Enterprise Context Management for AI Agents: Architecture & Patterns

Most enterprise AI projects don't fail because of bad models. They fail because the model never gets the right information at the right time. You can have the best reasoning engine in the world, but if it can't reliably access what "customer churn" means across your CRM, billing system, and support tickets, it's just an expensive guessing machine.

Context management is the infrastructure layer that makes AI agents useful in production. It's the difference between a chatbot that hallucinates customer details and an agent that can actually resolve a billing dispute by understanding the relationships between subscriptions, invoices, and support history across multiple systems.

What is Enterprise Context Management?

Beyond Prompt Engineering—Context as Infrastructure

Prompt engineering gets you through demos. Context management gets you through production.

Context management is the organization-wide capability to reliably deliver relevant data to AI context windows, combining structured metadata like schemas and lineage with unstructured knowledge like documentation and business definitions. It's not about writing better prompts—it's about building systems that surface the right entities, relationships, and business logic when agents need them.

Think of it as the semantic plumbing beneath your AI layer. Where prompt engineering asks "how do I phrase this request?", context management asks "how do I ensure every agent in my organization understands what a 'customer' is and how it relates to accounts, contracts, and usage?"

The Context Crisis in Enterprise AI

The numbers are sobering. Gartner predicts that by 2027, almost half of agentic AI projects will be canceled due to inadequate context delivery systems. The pattern is consistent: organizations rush to deploy agents without the infrastructure to feed them reliable context, and the agents either stall waiting for information or confidently hallucinate answers.

Poor data quality already costs organizations an average of $12.9 million annually, with 94% of businesses suspecting their customer data is inaccurate. When you put AI agents on top of that foundation, the costs multiply. An agent that merges the wrong customer records or misunderstands contract terms doesn't just create a bad dashboard—it takes automated actions that compound the error.

Without systematic context management, agents either operate in isolation with incomplete information or share bloated context that creates massive KV-cache penalties in multi-agent systems.

Key Components of Context Management Systems

Enterprise context management rests on four architectural pillars that work together to deliver reliable, meaningful data to AI systems.

Ontologies provide formal semantic data models defining concepts, properties, relationships, and constraints. They're generalized data models that specify types of things that exist in a domain and the properties used to describe them, creating shared vocabulary across systems.

Entity resolution identifies and merges records representing the same real-world entity across disparate systems. According to Gartner, there's a growing trend of clients beginning their MDM journey with entity resolution as the critical first step before constructing reliable master data.

Semantic layers translate technical data structures into business concepts, providing a user-friendly interface that converts raw data into meaningful business terms. They ensure consistency in data interpretation across both human users and AI systems.

Metadata management captures, inventories, and categorizes metadata so organizations can empower users and agents to search for, discover, and govern data. Active metadata management modernizes this through automation and continuous monitoring, providing proactive governance.

Knowledge Graphs as Context Foundations

Why Knowledge Graphs for AI Agents

Knowledge graphs serve as a network of interconnected facts facilitating state-based reasoning and task automation for agents. Unlike tables or documents, they explicitly model relationships, making dependencies and business logic visible to AI systems.

Consider a billing dispute. A relational database shows you invoice records. A knowledge graph shows you that Invoice_12345 is part of Subscription_ABC, which belongs to Customer_XYZ, who has an open Support_Ticket_789 about unexpected charges, and that subscription was modified by Sales_Rep_456 three days before the charge appeared. This structured context gives LLMs a deep understanding of specific business relationships, dependencies, and constraints.

Knowledge graphs prevent the context pollution that plagues document-based approaches. When every piece of information is explicitly typed and connected, agents can traverse relationships precisely rather than hoping semantic similarity surfaces the right chunks.

Semantic Knowledge Graph Architecture

Semantic knowledge graphs extend traditional knowledge graphs by explicitly linking instance data to an ontology, making both entities and their relationships machine-interpretable. This two-layer structure separates what exists from what things mean.

At the instance level, the graph captures real-world facts: specific customers, actual invoices, individual support tickets. At the ontology level, it defines meaning: what makes something a "customer," what properties customers must have, what relationships are valid between customers and subscriptions.

This separation enables concept-level reasoning. An agent can query "show me all high-value customers with payment issues" because the ontology defines "high-value" as a computed property based on revenue and tenure, and "payment issues" as a relationship pattern between customers, invoices, and support tickets. The semantic layer contextualizes data, embedding meaning through formal naming and definition of elements.

Real-time vs. Batch Knowledge Graph Construction

Glean built a real-time crawler architecture that continuously ingests enterprise content and metadata, powering both semantic and lexical search. Their personal graph captures employee activity to understand what individuals are working on, enabling proactive assistance like surfacing priorities and highlighting conflicts.

Traditional batch ETL approaches update knowledge graphs on schedules—nightly, hourly, or at best every few minutes. For many use cases, this latency is acceptable. But for agents handling customer interactions or operational decisions, stale context means wrong decisions.

The tradeoff is complexity. Real-time systems require change data capture, event streaming, and incremental graph updates. Batch systems are simpler to build and reason about. Most organizations need a hybrid: real-time updates for high-velocity operational data, batch processing for analytical datasets and historical context.

Ontology Modeling and Semantic Layers

Ontologies—Formal Models for Shared Understanding

An ontology is a formal specification that provides shareable and reusable knowledge representation, including descriptions of concepts, properties, relationships, constraints, and individuals. They're the schema that makes knowledge graphs interpretable.

The two main uses of ontologies in enterprise systems are interoperability and inferencing. Interoperability means sharing data according to shared vocabulary, so when one system says "customer" and another says "account," the ontology maps them to the same concept. Inferencing means deriving new knowledge from existing data through logical reasoning.

Without ontologies, you're back to tribal knowledge. Someone needs to remember that "active customer" in the CRM means something different than "active customer" in the billing system, and agents need to be explicitly told these differences in every prompt. With ontologies, these definitions live in infrastructure where both humans and AI can reference them.

Semantic Layer Architectures

Organizations implement semantic layers in three main patterns, each with different tradeoffs for governance and flexibility.

Decentralized architecture results in multiple system-level semantic layers, where CMS, CRM, and BI dashboards each manage their own semantic components. This is the default state for most organizations—fast to build, impossible to maintain consistency across systems.

Centralized semantic layer architecture serves as the authoritative source for shared data definitions within an enterprise data warehouse or data lake. Everything flows through one semantic model, ensuring consistency but creating a bottleneck for changes.

Federated approaches blend both: domain-specific semantic layers with a coordination mechanism ensuring critical concepts stay aligned. This matches how modern organizations actually operate, with domain teams owning their semantics but coordinating on shared entities like customers, products, and transactions.

W3C Standards for Interoperability

RDF (Resource Description Framework) represents data in a graph format where entities are described using triples: subject, predicate, object. Every fact becomes "Customer_123 has_subscription Subscription_ABC" rather than a row in a table.

RDFS (RDF Schema) extends RDF with basic vocabulary and structure, allowing definition of classes and properties. OWL (Web Ontology Language) adds richer relationships and reasoning capabilities, enabling systems to infer new facts from existing ones.

SPARQL provides a query language for RDF data, letting you ask questions like "find all customers who have overdue invoices and no recent support interactions" across federated knowledge graphs. These standards matter because they enable cross-system knowledge sharing without custom integration code for every pair of systems.

Entity Resolution and Data Unification

The Entity Resolution Challenge

Entity resolution identifies, matches, and merges records that correspond to the same entity across disparate systems. It's the unglamorous work that makes everything else possible.

Your CRM has "Acme Corp" with email domain "acme.com". Your billing system has "ACME Corporation" with domain "acmecorp.com". Your support system has three different accounts because three different employees signed up. Without entity resolution, your AI agent sees three unrelated companies and can't reason about the full customer relationship.

The challenge scales exponentially with data volume and variety. Different naming conventions, typos, abbreviations, merged companies, acquired subsidiaries—every variation creates another record that might or might not represent the same entity.

Deterministic vs. Probabilistic Matching

Deterministic matching uses exact matches based on rules: if the tax ID matches, it's the same company. Fast, explainable, but brittle. A single typo breaks the match.

Probabilistic matching uses statistical models to calculate match likelihood based on multiple attributes. If the company name is 90% similar, the domain is 80% similar, and the address is 70% similar, the combined probability suggests they're the same entity. More flexible, but harder to explain why two records merged.

Machine learning approaches learn matching patterns from labeled examples, adapting to your specific data quirks. They handle the messiest cases but require training data and ongoing tuning.

Most production systems use all three: deterministic rules for high-confidence matches, probabilistic scoring for candidates, and ML models for the ambiguous cases that need human review.

From Entity Resolution to Master Data Management

Gartner defines entity resolution as the capability to consolidate multiple labels for individuals, products, or other data classes into a single resolved entity. It's the foundation of master data management, but not the whole picture.

Entity resolution tells you these five records represent the same customer. Master data management maintains the golden record—the authoritative version with the best data from all sources, governance rules about who can update what, and lineage showing where each attribute came from.

Organizations embarking on MDM initiatives should consider starting with entity resolution as state-of-the-art solutions can provide immediate value through improved data quality. You can resolve entities and see benefits before building the full MDM infrastructure.

Context Engineering for AI Systems

Context Engineering Principles

Context engineering curates the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes. It's the discipline of designing systems that provide the right information and tools in the right format.

Good context engineering means an agent gets exactly what it needs: the customer's current subscription tier, recent support interactions, and payment history. Not the full chat logs from three years ago, not every invoice ever generated, not the complete product catalog.

As we move towards more capable agents operating over multiple turns of inference, we need strategies for managing entire context state including system instructions, tools, external data, and message history. Each turn potentially adds more context, and without active management, performance degrades.

Context Rot and Reduction Strategies

Context Rot is the phenomenon where LLM performance degrades as the context window fills up, even if total token count is within technical limits. The model starts missing details buried in the middle, mixing up similar entities, or simply producing lower-quality outputs.

Mitigation strategies include compaction (stripping redundant information reversibly), summarization (condensing message history into key points), and selective retrieval (only fetching context relevant to the current task).

The tricky part is knowing what to keep. An agent handling a customer escalation needs the full complaint history, but probably doesn't need the original onboarding emails from two years ago. Context engineering means building systems that make these decisions automatically based on task requirements.

Multi-Agent Context Isolation

Multi-agent systems fail due to context pollution when every sub-agent shares the same context, creating massive KV-cache penalties. If your customer service agent spawns a billing specialist agent and a technical support agent, they shouldn't all carry the full conversation history.

Context isolation means each agent gets its own focused context: the billing agent sees payment history and subscription details, the technical agent sees system logs and configuration. They communicate through structured messages, not shared context windows.

This architectural choice dramatically improves performance and cost. Instead of three agents each processing 10,000 tokens of shared context, you have three agents processing 2,000 tokens of specialized context plus a coordination layer managing 1,000 tokens of shared state.

Enterprise RAG Architecture Patterns

Beyond Naive RAG

Naive RAG achieves 10-40% success rates in enterprise environments, driving rapid evolution to advanced patterns. The basic approach—chunk documents, embed them, retrieve by similarity, stuff into context—breaks down when you need precision.

Research analyzing three case studies found seven common RAG failure points: missing content (relevant information not in the corpus), missed top-K (relevant chunks ranked too low), incorrect specificity (too broad or too narrow), incomplete responses, wrong format, incorrect reasoning, and harmful content.

Advanced patterns address these failures through hybrid search (combining vector similarity with keyword matching), reranking (using a second model to reorder retrieved chunks), query rewriting (reformulating questions for better retrieval), and multi-stage pipelines that validate and refine results.

Agentic RAG with Knowledge Graphs

GraphRAG incorporates knowledge graphs to model entity relationships rather than treating documents as independent chunks. Instead of retrieving "documents similar to the query," you retrieve "entities and relationships relevant to the query."

When a user asks about customer churn, GraphRAG can traverse the knowledge graph to find customers who canceled, their support ticket history, their usage patterns, and similar customers who stayed. The retrieved context includes explicit relationships, not just semantically similar text.

Agentic RAG deploys specialized agents responsible for distinct domain areas, delivering targeted responses while maintaining autonomous navigation capabilities. A financial agent knows how to query the billing knowledge graph, a product agent knows the feature relationship graph, and they coordinate to answer complex questions.

Common RAG Failure Modes

Missing content happens when your corpus doesn't include the information needed to answer the question. No amount of retrieval tuning fixes this—you need better data coverage.

Missed top-K occurs when relevant chunks exist but don't rank highly enough. Hybrid search combining dense semantic retrieval with sparse keyword methods shows 15-30% better retrieval accuracy than pure vector search.

Incorrect specificity means retrieving chunks that are too general ("here's our entire pricing page") or too specific ("here's one sentence about enterprise discounts") when you need the middle ground. This often requires query rewriting or retrieval parameter tuning.

Incomplete responses happen when the answer requires information from multiple chunks that don't get retrieved together. Multi-hop retrieval and graph-based approaches help by explicitly modeling relationships between pieces of information.

Data Integration and Cataloging Architectures

ETL vs. ELT Patterns for Context Systems

With the advent of scalable cloud data platforms, ELT has become the preferred pattern, allowing organizations to store untransformed data as a single source of truth. Transform after loading, using the processing power of modern data warehouses.

For context management systems, this means ingesting raw data from all sources, preserving original structure and semantics, then building semantic layers and knowledge graphs on top. The raw data stays as the authoritative source, and transformations can evolve without re-extracting.

ETL architecture refers to the design and structure of how data is extracted from source systems, transformed into a usable format, and loaded into a target destination. The choice between ETL and ELT depends on where transformation logic is most maintainable and performant.

Data Catalogs for Context Discovery

A data catalog is a centralized, searchable inventory that helps users discover, understand, and use data assets across an organization. For AI agents, catalogs provide the metadata needed to understand what data exists and how to access it.

Modern data catalogs use AI and ML to automate metadata creation, accelerate curation, and enhance data discovery. They collect metadata about datasets, processing, and people—including how they use data assets.

The catalog becomes the starting point for context management: agents query the catalog to find relevant datasets, check lineage to understand data quality, and read business definitions to interpret values correctly.

Metadata Management and Active Governance

Active metadata management modernizes the practice through automation, integrations, and continuous monitoring, providing automated alerts and proactive data governance. Instead of manually documenting schemas, active metadata systems automatically detect changes, flag quality issues, and update documentation.

For AI systems, active metadata management means context stays current. When a schema changes, the knowledge graph updates. When data quality degrades, agents receive warnings. When new data sources appear, they're automatically cataloged and made available.

A data catalog is a tool while metadata management is a process—effective metadata management processes use a data catalog to store and surface metadata. The catalog is the interface, metadata management is the discipline.

How Galaxy Enables Enterprise Context Management

Automated Ontology-Driven Knowledge Graphs

Galaxy builds knowledge graphs automatically from existing data sources, eliminating the months of manual ontology modeling and complex ETL pipelines that typically block knowledge graph adoption. You connect your systems, and Galaxy infers the entities, relationships, and business concepts.

Traditional knowledge graph projects start with workshops to define ontologies, followed by months of ETL development to transform data into graph form. Galaxy inverts this: it learns your ontology from your data, creating a semantic model that reflects how your business actually operates rather than how you think it should operate.

This automated approach means you can start seeing value in weeks rather than quarters. Galaxy continuously syncs your data sources, automatically resolving entities and maintaining the knowledge graph as your business evolves.

Shared Context Layer for People and AI

Galaxy creates a unified context graph with entities, relationships, and business definitions accessible to both human users and AI agents. The same semantic layer that helps your data team understand customer churn powers your AI agents' reasoning about customer health.

This shared foundation solves the consistency problem that plagues most AI deployments. When your analytics dashboard and your AI agent use the same knowledge graph, they give consistent answers. When a business definition changes, both update automatically.

Galaxy addresses fragmented data across many systems with inconsistent definitions and duplicated entities, making it hard to reason consistently. By building a shared context layer, Galaxy ensures that "customer," "subscription," and "revenue" mean the same thing everywhere.

Continuous Sync and Entity Resolution

Galaxy continuously syncs data sources and resolves entities across systems, maintaining consistent, reliable context as your business evolves. When a customer updates their information in your CRM, Galaxy propagates that change to the knowledge graph. When a new system comes online, Galaxy integrates it without manual mapping.

Entity resolution happens automatically. Galaxy identifies when "Acme Corp" in your CRM and "ACME Corporation" in your billing system represent the same customer, merging them into a single entity in the knowledge graph. This unified view gives agents the complete picture they need for accurate reasoning.

Galaxy enables teams to build agents, analytics, and automation that coordinate correctly, explain behavior, and evolve as business changes without reworking logic or pipelines. The knowledge graph becomes the stable foundation that absorbs change, so your agents don't break when schemas shift or new data sources appear.

Implementation Considerations

Starting with Domain-Specific Graphs

Begin with high-value business domains rather than enterprise-wide initiatives. Pick one area where fragmented data causes real pain—customer support, sales operations, financial close—and build a knowledge graph for that domain first.

This focused approach lets you demonstrate value quickly, learn what works in your organization, and iterate on the semantic model before expanding. A working knowledge graph for customer support that actually helps agents resolve tickets faster is worth more than a comprehensive enterprise graph that's still in planning.

Once you have one domain working, expansion becomes easier. The patterns you established, the entity resolution rules you refined, and the semantic layer you built become templates for other domains.

Integration with Existing Data Infrastructure

Context management platforms must connect to cloud data warehouses, SaaS applications, and legacy systems without replacing existing investments. You're not ripping out your data warehouse to build a knowledge graph—you're adding a semantic layer on top.

Galaxy connects to existing data sources and APIs, building the knowledge graph without requiring data migration. Your data stays where it is, in the systems optimized for their specific workloads. The knowledge graph provides the semantic glue that makes sense of it all.

This integration-first approach means you can adopt context management incrementally. Start with a few critical systems, prove the value, then expand coverage. You're not betting the company on a big-bang replacement.

Measuring Context Quality and Coverage

Track metrics that matter for AI agent success: entity resolution accuracy (what percentage of duplicates are correctly merged), ontology coverage (what percentage of your business concepts are formally modeled), metadata completeness (what percentage of datasets have business definitions), and agent success rates dependent on context.

Entity resolution accuracy is measurable through sampling: take 100 resolved entities, manually verify they're correct, calculate precision and recall. Ontology coverage requires domain expertise: can your knowledge graph answer the questions your business actually asks?

Agent success rates close the loop. If your agents are succeeding at their tasks, your context management is working. If they're failing or hallucinating, you need better context coverage, higher-quality entity resolution, or richer semantic models.

FAQ

What is the difference between a knowledge graph and a semantic layer?

A knowledge graph is a network of interconnected entities and relationships representing real-world facts, while a semantic layer is a business-friendly abstraction that translates technical data structures into meaningful business concepts. Knowledge graphs can power semantic layers by providing the underlying structure for contextual connections, but semantic layers focus more on business terminology and consistent definitions across the organization.

How does context management differ from traditional data integration?

Context management focuses on semantic meaning and relationships enabling AI reasoning, not just moving data between systems. Traditional data integration asks "how do I get data from System A to System B?" Context management asks "how do I ensure AI agents understand what this data means and how it relates to other data?"

Why can't RAG alone solve enterprise context problems?

RAG relies on document chunking and embedding similarity which miss explicit relationships, business logic, and entity resolution across sources. When you chunk a document, you lose the structure that makes relationships clear. When you retrieve by similarity, you might get semantically similar text that's actually about different entities.

What role do W3C standards play in context management?

W3C standards including RDF, OWL, and SPARQL enable interoperability and machine-interpretable semantics for cross-system knowledge sharing and reasoning. They provide a common language for expressing semantic relationships, making it possible to federate knowledge graphs across organizational boundaries without custom integration code.

How does Galaxy differ from building custom knowledge graphs?

Galaxy automates ontology creation, entity resolution, and continuous synchronization, eliminating months of manual modeling and ETL development. Instead of starting with ontology workshops and ETL pipelines, you connect your data sources and Galaxy infers the semantic model from your actual data. This approach delivers value in weeks rather than quarters.

Can context management systems work with existing data catalogs?

Context management complements catalogs by adding semantic layers, ontologies, and knowledge graphs on top of existing metadata infrastructure. Your data catalog tells you what datasets exist and where to find them. Context management tells you what the data means, how entities relate across datasets, and how to reason over the combined information. They work together, not in competition.