The Agentic AI/Graph Database Combo Powering Emerging Applications

Static AI models that provide insights on demand are no longer enough. Today’s enterprise needs systems that can dynamically adapt, make autonomous decisions, and optimize workflows in real time. Enter Agentic AI, a fast-evolving approach in artificial intelligence that’s gaining serious traction for its potential to enable systems that act with autonomy and intent. 

Agentic AI goes beyond pattern recognition to perception, reasoning, action, and learning in real-world environments. But to fully unlock its potential, Agentic AI needs the right data infrastructure—one that can handle complex relationships, adapt in real-time, and scale with ever-growing data demands. This is where graph databases come in, powering the next generation of agentic AI graph architectures, and where TigerGraph takes it even further.

Let’s start with the definitions.

Defining the Key Components

Agentic AI: AI That Acts, Not Just Reacts in Real Time

Agentic AI refers to AI systems that act independently to achieve a specific goal—for example, monitoring data in real-time and adapting its actions accordingly. To do this, an AI agent follows a structured process: it plans, executes, learns from outcomes, and adjusts based on changing conditions.

These capabilities make agentic AI a breakthrough for enterprise data management, continuously aligning insights with live operational data.

Relational Databases vs Graph Databases: Why Structure Matters

Most enterprise applications rely on relational databases, which work well for storing structured data in tables. However, they struggle with highly interconnected data. For instance, when analyzing multiple layers of connections (multi-hop relationships), such as tracing a product’s supply chain or detecting fraud across multiple accounts—relational databases rely on complex joins across multiple tables (combining data from two or more tables based on a shared key or common column). This approach becomes slow and inefficient as data complexity increases.

Additionally, relational databases aren’t built for real-time relationship analysis. They lack efficient graph traversal, meaning they can’t quickly follow connections between data points as they change. As businesses scale and data volumes grow into the billions, executing queries at high speed becomes increasingly difficult, leading to delays and performance bottlenecks.

Graph Databases: The Foundation for Agentic AI Applications

Graph databases are revolutionizing how businesses manage interconnected data. They are designed to overcome relational database limitations. 

Instead of rigid tables, they store data as nodes (entities) and edges (relationships), making it easier to connect, analyze, and traverse complex relationships. Unlike relational databases, graphs allow AI to retrieve insights in real-time, making them ideal for fraud detection, recommendation systems, supply chain optimization, and knowledge graphs.

This means AI can process relationships instantly, uncovering previously hidden patterns or too slow to analyze with traditional databases. Graph databases enable AI to make more informed decisions in real-time, as they represent knowledge with identifiable entities and rich, meaningful relationships.

TigerGraph: The Next Evolution in Enterprise Graph Databases

While graph databases provide a strong foundation, TigerGraph takes it to the next level. As a native parallel graph database, TigerGraph is designed for high-performance, enterprise-scale analytics.

It was specifically designed as a graph where managing and tracing relationships is its primary function (native), without resorting to table joins or any extra modeling layers. It breaks down complex graph queries into smaller tasks and processes them simultaneously across different parts of the system (parallel). This makes it ideal for high-performance, enterprise-scale analytics, where large amounts of interconnected data need to be analyzed in real-time.

TigerGraph stores entities as nodes and relationships as edges, mirroring real-world interactions while enabling high-speed multi-hop queries that AI agents can traverse in milliseconds, even across massive datasets. It supports real-time analytics and dynamic pattern discovery, helping AI systems detect changes and make decisions instantly. 

TigerGraph provides the dynamic relational awareness needed for intelligence agents to plan, reason and learn at scale. This makes TigerGraph uniquely positioned to make the most of Agentic AI.

Moving From Traditional AI to Agentic AI

Instead of static machine-learning models that rely on predefined rules and datasets, Agentic AI agents can plan, make decisions, act, and evolve in response to continuous input. This dynamic process defines an agentic workflow as a continuous loop of perception, reasoning, and adaptation that evolves with real-time data.

Thinking ahead and adapting makes Agentic AI more dynamic and capable than traditional AI models. Agentic AI can:

• Perceive: Analyze data streams and detect events and patterns.
• Reason: Make decisions based on relationships and historical trends.
• Act: Execute workflows and dynamically adjust business operations.
• Learn: Continuously refine its understanding through feedback loops.

For context, large language models by themselves are reactive. They respond to queries but cannot self-direct. 

Agentic AI, on the other hand, has a goal in mind. It monitors its performance, makes decisions, and adjusts its workflow. This shift moves enterprises beyond automation. With knowledge graph agentic AI architectures, systems can anticipate change, reason across relationships, and optimize actions proactively.

By integrating Agentic AI with TigerGraph, enterprises unlock unprecedented capabilities in real-time decision-making, adaptive automation, and hyper-personalization. AI can understand and respond to complex relationships in real time, creating smarter, more autonomous enterprise applications. It empowers organizations to build context-aware AI models to navigate and infer insights from rich data networks. 

Integrating TigerGraph with agentic AI is straightforward, connecting facts, decisions, and workflows in a live network:

• Data Ingestion: Structured and unstructured data is mapped into a graph schema.
• Graph Construction: AI agents traverse relationships between entities, decisions, and events.
• Agentic AI Deployment: AI models dynamically infer insights and execute actions.

The graph would provide both foundational knowledge and rules of engagement for Agentic AI. It can encode decision-making logic, allowing AI to follow predefined pathways while adapting dynamically. Agentic AI constantly monitors conditions and optimizes performance, offering maximum real-time adaptability.

The impact on the enterprise would be transformative—it already is.

Transformative Impact on the Enterprise 

AI agents combined with graph databases can seamlessly navigate enterprise workflows, making autonomous, context-aware decisions without human input. By uncovering hidden patterns and deeper relationships within data, these advancements empower businesses to operate with greater intelligence, agility, and automation.

This foundation enables truly agentic AI data-driven decisions—insights that evolve continuously based on live context rather than static datasets.

For example, in logistics, an AI system monitoring shipping routes detects delays and automatically reroutes to minimize disruption. Supply chain optimization also benefits from AI-powered graph analytics, where real-time demand signals help dynamically adjust vendor orders and inventory management. In manufacturing, an energy management AI continuously assesses energy use, optimizes distribution, and adjusts dynamically to changing demands, ensuring operational efficiency.

In customer-facing applications, AI-driven personalization leverages graph-based insights to deliver hyper-personalized recommendations. By analyzing customer interactions across multiple touchpoints and understanding the relationships between purchases, interests, and user networks, AI can refine recommendations with greater accuracy. This capability enhances customer experience, leading to stronger engagement and increased sales.

In customer relationship management (CRM), AI can even predict customer needs by analyzing historical behavior and engagement patterns, allowing businesses to address concerns or offer tailored solutions proactively.

In cybersecurity and IT operations, agentic AI for data management and graph reasoning enable continuous transaction monitoring, user behavior, and access points, detecting anomalies that indicate fraudulent activity or potential system vulnerabilities. By dynamically adapting to evolving threats, AI strengthens enterprise security and reduces risks in real-time.

From logistics to personalization, supply chains to cybersecurity, integrating Agentic AI with graph databases revolutionizes business operations. It allows enterprises to anticipate challenges, optimize processes, and deliver smarter, data-driven decisions at scale.

It’s not without challenges, though. 

Challenges and Considerations

While integrating Agentic AI with graph databases offers significant advantages, it also presents challenges that enterprises must navigate.

One major concern is data privacy and compliance. As AI systems make increasingly autonomous decisions, ensuring that their recommendations align with regulations such as GDPR and industry-specific data protection laws becomes critical. Enterprises must implement strict data governance frameworks to maintain transparency and accountability in AI-driven processes. TigerGraph enhances security with fine-grained access controls, encryption mechanisms, and compliance-ready solutions to help organizations manage sensitive data within a graph database environment. 

Another challenge is system complexity. Managing large-scale graph search and reasoning processes requires sophisticated infrastructure to handle highly interconnected data. Managing agentic AI real-time data streams demands infrastructure that can process updates, context shifts, and model feedback with minimal latency.

As AI models grow in complexity, ensuring efficient query execution and maintaining system performance becomes increasingly difficult. As noted earlier, TigerGraph’s native parallel processing architecture delivers high-speed performance out of the box—so teams don’t need to jury-rig complex workarounds just to meet performance demands.

Scalability is also a key factor. Maintaining speed and accuracy without compromising system efficiency is a constant balancing act. TigerGraph’s distributed computing model ensures scalability by allowing enterprises to scale both vertically and horizontally. 

Beyond these technical challenges, enterprises must also ensure good data quality, eliminate hallucinations in AI decision-making, and properly define AI’s operational boundaries. 

AI-driven insights can become unreliable without robust validation mechanisms, leading to flawed decision-making. Addressing these concerns is crucial to ensuring that AI systems remain powerful, trustworthy, and effective in enterprise environments.

Future Outlook in Graph-powered Agentic AI 

As AI and graph technology continue to evolve, real-time AI-driven graph insights are becoming essential for detecting patterns and anomalies—and making instant decisions. AI agents can continuously analyze graph patterns to identify fraud, security threats, or operational inefficiencies as they emerge, allowing organizations to respond proactively rather than reactively.

Next-generation agentic analytics software with automated data storytelling will visualize these insights—using adaptive dashboards, or even an agentic AI chart, to narrate complex graph results in human-readable form.

Graph provides an understandable way to encode rules and policies for AI—helping balance transparency and control in Agentic AI. The future of AI is not just automation—it’s intelligent decision-making that continuously adapts to real-world conditions. Graph and Agentic AI together make that possible.

Frequently Asked Questions (FAQ)

1. What is Agentic AI?
   Agentic AI is a form of artificial intelligence that perceives, reasons, acts, and learns continuously. It enables systems to make autonomous decisions and adapt to new information in real time.
2. How do graph databases support Agentic AI?
   Graph databases provide the contextual framework Agentic AI needs to reason effectively. They store data as relationships, allowing AI to analyze connections and dependencies instantly.
3. What are the main business benefits of combining Agentic AI and graph databases?
   Together, they enable faster, context-aware insights, dynamic decision-making, and self-optimizing workflows. These are essential for use cases like fraud detection, logistics, and customer personalization.
4. Why is TigerGraph ideal for Agentic AI applications?
   TigerGraph’s native parallel architecture scales to billions of relationships, delivering the real-time analytics and reasoning power Agentic AI needs for enterprise-grade performance.

In Summary:

Enterprises that embrace graph-powered agentic AI will unlock new levels of efficiency, intelligence, and automation—driving the next generation of business applications and shaping the future of AI-driven innovation. Reach out to learn more!

Why Agentic AI Needs More Than Just Rules (It Needs Guardrails) 

Traditional AI models often operate in rigid, rule-based environments, but real-world scenarios demand nuance. Agentic AI doesn’t just follow instructions—it interprets them, reasons through options, and makes choices based on perceived goals. To do that responsibly, it needs more than logic—it needs context.

Graphs provide this context.

Graph databases model not just data points but the relationships between them. A knowledge graph, for instance, can represent everything from company policies and domain-specific regulations to behavioral norms and ethical principles. This allows an agentic AI to reason through decisions in a more human-like, adaptable way.

Self-Driving Cars and Situational Awareness 

Consider a self-driving car: Programming the legal rules of the road—speed limits, stop signs, right-of-way laws—is relatively straightforward. These are hard-coded, rule-based instructions. But driving isn’t just about following laws. It’s about reading the room.

Take the rule, “Yield to pedestrians.” It seems simple. But what happens when a pedestrian is near the curb, scrolling on their phone, making no eye contact? What if the crosswalk light is blinking and traffic is backing up? Is the person about to cross—or just waiting for an Uber?

These are not binary decisions—they require situational judgment. And that’s where traditional logic systems fall short. Behind the curtain, an autonomous vehicle needs to: 

• Ingest real-time data from sensors (e.g., lidar, cameras, GPS) about nearby objects, speed, trajectory, and intent. 
• Link this data to context: Is this a school zone during drop-off hours? Has the same pedestrian crossed here before? Is this behavior consistent with past crossing patterns? 
• Weigh competing priorities: safety, legality, traffic flow, rider experience.

This is where graph comes in. A graph-based system connects these data points as a web of relationships: 

• The pedestrian is a node, linked to attributes like direction of movement, gaze, phone usage. 
• The location is a node tied to school zone status, time-based restrictions, or past accident history. 
• The vehicle state (speed, path, braking time) is part of a larger dynamic network that influences and is influenced by all the above.

With this graph, the AI agent can reason through decisions—not just react. It can say, “I’ve seen this pattern before. In similar conditions, the pedestrian crossed late. There’s a history of near misses at this time. The safest move is to yield.”

That’s not just compliance—it’s judgment. And it’s made possible by a graph that models not just what is, but how everything relates.

In short, graphs give agentic AI the situational awareness to act more like a cautious, adaptive driver—not just one following the letter of the law, but one aligned with the spirit of it.

Building Responsible Autonomy with Graphs 

As AI systems evolve from reactive models to autonomous agents, their ability to operate independently introduces new risks—and new responsibilities. Agentic AI doesn’t just follow a linear set of instructions; it makes decisions, adjusts strategies, and acts over time in dynamic environments. These systems need more than data and logic to ensure that autonomy is exercised responsibly. They need context, norms, and guardrails.

Graph technology provides this scaffolding by modeling relationships—not just between data points, but between rules, entities, behaviors, and outcomes. It’s the connective tissue that allows agentic systems to reason like humans do—not in isolation, but with situational awareness.

With graph-based infrastructure, autonomous systems can:

Align with Organizational Policies 

A graph can represent internal structures such as reporting hierarchies, approval workflows, and business policies. When an AI agent evaluates a course of action—say, escalating a transaction or triggering an alert—it can traverse this graph to understand what’s allowed, who needs to be informed, and under what conditions exceptions apply. This is more than access control—it’s embedded operational intelligence.

Incorporate Domain-Specific Ethics 

Legal boundaries, cultural expectations, and industry regulations vary across contexts. A healthcare AI should respect HIPAA privacy constraints. A financial advisor bot might need to avoid investments that conflict with ESG preferences. Graphs make it possible to encode these domain-specific norms as traversable relationships and dynamic rules—so that AI systems can reason through them, not just check boxes.

Adapt While Staying Accountable 

One of the promises of Agentic AI is its ability to explore new strategies and learn from outcomes. However, this exploration must happen within well-understood limits in enterprise and regulated environments. Graphs provide those limits—not by halting adaptation, but by guiding it. Graphs are how you encode behavioral norms—not just what’s allowed, but what’s expected.

It’s important to note that not all graph systems can handle the scale, depth, or complexity of real-world autonomous decision-making. Agentic AI requires real-time feedback, deep multi-hop reasoning, and policy-aware traversal logic. This is where TigerGraph’s native parallel architecture makes that vision operational.

TigerGraph, for example, supports: 

• Shared-variable logic – for efficient collaborative network exploration and analysis. 
• Streaming graph updates – enabling the system to integrate fresh data without disruption. 
• Native parallelism – meaning it can handle many decisions and data paths simultaneously.

In plain terms: TigerGraph helps agentic AI systems see relationships as they evolve, adapt decisions as new information comes in, and maintain alignment with human expectations at scale.

This is how you move from automation to alignment. From agents that execute code to agents that act with awareness.

The goal isn’t just to prevent catastrophic failure—it’s to build systems people can trust. Whether that’s a compliance officer reviewing a flagged transaction or a customer relying on AI for a financial decision, explainability and accountability are non-negotiable. And graphs—especially when engineered for real-time performance and complex traversals—are uniquely suited to deliver both.

By embedding policies, relationships, and behavioral expectations directly into the AI’s reasoning substrate, graph technology ensures that as autonomy grows, so too, does accountability. To truly empower AI systems with this level of structured, contextual awareness, you need more than a database—you need a knowledge graph.

What Is a Knowledge Graph—And Why Does Agentic AI Need One? 

A knowledge graph is a special type of graph database that doesn’t just store data—it models meaning. It represents entities (like people, accounts, or policies) as nodes and encodes their relationships as edges. But what makes it a “knowledge” graph is how it captures the semantics, rules, and norms behind those connections.

Think of it as a living, contextual map of how your world works—one that: 

• Reflects your organization’s policies, data sources, and workflows. 
• Describes the context and history of key entities, evolving as situations change.
• Enables AI agents to reason in real-time with awareness of both structure and meaning.

For agentic AI, this is essential. A knowledge graph gives the agent a form of memory, policy guidance, and situational understanding. It can answer questions like: “What actions are typical for this scenario?” “Have we seen a pattern like this before?” and “Does this behavior violate any rules, norms, or expectations?”

When built on a platform like TigerGraph—which is purpose-built for deep reasoning, not just speed—this knowledge graph becomes a live, scalable decision layer. 

Its architecture supports the full stack of requirements for explainable, policy-aware AI: from reusable logic (so agents can apply consistent rules across different situations), to streaming updates (so the system can respond to new information in real-time) to parallel traversal (so it can analyze complex relationships across billions of nodes and edges without slowing down). 

In short, TigerGraph enables AI systems not only to act fast—but to act with understanding, consistency, and accountability.

Graphs as the Moral Compass of Machines? 

As AI becomes more agentic—capable of reasoning, planning, and acting independently—the need for explainability, accountability, and contextual awareness only grows. Graphs offer a way forward. By structuring data around entities and their relationships, graph technology provides the contextual backbone that helps autonomous systems navigate complex environments while staying aligned with their intended goals.

This isn’t theoretical. Forward-looking organizations are already pairing knowledge graphs with real-time data and agentic models to build systems that are not only autonomous—but aligned. In financial services, for instance, AI agents must make decisions that comply with regulations, reflect organizational priorities, and stand up to audit—often in real-time.

TigerGraph distinguishes itself here. Unlike general-purpose graph databases, it is engineered specifically for complexity and enterprise-grade performance. Its ability to support deep reasoning, not just fast lookup, is critical when AI needs to explain why—not just what—it did.

Imagine a digital finance advisor that goes beyond optimizing returns to also consider ethical constraints, long-term goals, and shifting market conditions—all while explaining its choices. Or a healthcare assistant that dynamically adjusts recommendations based on clinical history, social context, and emerging research.

That’s not just decision-making—it’s responsible autonomy. And graphs make it possible.

The future of AI won’t be shaped by model size alone—it will be defined by structure, alignment, and trust. Graph provides the scaffolding, and TigerGraph enables it at enterprise scale.

Let’s build AI that doesn’t just act smart—but thinks responsibly. Reach out to learn more and see the next evolution of graph databases in action!

Unleashing AI’s Potential: Why Graph Databases are the Secret Weapon

Artificial intelligence is rapidly transforming industries, but its biggest challenge remains: understanding relationships within data at scale. Traditional databases fall short, but graph databases, especially TigerGraph, bridge this gap, unlocking AI’s full potential 

Graph Databases: The Foundation for Smarter AI

Traditional databases often struggle to represent and analyze the complex connections that exist in real-world data. Graph databases, on the other hand, are designed to excel at this. They model data as nodes (entities) and edges (relationships), allowing AI algorithms to navigate and understand the interconnected nature of information.

Why Graphs are Essential for AI Training and Inferencing:

• Enhanced Understanding: Graph databases provide a richer context for AI models, leading to more accurate and insightful results. By capturing relationships, AI can better understand the “why” behind data patterns.
• Improved Reasoning: AI models trained on graph data can reason more effectively, making them ideal for tasks like fraud detection, recommendation systems, and knowledge graph analysis.
• Agentic AI and Task Workflows: The rise of Agentic AI, where AI agents autonomously perform complex tasks, demands a sophisticated understanding of relationships. Graph databases are essential for managing the workflows and dependencies within these agentic systems. An agent needs to understand the relationships between tasks, resources, and actors, and graphs are perfect for this.

TigerGraph: Supercharging AI with Graph Power

TigerGraph stands out as a leader in the graph database space, offering unique capabilities that empower AI development:

• Blazing-Fast GNN Training and Inference: Graph Neural Networks (GNNs) are a powerful class of AI models that leverage graph data. However, training GNNs at scale has historically been a challenge. Thanks to the collaboration between NVIDIA and TigerGraph, this is no longer the case.

• Significant Speed Improvements: As the only truly scalable graph database TigerGraph + Nvidia GPUs harness deep parallel processing and GPU acceleration to train GNN 200x faster. This breakthrough enables developers to train larger, more complex models in a fraction of the time, unlocking new possibilities for AI at scale.
• GNN at Scale Breakthrough: Prior to the joint Nvidia and TigerGraph development, GNN at scale was a “big problem.” Meaning, scaling to meet time requirements for answers was not possible. The joint effort has created a high-performance, massively scalable GNN architecture that is used for both training and inference.

• Vector as an Attribute: TigerGraph’s ability to store vectors as attributes within its graph database is a game-changer. This allows for seamless integration of vector search and similarity analysis with graph analytics, enabling powerful applications like semantic search and personalized recommendations.
• NVIDIA & TigerGraph high-performance, massively scalable GNN architecture used for both training and inference: This point cannot be overstated, the ability to train and run inference at scale, is a key component to real world applications. Scalability improves not just performance but also prediction accuracy, as richer datasets enable AI models to capture deeper relationships and make more precise predictions.

The Future of AI is Graph-Powered

As AI continues to advance, graph databases will become indispensable for driving deeper intelligence and more accurate predictions. By providing a foundation for understanding complex relationships, graph databases like TigerGraph empower AI to reason, learn and scale like never before. Whether you’re building a fraud detection system, a recommendation engine, or an agentic AI platform, graph databases are the key to unlocking the true potential of your AI initiatives.

In the rapidly evolving landscape of data analytics and artificial intelligence (AI), the recent talk by Dan McCreary, Head of AI at TigerGraph, at the NVIDIA GTC event stands out as a significant milestone. His presentation, titled “Enhanced Data Analytics: Integrating NVIDIA Rapids cuGraph with TigerGraph,” shed light on the critical importance of graph databases in AI and the groundbreaking work TigerGraph is doing in collaboration with NVIDIA. This blog dives into the key insights from Dan’s talk and the implications for the future of AI and data analytics.

The Critical Role of Graph Databases in AI

Dan McCreary kicked off his presentation by emphasizing the crucial role of graph databases in the realm of AI. Graph databases, unlike their relational and non-relational counterparts, are designed to handle highly interconnected data efficiently. This characteristic makes them particularly suited for applications that require the analysis of complex relationships between data points, such as fraud detection in banking—a field where TigerGraph has already marked its prowess with several successful implementations.

Drawing inspiration from Jeff Hawkins’ theories on the brain, as outlined in his books, Dan used a poignant quote to set the stage: “The key to artificial intelligence has always been the representation.” This statement highlights a fundamental challenge in AI: accurately modeling and representing the data in a way that machines can effectively process and learn from.

Navigating the Representation Problem in AI

Dan’s talk delved into the representation problem in AI, a crucial hurdle to achieving more advanced and efficient AI systems. He identified four key types of data representations used in AI today: images, sequences, tables, and graphs. Each of these representations has its domain of applicability and associated challenges, but Dan’s focus was on graph representations due to their ability to model complex relationships and dynamics.

One of the main challenges with graph data is its inherent sparsity and the difficulty of optimizing these representations for hardware. This is where the collaboration between TigerGraph and NVIDIA becomes pivotal. Dan walked the audience through the complexities of dense and sparse matrix representations and discussed the journey towards achieving a fully hardware-optimized graph system.

Leveraging NVIDIA’s RAPIDS cuGraph for Breakthroughs in Performance

The partnership between TigerGraph and NVIDIA has been instrumental in addressing the challenges of graph data analytics. Dan highlighted how TigerGraph is leveraging NVIDIA’s RAPIDS cuGraph libraries to tackle the problems associated with sparse matrix representations. The discussion touched upon the pros and cons of using Python for these tasks but underscored the substantial performance improvements enabled by NVIDIA’s RAPIDS libraries.

A highlight of Dan’s presentation was the demonstration of up to 100x speedups in performance when utilizing NVIDIA GPUs for algorithms like PageRank. This impressive achievement underscores the potential of graph analytics when combined with powerful hardware acceleration, offering a glimpse into the future of AI where graph representations play a central role.

The Synergy Between TigerGraph and NVIDIA: Pioneering the Future of AI Hardware

In closing, Dan McCreary expressed his gratitude towards NVIDIA for their partnership. This collaboration is not just about achieving short-term gains in performance but about jointly paving the way for the next generation of graph-optimized hardware. By combining TigerGraph’s expertise in graph database technology with NVIDIA’s leadership in GPU technology, the two companies are at the forefront of creating solutions that can handle the complexity and scale of tomorrow’s AI challenges.

The significance of Dan McCreary’s talk at NVIDIA GTC extends beyond the technical details of integrating cuGraph with TigerGraph. It represents a pivotal moment in the evolution of AI and data analytics, highlighting the shift towards graph representations as a key enabler of more sophisticated and effective AI systems. As companies increasingly migrate to graph representations to enhance their predictive capabilities, the work being done by TigerGraph and NVIDIA will undoubtedly play a crucial role in shaping the future of AI.

In an era where the ability to analyze and leverage complex relationships in data can provide a competitive edge, the advancements discussed in Dan’s presentation offer exciting possibilities. Whether in detecting banking fraud more accurately or in understanding customer behaviors and product dynamics, the integration of NVIDIA Rapids cuGraph with TigerGraph is setting new benchmarks for what is possible in AI and data analytics.

The journey towards a future where AI can more closely mimic the intricacies of human intelligence and decision-making is fraught with challenges. Yet, with visionaries like Dan McCreary leading the charge and fostering collaborations between industry giants like TigerGraph and NVIDIA, the path forward seems not only clearer but also significantly more promising. As we look ahead, the continued innovation in graph database technology and hardware optimization heralds a new era for AI—one that is more intelligent, efficient, and capable of understanding the complex web of relationships that define our world.

As the Head of Marketing at TigerGraph, I’m thrilled to extend a warm invitation to all enthusiasts, professionals, and curious minds to join us at the upcoming Graph + AI Summit 2024. This event is an absolute must for anyone interested in leveraging the transformative potential of graph technology and artificial intelligence. Here is why: 

1. Exclusive Announcements and Sneak Peeks: As a participant of Graph + AI, you’ll be among the first to hear about our latest product announcements, updates, and future roadmap plans. Get exclusive access to sneak peeks, beta releases, and insider information straight from the source.

2. Great Networking Opportunities: At Graph + AI Summit, you’ll have the chance to connect with the industry leaders, innovators, and experts in the fields of graph databases and artificial intelligence. Rub virtual shoulders with professionals from organizations like Mastercard, KPMG, and JPMorgan Chase & Co, among others. Whether you’re a seasoned professional or just starting your journey, networking with like-minded individuals can open doors to collaborations, partnerships, and invaluable insights.

3. Cutting-Edge Insights and Case Studies: Our event will feature keynote speeches, panel discussions, and workshops led by top thought leaders and practitioners. Gain firsthand knowledge from real-world case studies showcasing how leading organizations are leveraging the synergy between graph databases and AI to drive innovation, solve complex problems, and unlock new opportunities.

4. Exclusive Insights from Industry Visionaries: We are honored to have Hamid Azzawe, TigerGraph’s CEO, to present at the event. With a wealth of experience from Meta, Amazon, Microsoft, Bloomberg, RBC, AMFAM, and IBM, Hamid brings a unique perspective to the table. 

5. Hands-On Workshops and Demos: Explore the practical applications of graph technology and AI through interactive workshops and live demonstrations. Whether you’re interested in building recommendation systems, fraud detection algorithms, or knowledge graphs, our workshops will provide you with the tools, techniques, and best practices you need to succeed.

Graph + AI Summit isn’t just another event—it’s a gathering of passionate individuals united by a common goal: to unlock the full potential of graph technology and artificial intelligence. Join us on this exciting journey of discovery, collaboration, and innovation. We can’t wait to see you there!

Save the Date: May 1-2, 2024 

Location: virtual 

Registration Link 

Many executives are pondering difficult decisions about making large investments in AI. For many of them, their lack of a technical background makes it difficult for them to visualize the impact of AI on their customers, their products, and their employees. To help executives make the right strategic decisions, we need powerful storytelling in terms they can understand and remember.

I have been creating a set of stories and metaphors to guide executives when they need to make strategic decisions about AI investment. After testing, my Jellyfish and Flatworm story has been remarkably effective at helping them guide their peers. I would appreciate feedback from my readers if this story is sticky enough to guide your leaders.

At the core, this story is about why Knowledge Representation (KR) must be the core of any cost-effective long-term AI strategy. We will see how Large-Language Models (LLMs), Knowledge Graphs (KGs), and Reference Frames (RFs) are moving us closer to general AI and how building hybrids of these three knowledge representation strategies is the best path.

At the end of this story, you can start to ask if your organization is more like a jellyfish or a flatworm. Clues about how much you need to invest in AI will be clear. Let’s begin our story.

The Evolution of Animal Intelligence

About 600 million years ago, animals evolved cells that helped them react to environmental changes. Let’s start with the elegant jellyfish. Jellyfish live in the open ocean, far away from complex structures. A jellyfish only needs simple rules to navigate its environment. Jellyfish might move to depths that allow them to capture more prey and avoid their predators. But they are not hunters. They depend on fish drifting into their tentacles.

Jellyfish live in a relatively simple environment and need to be efficient with their energy use. They really don’t need a complex centralized nervous system to help them navigate the ocean. If jellyfish had a big complex brain that required energy, they would quickly starve. To survive, they needed to keep things simple.

In contrast, on the ocean floor, things were getting much more complicated. To seek their prey and avoid predators, animals like flatworms started to develop muscles to help them move around. They also developed more cells on their skin that could process complex signals such as light, temperature, and smell. They used these sensory systems to get detailed information about their environment. And like the jellyfish, they also developed rules to survive. But not all the rules stayed simple. Knowing both what rules to follow and when to follow them became more complex.

Flatworms are thought to be the first hunters.

Movement and the Evolution of the Central Nervous System in Flatworms

Then something really interesting started to happen. Flatworms started to centralize where these rules were executed. Putting them all near their front-facing sensors made sense. We now call that the “head” of our animals. They started to evolve a complex network of centralized nerve cells, which we now call a centralized nervous system or CNS in their heads. These networks of communicating nerve cells evolved to become the brains of animals that move about in the world.

So why did they need to build such complex and energy-consuming cells? The key thing to understand is that movement makes executing rules complicated. Like an anemone, a plant sits in a single location on the ocean floor. It does not need to understand how things change if it moves. But any animal that moves needs to start to learn the structure of its environment. If it turns around 180 degrees, it needs to know that this helps it move away from predators. The bottom line is that we can’t really understand animal intelligence without having a deep appreciation for understanding how intelligence and models of our world are tied to motion and, importantly, maps and structure.

The Evolution World Models in Brains

Let’s explore why storing models of the world around it gave these flatworms a competitive advantage over their siblings. We ask, how can we have more precise ways to know what rules to execute and when to execute these rules in order to survive?

Imagine two flatworms. One that had a precise model of the world around them in their brain, and another that did not have a precise model. As these animals crawled out of their holes to seek their prey, those with a more precise model would remember where the best food was. They could also remember where predators threatened them. You can think of these models as internal maps of a flatworm’s surroundings. They used these models to give themselves a competitive advantage. They had more offspring, and these offspring also built more precise models of the world around them. We call these models “world models” because their structure represents the world around them.

The key point here is that these early nervous systems evolved into many other much more complex systems that have become our brains. Humans out-competed our extinct ancestors because we could model the world and predict how our actions could help us survive and out-compete our rivals. Modeling what is in our prey or predator’s brain can also be very helpful for survival. Does that mammoth think strategically about the consequences of being headed toward a cliff?

In summary, animals have brains that are predictive organs that must model their world and build mental maps of their world. These models advise us on what actions to take to help us survive. They also give us advice on the consequences of we don’t think strategically about the complex systems around us.

Let’s apply what we learned about jellyfish and flatworms to our organization.

Language Models Are *Not* World Models

Now, you might ask, “What does this all have to do with AI?” Much discussion has been about LLMs and how they are used to generate text. But these language models are fundamentally different from the world models in our brains. Let’s consider how they are different.

LLMs are used to predict the next word given a sequence of preceding words. They were never designed to store accurate models of the real world. Language is a collection of symbols we use to describe our world. When we need to communicate ideas between people, we generate sequences of words that fit within patterns called grammar and syntax. Although tools like ChatGPT and Llama 2 are incredibly useful, they were never designed to model the world and simulate the impact that our actions would have on the future states of our worlds.

Don’t get me wrong here. I love my GPT-4! But we must be clear. Modeling language is only a shadow of how we communicate about the world. It really is not a precise model of the world. It can be complemented with actual models of the world, but fundamentally, the knowledge representation distributed through billions of weights in a neural network has severe limitations with precision, reproducibility, truthfulness, performance, and explainability.

Knowledge Graphs *ARE* World Models

Many of my readers know that I have been deeply involved in building large-scale Enterprise Knowledge Graphs (EKGs) for the last six years. Before that, I wrote books on the tradeoffs of using various NoSQL databases. I am a person who can quickly visualize how knowledge is represented in computers, and my goal is to explain the tradeoffs of these alternative representations.

Knowledge graphs are the closest thing we have today to modeling our world in computers. Oh, and by the way, if you pick the right graph database, you can get it to scale out over hundreds of servers. Google, Amazon, LinkedIn (Microsoft), and even Pinterest have proven this for over ten years.

Just like the flatworm needed to model the structure of their environment by building precise maps, knowledge graphs are also the best way for us to manage structure. This can be the structure of our customers, our products, and our competition.

Animal Brains Use Reference Frames

Now, we come to the most interesting fact. Our brains don’t really store data like large-language models or knowledge graphs. We store knowledge in a form that evolved from building maps of our world. These are called Reference Frames and are described clearly in Jeff Hawkin’s book A Thousand Brains. Unlike an LLM, their knowledge can be continually updated. And just like scale-out distributed knowledge graphs, their processing is done in parallel. I won’t go into too much detail on reference frames here but look to innovative companies like Numenta to combine reference frames with LLMs to build new AI systems.

The take-home point is that reference frames can teach us many things about intelligence and how we need to use maps and structure to help us make better predictions. There will be more to come on this topic in future blog posts.

Measuring Complexity In Your Organization

So, should you be building a model of the world in your internal computer systems? Let’s take a look at what a simple company might be.

Imagine you supply a single specialized part to another manufacturer. You are good at what you do and get the same contract every year. You don’t really have any competition. I would describe this company as living in a simple environment, much like the jellyfish living in the open ocean. We can call this company a “jellyfish company.” You can probably model your organization using a spreadsheet or a relational database that uses flat file representation of the world with a few very slow JOIN operations if things get complicated. Your IT department doesn’t need a huge budget.

Now, let’s look at a more complex company. One that has many customers sells many products, and these products each have many competitors. Their structure might look like the following:

You can see that you need a complex model of your world to sell your products in a highly competitive landscape to many types of consumers. You are more like a flatworm company than a jellyfish company. You need complex models that include structure, relationships, precision, explainability, and the ability to add new complexity at will.

How you manufacture and market your products can be dauntingly complex. Can you simulate the impact of a price increase on one of your products? Are you modeling customer behavior? Can you predict the impact of a new marketing campaign? Can you explain why sales of some items are dropping off? If you can’t do this today, it might be that your model of the real world is too simple and too flat without structure. You might need to invest in using a combination of knowledge graphs and LLMs to accelerate your ability to predict the future.

Conclusion

Today, we are seeing unprecedented investments in artificial intelligence. The first wave is mostly investment in tools to make it easier for firms to build intelligent agents that help worker productivity. But all the agent software in the world might not help if your data is trapped in spreadsheets and siloed data. Knowledge needs to be centralized and connected.

Today, jellyfish companies are exceedingly rare. Most companies must deal with rapidly evolving complexity and make precise predictions that require accurate models of the world around them. Companies must focus on building the foundations that will power thousands of intelligent agents working together on centralized knowledge. And remember, going to the cloud will not save you if you have 1,000 silos.

Let me know if this story works for you. Can you tell this story to executives and ask them “are we a jellyfish or a flatworm company”?  Ask them if a centralized knowledge graph would help them answer hard questions about their customer, products and competitors.

If you would like to hear how TigerGraph can help your organization build a centralized nervous system, contact us at info@tigergraph.abstage.xyz. re

In the rapidly evolving world of customer-facing businesses, providing an exceptional omnichannel customer experience has become the key to success. As online retail sales have soared over the last decade, it has become evident that connecting data across various silos is essential for a true omnichannel approach. In this blog post, we will explore how TigerGraph, a powerful graph database platform, is helping large customer-facing businesses create a connected customer platform, enabling them to leverage data effectively, improve customer interactions, and boost profits.

The Challenge of Consolidating Customer Data

Creating a comprehensive and coherent dataset that integrates everything known about customers, their purchasing behavior, and service usage is the foundation of a connected customer platform. However, consolidating these datasets is often a daunting task, and many businesses have struggled to achieve it successfully.

Retailers often face the challenge of dealing with messy customer data, multiple accounts for a single customer, and inconsistencies when they have grown through acquisitions. Moreover, purchase decisions are made at the customer or household level, but the data is often at a device or account level, leading to potential inaccuracies in models and insights.

The Power of Graph Databases

While traditional databases have failed to effectively connect data across silos, graph databases have emerged as a game-changer. Unlike traditional tabular databases, graph databases work on networks of connected data, allowing businesses to structure their databases as vast networks of customer-related information.

Graph databases offer several advantages in consolidating data, including:

• Flexibility: Graphs don’t impose a single structure on all records, enabling connections between data from different silos, even with inconsistent structures and many-to-many relationships.
• Graph Entity Resolution: Using this technique, the graph can quickly identify likely matches between data from different systems, resolving them through fuzzy matching, address standardization, and machine learning.

TigerGraph: Transforming Customer Data and Driving Omnichannel Profits

TigerGraph has emerged as a leading graph database platform, delivering unparalleled performance and scalability. Its ability to handle real-life retail and banking datasets up to 30 times larger than its closest competitor and its remarkable speed, up to 1000 times faster, make it a perfect fit for large customer-facing businesses. A case in point of successful utilization of TigerGraph is demonstrated by these two enterprises, showcasing how they have effectively leveraged its capabilities.

1. Multichannel Retailer: By leveraging TigerGraph, a large multichannel retailer was able to bring together data from five legacy acquisitions and connect family units of customers using multiple devices, payment cards, and addresses. This allowed them to market consistently across all customer touch points and resulted in a 17% increase in customer engagement.
2. Global Media Conglomerate: Another success story involves a global multichannel media conglomerate that merged data from 15 independent divisions to create the first and largest identity graph in the advertising industry. This enabled them to target audiences with personalized commercials aligned with their interests, leading to improved advertising performance.

The importance of a connected customer platform cannot be underestimated in today’s customer-centric business landscape. TigerGraph is empowering large customer-facing businesses to consolidate data across silos, improve customer interactions, and drive omnichannel profits. As the platform continues to gain recognition and accolades, it remains a valuable asset for businesses seeking to deliver a true omnichannel customer experience.

If you’re interested in exploring how TigerGraph can transform your customer data and drive profits in your business, you can sign up for a free instance of TigerGraph Cloud at tgcloud.io or contact us at info@tigergraph.abstage.xyz.