Agentic AI is evolving beyond initial hype and moving toward meaningful innovation. Autonomous business systems are emerging from breakthroughs in Agentic AI, acting as self-directed ‘agents’. The technology can make informed decisions by drawing on multimodal data and underlying algorithms, and can then ‘learn’ from the outcomes of those decisions.
The defining strength of Agentic AI versus its predecessors lies in that very ability to plan, operate, and adapt independently and execute tasks from start to finish. Considering its applications in supply chains, for example, AI agents can analyse market trends and historical demand to anticipate stock requirements and help mitigate out-of-stock scenarios, such as by automating restocking processes. These agents respond to fluctuating market conditions and adjust their behaviour to better support supply chain optimisation. It’s no surprise, then, that more than one in four leaders (26%) say their organisations are beginning to define strategic roadmaps for Agentic AI.
However, as promising as it sounds to outsource such tasks to Agentic AI, it also calls for careful oversight. 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 can we be confident its decisions are truly grounded in what’s happening in the real world, or in the enterprise’s view of the world?
Recent regulation efforts, including amendments to the Data (Use and Access Bill), which mandate greater transparency of what data is used to train AI, also signpost the need to integrate greater transparency and oversight.
In the same way our brains use observation and extra inputs to draw conclusions, AI agents require extensive external sources and signals to enhance their reasoning capabilities. Hence, we need solutions and platforms that efficiently collect and present data in a way that is both accessible and retrievable.
Addressing the complexities of AI-driven decision-making
As discussed, unlike traditional AI systems that typically engage in linear conversation, Agentic AI is designed to operate autonomously. The complexity of the tasks handled by agents often requires access to 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 optimise 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.
Leveraging knowledge graphs to unlock deeper insights
For Agentic AI to make informed, data-driven decisions, the underlying insights must be accurate, explainable, and grounded in context – capabilities that graph databases are purpose-built 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, made up 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.
What users have been finding with GraphRAG is that not only are the answers more accurate, but they are also richer, more complete, and consequently more useful. For example, an AI agent addressing customer service queries could offer a particular discounted package on broadband 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 customer and aspects of their 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 LinkedIn’s customer service team, who have reduced median per-issue resolution time by 28.6% since implementing GraphRAG.
Transforming your data ecosystem for the Agentic future
The intelligence of AI agents is shaped not only by the models themselves but also by the robustness of the data environment they operate within. The LLMs powering AI agents are ready. The critical next phase is ensuring your enterprise data is rich, interconnected, and contextually aware, making it fully accessible and actionable for intelligent agents. This approach unlocks the full potential of your data, empowering agents that not only operate with more accuracy and efficiency but are also more explainable in their actions. The integration of Agentic AI with knowledge graphs becomes a genuine game-changer in this transformation. By providing richer context through connected data, agents are equipped to reason more effectively, generate smarter results, and deliver greater real-world impact.
