By Adam Slack, Head of Engineering at Nimble Approach
As organisations invest in AI, one challenge is becoming increasingly apparent: providing access to information is only part of the solution. For AI to make reliable decisions, it also needs to understand the context surrounding that information.
Knowledge graphs have long been used to connect data and describe relationships between people, systems and concepts. They provide an excellent foundation for finding and linking information across an organisation. Agent memory builds on this by allowing AI systems to retain information between interactions, creating continuity over time.
However, enterprise AI often requires something more.
Context graphs focus on capturing the relationships that influence business decisions. They model not only what exists, but how work happens, who is involved, which processes apply, and why certain decisions are made. This additional layer of understanding enables AI agents to interpret information in ways that align with organisational goals rather than simply retrieving facts.
This distinction becomes increasingly important as organisations move towards agentic AI. AI agents are expected to reason, collaborate, and complete complex tasks across multiple business systems. Without sufficient context, they risk producing inconsistent recommendations or acting without understanding the wider business implications.
Rather than replacing knowledge graphs or agent memory, context graphs complement them. Together, they create a richer foundation that allows AI to operate with greater confidence, consistency, and explainability.
As enterprise AI adoption grows, organisations that invest in contextual understanding will be better positioned to build intelligent systems that deliver meaningful business outcomes rather than isolated automation.
Read the full article at think.nimbleapproach.com
