**The Limitations of Information Graphs**
As AI continues to advance, Information Graphs (KGs) have become a fundamental building block for representing information in a machine-readable format. By using triples – a head entity, a relation, and a tail entity – KGs create a graph-like structure where entities are nodes and relationships are edges. This allows machines to understand and reason over linked information, supporting applications like query answering, semantic analysis, and recommendation systems.
Despite their effectiveness, KGs have some notable limitations. One of the biggest issues is that they often lose essential contextual information, making it difficult to capture the complexity and richness of real-world information. Additionally, many KGs suffer from information sparsity, where entities and relationships are incomplete or poorly linked. This lack of full annotation limits the contextual indicators available during inference, posing challenges for efficient reasoning, even when combined with large language models.
**Context Graphs: The Next Step**
To overcome these limitations, Context Graphs (CGs) were developed. Instead of storing information as isolated data, CGs capture the situation in which a truth or decision occurred, resulting in a clearer and more accurate understanding of real-world information. This includes additional details such as time, location, and source information.
When used with agent-based systems, CGs also store how decisions were made. Agents need more than just rules – they should understand how rules were applied, when exceptions were allowed, who authorized decisions, and how conflicts were handled. By saving decision trails, CGs help agents learn from previous actions, allowing programs to know not only what occurred but also why it occurred, making agent behavior more consistent and reliable.
**The Power of Contextual Information**
Contextual information provides a crucial layer to information representation by going beyond simple entities-relation information. It helps distinguish between information that appears related but occurs under different circumstances, such as variations in time, location, scale, or surrounding circumstances. For instance, two companies could be rivals in one market or time period but not in another. By capturing such context, programs can represent information in a more detailed manner and avoid treating all similar-looking information as equivalent.
In CGs, contextual information plays a key role in reasoning and decision-making. It includes indicators such as historical decisions, policies applied, exceptions granted, approvals involved, and related events from other systems. By documenting how a decision was made – what information was used, which rule was checked, and why an exception was allowed – this information becomes reusable context for future decisions. Over time, these data help connect entities that are not directly linked and allow programs to reason based on previous outcomes and precedents, rather than relying solely on fixed rules or isolated triples.
**Real-World Examples**
The shift towards Context Graphs is already visible in various applications. For instance, Google’s Gemini and Gemini 3-based agent frameworks are providing AI that can shift from simple assistance to active decision-making. OpenAI’s ChatGPT Health brings health information from various sources into one place, creating a clear, shared context that helps the system understand health patterns over time. JP Morgan’s Proxy IQ is changing proxy advisors with an AI device that combines and analyzes voting information across hundreds of conferences. NVIDIA’s NeMo Agent Toolkit is helping turn AI agents into production-ready systems by including observability, analysis, and deployment controls.
These examples highlight a broader shift towards AI systems that retain state, history, and context, which is essential for reliable, large-scale deployment. As we move forward, it’s crucial to recognize the limitations of traditional Information Graphs and adopt Context Graphs to capture the complexity and richness of real-world information.
