According to Gartner, organizations that build AI agents on a dedicated context layer that combines semantics, operational state, and provenance can improve trust, reduce costs, and increase business value. The company predicts that organizations that prioritize AI-enabled semantic data could improve agent AI accuracy by up to 80% and reduce costs by up to 60% by 2027.
As AI agents move from experimentation to enterprise deployment, many organizations are realizing that scaling the technology requires more than deploying large-scale language models. According to Gartner, much of the missing piece is a dedicated context layer that allows AI agents to understand business knowledge, access up-to-date information, and track every decision.
“The context layer is the foundation of AI success.” Gartner says.. “D&A leaders must prioritize developing a robust context layer to provide AI agents with the knowledge they need to make consistently reliable, cost-effective, and context-relevant decisions.”
The research firm argues that organizations that invest heavily in agent AI continue to suffer from inconsistent business outcomes because AI systems operate without a structured foundation of contextual knowledge. As enterprises increase spending on AI agents, Gartner says data and analytics leaders should focus more on the architecture that supports the models rather than the models themselves.
This recommendation comes as enterprises accelerate their adoption of AI agents. According to Gartner’s 2026 CIO and Technology Executive Survey42% of organizations expect to deploy AI agents by the end of 2026. At the same time, the company’s latest 2025 Data Value Realization Study predicts that AI agent spending will increase from an average of 22% of annual AI investment in 2025 to 31% in 2026.
Despite this momentum, organizations continue to Struggling to generate tangible benefits from generative AI initiatives. Gartner reports that only one in five companies are seeing significant business value from these initiatives, and one in eight believe the technology is unlikely to meet expectations. Limited business impact and illusions remain the main obstacles.
Build context instead of buying it
Gartner explains the context layer It serves as a dedicated architectural component that collects, integrates, and provides the knowledge that AI agents need to interpret information, make decisions, and perform multi-step tasks that align with business goals. Rather than functioning as a standalone software product, the context layer combines services, governance capabilities, and custom data modeling to transform organizational knowledge into machine-readable information.
The company warns that no vendor currently offers a complete out-of-the-box solution. Instead, organizations should expect to integrate internally developed capabilities and commercial technologies to align with their business processes.
This distinction is becoming increasingly important as organizations look to deploy customized AI agents rather than relying solely on pre-built assistants. Commercial AI agents typically have limited support for external context layers, but Gartner expects broader compatibility to emerge as context engineering becomes more widely adopted.
The company also recommends avoiding large-scale implementation projects. Instead, organizations should start with high-value business use cases before scaling their architecture based on measurable outcomes.
Three components that define the context layer
Gartner identifies three fundamental components that enable reliable agent AI: semantics, operational state, and provenance.
Semantics enables AI agents to understand business meaning rather than simply processing keywords or isolated datasets. This includes business ontologies, knowledge graphs, metadata, policies, business rules, and standardized metrics that help agents consistently interpret organizational information.
Rather than pursuing large, centralized projects, it is essential to integrate semantic modeling across business domains. Organizations should also integrate business glossaries into knowledge graphs and express compliance policies in a machine-readable format to improve governance and consistency.
Gartner predicts that by 2027, organizations that prioritize AI-enabled data semantics will be able to improve agent AI accuracy by up to 80% while reducing costs by up to 60%. According to the company, organizations that have adopted semantic technologies such as taxonomies and ontologies are up to 2.2 times more likely to have highly effective data engineering practices for AI, but only 44% have implemented them.
The second component, operational state, provides situational awareness by connecting AI agents to current business information. Rather than relying solely on historical datasets, agents access operational data at the right time from business systems, event-driven analytics platforms, APIs, and search extension generation frameworks.
Gartner recommends leveraging emerging standards such as Model Context Protocol (MCP) to provide secure and efficient access to enterprise data while ensuring the underlying information is structured to be AI-ready.
The report notes that only 30% of data and analytics solutions use real-time streaming and event-driven analytics, even though real-time analytics can generate up to 35% more business value.
The third component source focuses on transparency and accountability. Provenance allows organizations to track data lineage, AI decisions, results, and feedback throughout the agent lifecycle, creating an audit trail that supports governance, regulatory compliance, and continuous improvement.
According to Gartner, organizations should establish a formal process to regularly review AI results and use those insights to improve the rules, knowledge, and data provided to AI agents over time. The report adds that 74% of organizations surveyed recognize that data governance tools play a critical role in operationalizing AI governance, reinforcing the need for mechanisms to monitor and document AI operations.
Context engineering becomes a differentiator
In addition to defining the architecture itself, Gartner positions the context layer as the foundation for context engineering. Context engineering is a field focused on providing AI models with the most relevant information while minimizing unnecessary data.
As enterprise knowledge becomes increasingly fragmented between structured and unstructured repositories, organizations are challenged to help AI agents find, filter, and prioritize the information needed for each task.
The context layer addresses this challenge through a three-step process of capturing relevant information, organizing it into a consistent business context, and selecting only the highest priority knowledge before presenting it to the AI model.
For data and analytics leaders, competitive advantage comes not just from deploying more AI agents, but also from building a contextual infrastructure that enables those agents to make reliable, explainable, and business-aligned decisions at scale.
