Traditional RAG isn’t enough: How to close the AI context gap with agentic RAG
A guide to using agentic retrieval to unearth the full context within unstructured enterprise data to drive trustworthy AI
How the AI context gap undermines reliable outcomes
As AI adoption accelerates, one problem continues to undermine outcomes: AI systems don’t have the context needed to be trustworthy. Most business knowledge—the real source of truth behind decisions—lies dormant in unstructured documents, tools, messages, systems of record and years of operational nuance. Traditional RAG has limitations that keep it from understanding this knowledge or applying it with the depth and accuracy real workflows require.
This gap—call it the AI context gap—is a growing disconnect between the knowledge organizations have and the knowledge their AI systems can use. This AI context gap is why AI answers or agentic search often feel shallow and are inconsistent or incomplete, and why teams struggle to move from promising demos to reliable production use. It’s why context engineering—determining what information the model should consider and how that information should be assembled—has emerged as a critical discipline in today’s market conversations around AI.
Of CDOs surveyed by the IBM Institute for Business Value, only 26% are confident their organization can use unstructured data to deliver business value while 82% say they’re hiring for new gen AI-related data roles.1
Closing the gap
Closing the AI context gap requires enterprises to tap into their unstructured business knowledge, interpret it, and apply it with intent—regardless of company size or AI maturity. That requires more than vector search or chunking. It requires agentic retrieval that understands intent, navigates diverse sources and assembles the right context—supported by a governed, open architecture that ensures accuracy, trust and operational scale.
Organizations at any stage of their AI journey can build agents that truly understand their business. Building such an AI requires a context‑first retrieval approach—one that unifies, enriches and assigns meaning to business knowledge, enabling AI systems to deliver reliable, scalable outcomes beyond isolated pilots.
“Context is the combination of the data itself, the meaning of that data, the relationships between different data elements, the history of how that data has been produced and transformed, and the rules that govern how it can be used.”²
Inferior accuracy
Traditional RAG uses rigid, single-pattern retrieval so it can’t adjust to various question types or complexity levels, yielding unreliable AI responses.
Use intent‑aware, adaptive retrieval to interpret the question, select the right retrieval strategy and bring context that’s relevant and complete.
More accurate, dependable AI responses that improve confidence in using AI for everyday decisions.
Fragmented data ecosystem
Business knowledge across data sources, tools, systems and formats yields a fragmented ecosystem that limits how AI can access and use information.
Adopt an open, composable architecture that connects to knowledge where it resides and enables integration and reuse across tools and data sources.
Faster, more flexible integration and the ability to scale AI across the organization without being constrained by existing systems or vendor lock‑in.
Lack of scalability
AI prototypes often work well in pilots but fail in real environments. Missing governance, security and operational controls prevent AI from scaling.
Provide an enterprise-ready foundation with built-in governance, security and hybrid deployment capabilities so that AI scales from pilot to wide adoption.
A reliable, production‑grade AI environment that supports consistent performance and compliance and broader organizational rollout.
Traditional RAG versus agentic RAG
| Traditional RAG | Agentic RAG |
| Homogeneity: Uses a single, fixed retrieval pattern for every query, regardless of intent or complexity | Personalization: Adapts retrieval strategies based on user intent, question structure and context, selecting the most effective approach for each query |
| Lack of context: Relies primarily on semantic similarity, which often retrieves content that looks related but lacks the precise context needed for accurate answers | Highly contextual: Combines multiple retrieval methods—semantic, keyword, metadata and structured filtering—to assemble more relevant and contextually correct information |
| Relationships lost: Retrieves information in isolated chunks, causing important relationships across documents or sections to be lost | Relationships retained: Reconstructs relationships across documents and content sources using multistep reasoning before generating an answer |
| No validation: Works as a one‑shot retrieval process with no mechanism to refine or validate results when the initial retrieval is incomplete | Iterative validation: Applies iterative validation loops, requerying and refining retrieval when needed to improve accuracy |
| Constant manual tuning: Requires constant manual tuning and rule adjustments to maintain accuracy as questions and content evolve | Reduced manual tuning: Reduces the need for manual tuning by dynamically adjusting retrieval logic as use cases and queries evolve |
A retrieval framework to close the AI context gap
OpenRAG is an agentic retrieval framework developed by IBM that, when run on IBM® watsonx.data®, provides the modern retrieval architecture needed to close the AI context gap. To run agentic RAG reliably in enterprise environments, teams need a governed, open retrieval foundation. Watsonx.data operationalizes this agentic, multistep retrieval process at enterprise scale with governance, security and hybrid flexibility capabilities built in.
Composition
OpenRAG is built on an open, composable architecture that brings together 3 open‑source technologies, each addressing a critical part of context‑aware retrieval to support accurate, adaptable, production‑ready AI outcomes.
Prepares unstructured enterprise data by turning complex documents into AI‑ready knowledge
Provides hybrid keyword and semantic retrieval to surface the right context at the right time
Orchestrates agentic workflows so that retrieval, reasoning and refinement happen dynamically instead of through fixed pipelines
Capabilities
OpenRAG transforms complex files such as PDFs, images, contracts, presentations, spreadsheets and manuals into clean, structured, AI-readable data that provides context for AI.
OpenRAG connects to knowledge wherever it resides, whether in watsonx.data, cloud storage—Drive, OneDrive, SharePoint or S3—or internal systems without centralizing or duplicating data. Retrieval becomes part of the reasoning process: the agent decides when to search, what to retrieve, what tools to call and when refinement is needed.
OpenRAG combines full‑text keyword retrieval, vector similarity search and metadata filtering into a unified scoring model. This combination of capabilities ensures that AI retrieves information that’s actually relevant, not just semantically similar, thereby improving grounding and reducing hallucinations.
Watsonx.data provides identity and access control, encryption, lineage, policy enforcement, audit logging and hybrid and multicloud deployment options. These capabilities give organizations the confidence to run high-throughput, long-running AI workloads secured and at scale.
IT service and support
Computer services, electronics and retail companies with complex IT operations have siloed knowledge—increasing resolution times and operational risk. Agentic RAG “reasons” across operations to deliver accurate, actionable guidance, reducing mean time to resolve and improving IT productivity.
Customer support knowledge access
Customer support teams in wholesale distribution and services, retail and financial services struggle because vital information spans siloed systems. AI assembles the right context for each customer issue, improving response accuracy, first-contact resolution and the customer experience.
Compliance, risk and regulatory knowledge
Financial services firms and others in regulated sectors can introduce compliance risk, interpreting complex policies and regulations without context. Agentic RAG “reasons” across regulatory documentation to deliver explainable, governed responses aligned with applicable requirements.
Procurement and vendor management
Procurement teams in supplier-driven companies lack a unified view of contracts, pricing, internal policies and more—slowing decisions and upping risk. AI retrieves and connects vendor and contract context across systems, enabling faster negotiations and more consistent decision‑making.
Sales and revenue operations
B2B sales teams working in electronics, wholesale and other sectors struggle to get true product, pricing and policy information from CRM and internal systems. Agentic RAG delivers trusted commercial context in real time, improving seller productivity and customer interactions.
HR and workforce inquiry resolution
HR teams in companies with distributed workforces field high volumes of employee queries on policies, benefits and more, giving inconsistent answers. AI retrieves the correct policy context and exceptions dynamically, reducing manual workload and improving employee experience.
Enterprise‑wide knowledge retrieval
In multisystem businesses across industries, critical knowledge spans functions, systems and formats, limiting the reliability of AI efforts at scale. A shared, context-first agentic retrieval foundation enables consistent, context‑aware AI across workflows and teams.
Strategies to close the AI context gap
- Start with one workflow where unreliable answers are hurting productivity—service desks, customer support or anywhere teams struggle to find the right information.
- Invest in context engineering—defining what information matters, how it relates and how it should be interpreted.
- Establish governance—access policies, auditability and trust controls—before you think about retooling.
- Keep your architecture open and composable so that your data sovereignty and retrieval needs can evolve.
- Expand domain by domain, reusing the context signals, governance patterns and retrieval logic that have already worked.
Footnotes
1. The 20215 CDO Study: The AI multiplier effect, IBM Institute for Business Value, November 2025.
2. Context Clues #1: In the Beginning, There Was Context, Dima Spivak, Vice President for Product, IBM, LinkedIn post, 23 March 2026.