Singapore’s ambition to become a global hub for artificial intelligence (AI) is rapidly taking shape, fueled by over $1 billion in government investments. But beyond the headlines lies a crucial shift unfolding in businesses across the city-state: the rise of autonomous AI agents, or agentic AI.
Unlike traditional AI systems, these self-directed agents can execute complex tasks without human intervention. Leveraging data captured in multiple formats and iterative learning, they independently make informed decisions. Industry analysts project that by 2028, 33% of enterprise software applications will incorporate agentic AI, up dramatically from under 1% in 2024.
In Singapore’s retail sector, a recent report highlighted that 69% of businesses now view AI agents as essential for maintaining a competitive advantage, and 85% of Singaporean retailers plan to boost AI investments in the next year alone, demonstrating a strong sense of urgency and growing confidence in the promise of AI.
Interoperability and trust as twin barriers to adoption
Despite this promising trajectory, significant barriers threaten to slow agentic AI's integration into Singapore's business ecosystem. The most pressing challenge lies in interoperability, or rather, its absence. Most AI agents today remain locked within proprietary technology stacks, with platform-specific memory and orchestration systems that prevent seamless coordination across different platforms.
This limitation constrains what should be flexible, adaptive tools into technological silos. Without the ability to share context or delegate tasks across systems, these AI agents cannot deliver their full potential. For organisations looking to scale AI adoption across different business units, it stalls AI deployment at the boundaries of vendor ecosystems.
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In consumer-facing applications, trust has also emerged as a critical concern. When AI agents make purchase decisions on behalf of shoppers, Singaporean consumers express clear requirements for establishing trust. A report revealed that their highest-ranked factors include robust data privacy and transparency in how their data is used. However, like most Large Language Model (LLM)-powered applications, agentic AI may operate in “black boxes”, where its internal decision-making processes remain opaque to users or even developers. This lack of transparency undermines consumer confidence, especially in retail contexts where personal and financial information is at stake.
Building a reliable data foundation with knowledge graphs
Addressing these twin challenges requires a fundamental shift in how we structure and connect the data that powers AI agents. Knowledge graphs have emerged as a critical technology for bridging these gaps by providing a shared data foundation that enables agents to reason over connected, context-rich information.
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The unique structure of knowledge graphs, made up of "nodes" representing entities and "edges" showing the relationships between them, creates a framework suited for AI reasoning. This approach solves the interoperability problem by establishing a common structure that different agents can use, and establishes a standardised repository for AI agents to access information, regardless of their underlying platform or vendor.
Trust also improves substantially as graph-based systems offer transparent data lineage and clear reasoning paths. AI decisions become explainable through linked context, which also grounds AI outputs in factual and context-rich data, reducing the "hallucinations" plaguing large language models (LLMs) when they operate with unstructured or inaccurate data.
From fragmented information to comprehensive insights
What users discover with GraphRAG (Retrieval Augmented Generation using knowledge graphs) is that responses become not just more accurate, but richer, more complete, and consequently more useful. GraphRAG applications incorporate knowledge graphs in the information retrieval process for AI models, enabling AI agents to make informed decisions.
Consider a practical retail scenario: An AI agent addressing customer service queries could recommend a personalised discount package based on a comprehensive customer understanding, drawing on connected information about their tenure, current service usage, and history of interactions.
Without this connected view, an agent might offer inappropriate discounts based on fragmented data, creating both customer confusion and business losses. However, with a knowledge graph connecting disparate pieces of information, the agent can see that a customer has been loyal for five years, currently uses three distinct services, and has recently filed a complaint, enabling truly personalised and appropriate recommendations.
Preparing for the agentic era
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The performance of agentic AI hinges on the quality of the data that powers it. The integration of agentic AI with knowledge graphs is a game-changer, with connected data providing the deeper context for agents to reason effectively and deliver greater real-world impact with smarter results.
As Singapore continues its journey toward becoming a global AI hub, businesses must recognise that the LLMs powering AI agents are evolving rapidly with each iteration. Agentic frameworks are steadily lowering the barriers to building sophisticated, multi-step applications that can transform operations across retail, supply chain, customer service, and beyond.
The essential next step for Singapore’s enterprises is ensuring their data is as rich, connected, and contextually aware as possible, making it fully accessible and usable by these intelligent agents. This preparation helps unlock the true value of organisational data, enabling agents that are more accurate, efficient, and transparent in their actions.
Xander Smart is the general manager for Asean at Neo4j
