The race to deploy AI agents is accelerating, but most organizations are still building on sand. A new Gartner report suggests that the key to building reliable AI agents is a “context layer”.
According to Gartner’s latest research, 42% of enterprises plan to deploy AI agents by the end of 2026, and AI agent spending is expected to grow from 22% to 31% of total AI budgets in just one year1. Despite this wave of investment, only one in five organizations report that their GenAI tools are delivering significant value. Hallucinations, limited impact, and unpredictable behavior remain stubbornly common.
The problem, Gartner argues, isn’t the models, but rather what surrounds them.
The Missing Layer
Behind every reliable AI agent is something Gartner now calls a “context layer”— a dedicated architectural component that curates, organizes, and delivers the knowledge an agent needs to act intelligently. Without it, agents are left processing noisy, poorly prioritized data, making expensive errors and producing outputs that can’t be trusted or traced.
Gartner is unambiguous about the stakes: by 2027, organizations that prioritize semantics in AI-ready data could increase their agentic AI accuracy by up to 80% and reduce costs by up to 60%. The context layer is no longer an optional refinement — it is the necessary foundation.
And yet this layer cannot simply be purchased. No vendor offers it out of the box. It must be engineered, assembled from services, capabilities, and custom modeling that together transform an organization’s tacit knowledge into something AI agents can actually use.
Three Components, One Foundation
As stated in the report, there are three interlocking components that make up this ‘context’ layer: semantics, operational state, and provenance. Together, they form a pipeline that allows agents to retrieve the right information, organize it coherently, and act on it with accountability.
Semantics: Meaning, Not Just Data
Semantics is the component most organizations are missing, despite it being the one with the greatest leverage. Gartner finds that organizations implementing semantic modelling such as ontologies and knowledge graphs, are 2.2 times more likely to achieve high effectiveness in AI data engineering, however, only 40% of organizations have done so.
Semantics means representing your organization’s knowledge—business entities, rules, policies, relationships, metrics—in machine-readable form. This allows AI agents to interpret what something means in context and execute an action based on that context, not just pattern-match on keywords. Without this layer, even the most sophisticated agent is, in effect, guessing.
This is precisely the domain where metaphactory brings long-standing proven capability. metaphactory by metaphacts, a Digital Science solution, is a knowledge graph platform enabling organizations to build and maintain rich semantic models for over a decade—connecting business glossaries, ontologies, and data products in ways that AI agents can directly leverage. For organizations serious about agentic AI, a robust semantic foundation isn’t a future aspiration; it is a prerequisite.
While semantics provides meaning, your operational state provides situational awareness. AI agents need access to current, accurate information about the entities and processes they’re acting on beyond just snapshots, such as up-to-date information on customers, datasets, experiments, publications and suppliers.
For research-intensive organizations, this is particularly acute. The ‘operational state’ of a research environment spans live datasets, ongoing experiments, researcher expertise, institutional repositories, and the evolving landscape of published science. Digital Science’s portfolio—including Dimensions, Altmetric, and Figshare—represents exactly this kind of curated, continuously updated operational knowledge. Rather than building this knowledge from scratch, organizations working in research and innovation already have access to a pre-assembled foundation.
Gartner also highlights the Model Context Protocol (MCP) as the emerging standard for connecting agents to operational state efficiently and securely. Dimensions, Altmetric, and metaphactory already support MCP, reflecting a broader conviction that research infrastructure should be designed to meet agents where they are, not retrofitted after the fact. As adoption of the protocol grows across the industry, having well-structured knowledge accessible through it will matter more, not less.
Provenance: Trust Through Traceability
The third component—provenance—is what makes agentic AI governable. It encompasses the systematic tracking of data lineage, agent decisions, actions, outcomes, and feedback across the full lifecycle of AI operations.
For research organizations, publishers, and funders, provenance isn’t merely a governance checkbox. It is central to the integrity of the work itself. Reproducibility, accountability, and the ability to audit AI-assisted conclusions are not simply peripheral concerns; they are defining ones. Gartner notes that 74% of organizations recognize that data governance tools are essential to operationalizing AI governance, yet robust provenance mechanisms remain rare in practice.
Digital Science’s longstanding commitment to open, traceable research infrastructure, including persistent identifiers, transparent data lineage and open metadata, gives research organizations a natural head start on this component. The challenge is connecting these capabilities explicitly into the agentic architecture, so that every AI-assisted decision can be traced back to its sources and reviewed.
Research Intelligence as a Context Layer
There is a broader framing worth making explicit here: for organizations operating in research, science, and innovation, the context layer is not merely a technical architecture problem. It is, at its core, a research intelligence problem.
The tacit knowledge Gartner describes—the organizational understanding that must be made machine-readable for AI agents to function—is, in a research context, the accumulated intelligence of a scientific community: what has been discovered, by whom, with what methods, validated how, and applied where.
We have spent over a decade building infrastructure that captures precisely this kind of knowledge at scale. The shift to agentic AI doesn’t make that infrastructure less relevant—it makes it more so. The question is no longer just “can researchers find the right information?” but “can AI agents, acting on researchers’ behalf, find, interpret, and act on that information reliably and accountably?”
The answer depends entirely on the quality of the context layer underneath.
What This Means in Practice
For R&D leaders and data and analytics leaders, the practical implication is this: before asking which AI agent to deploy, ask what context layer you have in place to support it. Gartner’s advice is to start with high-value use cases rather than attempting a comprehensive build all at once—iterate, demonstrate outcomes, and expand. That is sound counsel. But iteration without a semantic foundation, without right-time data access, and without provenance mechanisms will simply produce faster failures.
The organizations that will lead in agentic AI are not those that move fastest to deploy agents. It is the organizations that invest earliest in the knowledge infrastructure that make agents worth deploying.
Digital Science is working with research organizations and data-intensive enterprises to build the context layers their AI strategies require.
- Gartner. (2026). The 3 core components of the context layer for AI agents. [Research Note/Report]. https://www.gartner.com/document/ [G00848874]
The post Why Your AI Agents Are Only as Good as the Knowledge Behind Them appeared first on Digital Science.
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