up a discounted package when it was not due, leading to cost implications for the business.
As mentioned by the CEO of Klarna, “Feeding an LLM the fractioned, fragmented, and dispersed world of corporate data will result in a very confused LLM”. But the outcome is very different when data is connected in a graph: Positive results have been reported by the likes of LinkedIn’s customer service team, who have reduced median per-issue resolution time by 28.6% since implementing GraphRAG.
Why connected data is key to Agentic AI readiness
With every iteration, the LLMs behind AI agents are advancing quickly, and agentic frameworks are making it easier to build complex, multi-step applications. The next vital move is to make enterprise data as rich, connected, and contextually aware as possible, so it’s fully accessible to these powerful agents.
Taking this step allows businesses to unlock the full value of their data, enabling agents that are not only more accurate and efficient but also easier to understand and explain. This is where the integration of Agentic AI and knowledge graphs proves transformational. Connected data gives agents the context they need to think more clearly, generate smarter outputs, and have a greater impact.
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