Context Engineering for AI Agents: Building Reliable Enterprise Systems

An insurance company's AI agent processed 3,000 claims flawlessly during beta testing. On day four of production, it approved a fraudulent claim by citing a coverage clause that existed only in its degraded attention span. The agent didn't break—it drowned. Somewhere past the 40,000th token, buried under conversation logs, policy PDFs, and system instructions, the model's transformer architecture hit critical mass. Every token now competed with thousands of others for attention. Signal became noise. Confidence remained high.

The engineering team spent two weeks debugging prompts. The real problem was architectural: they'd built an agent with infinite appetite for context in a system with finite, degrading capacity for reasoning. This wasn't a prompt engineering problem. It was a context engineering failure.

Anthropic positions context engineering as "the natural progression of prompt engineering" for multi-turn agents, shifting focus from writing better prompts to curating optimal token configurations. Enterprise AI agents fail not from poor prompts but from context mismanagement: context rot, token budget exhaustion, semantic confusion across data sources. This guide covers context engineering frameworks, semantic grounding layers, long-horizon task patterns, and memory architectures for reliable enterprise agents.

What Is Context Engineering (And Why It Replaces Prompt Engineering)

From Prompts to Context as First-Class Architecture

Prompt engineering focuses on "how to write effective prompts, particularly system prompts." Context engineering addresses "strategies for curating and maintaining the optimal set of tokens during LLM inference." Building with LLMs now means answering "what configuration of context is most likely to generate our model's desired behavior?" rather than finding the right words.

Google's ADK treats context as "a first-class system with its own architecture, lifecycle, and constraints" using tiered storage and compiled views. Context management can no longer mean string manipulation—it requires architectural discipline alongside storage and compute.

The Context Window Problem in Enterprise Environments

Enterprise monorepos span "thousands of files and several million tokens" while LLMs limit at approximately 1 million tokens. The effective context window "where the model performs at high quality, is often much smaller than the advertised token limit"—currently less than 256k tokens. Token pricing creates "untenable OpEx for organizations with large engineering teams" when using indiscriminate context stuffing.

Context Rot and Attention Dilution

Context rot occurs when "as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases." The architectural cause: transformer architecture creates "n² pairwise relationships for n tokens" causing attention to stretch thin.

Models perform better at using information from the start or end of contexts, with performance degrading when accessing middle information. Context failures are invisible: "Your agent keeps running with incomplete information and produces confident but wrong results."

Core Context Engineering Principles

Treating Context as a Finite Resource

Context must be treated as "a finite resource with diminishing marginal returns" similar to LLMs' "attention budget." Good context engineering means "finding the smallest possible set of high-signal tokens that maximize the likelihood of some desired outcome."

Effective agentic systems must "treat context the way operating systems treat memory and CPU cycles: as finite resources to be budgeted, compacted, and intelligently paged."

Context Pollution and Signal-to-Noise Ratio

Context pollution refers to "the presence of too much irrelevant, redundant, or conflicting information within the context that distracts the LLM and degrades its reasoning accuracy." Flooding an LLM with dozens of irrelevant files actively harms its reasoning capabilities as the model must sift through noise.

The Five Context Components

System prompts should be "extremely clear and use simple, direct language that presents ideas at the right altitude" avoiding both rigid hardcoded logic and vague high-level guidance. Tools must be designed for "token efficiency" and encourage "efficient agent behaviors" with minimal overlap in functionality. Examples, memory, and retrieved knowledge complete the context configuration.

Just-In-Time Context Retrieval Patterns

Dynamic Loading vs Context Stuffing

Just-in-time context retrieval means "agents maintain lightweight identifiers (file paths, stored queries, web links) and use these references to dynamically load data into context at runtime using tools." Claude Code uses "CLAUDE.md files naively dropped into context up front, while primitives like glob and grep allow it to navigate its environment and retrieve files just-in-time."

A "memory-based workflow" replaces the "context stuffing anti-pattern" by using retrieval tools when needed.

Progressive Disclosure Architecture

Progressive disclosure enables "agents to assemble understanding layer by layer, maintaining only what's necessary in working memory and leveraging note-taking strategies for additional persistence." Factory.ai addressed context limitations by "building multiple layers of scaffolding, such as structured repository overviews, semantic search, targeted file operations, and integrations with enterprise context sources."

Memory Pointers Approach

The memory pointers approach shifts "the model's interaction from raw data to memory pointers" enabling agents to "process and utilize tool responses of arbitrary length without loss of information."

Managing Long-Horizon Tasks

The Long-Running Agent Challenge

Deep agents are "advanced AI systems designed to tackle complex, multi-step tasks that require sustained reasoning and execution over extended periods." The core challenge: "long-running agents must work in discrete sessions, and each new session begins with no memory of what came before."

Agent failure patterns include "the agent tended to try to do too much at once" and "prematurely considering the project complete."

Compaction Strategy

Compaction allows agents to "summarize its contents, and reinitiating a new context window with the summary" when nearing context limits. Claude Code compaction involves "passing the message history to the model to summarize and compress the most critical details" while "preserving architectural decisions, unresolved bugs, and implementation details while discarding redundant tool outputs."

Structured Note-Taking and Agentic Memory

Structured note-taking (agentic memory) enables "the agent regularly writes notes persisted to memory outside of the context window." A-MEM uses "the Zettelkasten method to create interconnected knowledge networks through dynamic indexing and linking."

Memory notes in A-MEM include "raw content, timestamp, LLM-generated keywords, LLM-generated tags, context descriptions, a dense embedding, and an initially empty set of links." A-MEM achieves "85–93% reduction in memory operation token usage" and "retrieval latency remains sub-10 microseconds for 1M notes."

Multi-Agent and Sub-Agent Architectures

Sub-agent architectures provide separation where "specialized sub-agents can handle focused tasks with clean context windows" while the main agent coordinates. "In the subagents pattern, a supervisor agent coordinates specialized subagents by calling them as tools" maintaining "strong context isolation."

A multi-agent research system with "Claude Opus 4 as the lead agent and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2%." The trade-off: multi-agent systems use about 15× more tokens than chats. Subagents facilitate compression by operating in parallel with their own context windows, exploring different aspects of the question simultaneously before condensing the most important tokens.

Semantic Layers: Grounding Agents in Business Logic

Why Raw Data Breaks AI Agents

"Without a robust semantic layer, AI agents can easily misinterpret intent, miss critical context, or even generate the wrong results." A semantic layer "helps your AI agent to make sense of disparate data sources to avoid jumping to the wrong conclusion."

"Data meaning disconnect" occurs when "a single business term like 'customer churn' or 'active lead' often has multiple, conflicting definitions across different systems and teams."

Semantic Layer Architecture and Components

The Agentic Semantic Layer is "dynamic, context-aware, and designed to work hand in hand with AI agents" ensuring queries are "interpreted within the correct business context." In data mesh environments, semantic layers "make sure all departments use the same definitions, so AI models don't get confused."

Measured Accuracy Improvements

Knowledge Graphs in semantic layers showed "54% accuracy boost of SQL queries" and dbt claims "83% accuracy rate for natural language questions." AtScale reports "up to 100% accuracy when business users query data through AI interfaces connected to our semantic layer" compared to "80+% failure rates from direct LLM-based querying without context."

Semantic layers "reduce Gen AI data errors by 66%" and are essential for "production-grade AI analytics."

Knowledge Graphs for Multi-Hop Reasoning

Knowledge Graphs vs Vector Databases

Knowledge graphs provide "the structured relationships and semantic context needed for agents to make informed decisions, collaborate effectively, and adapt to evolving inputs." Knowledge graphs enable "multi-hop reasoning" where "agents can perform logical inferences and planning by graph search and traversal, retrieving connected facts across several hops to reach a conclusion."

"Knowledge graphs capture how data is related through logical connections" enabling reasoning "beyond just finding similar items." The best approach: "combine vector search with Knowledge Graphs (GraphRAG)" to leverage "vector search's efficiency for initial retrieval with knowledge graphs providing precise context."

Explainability and Provenance

"Inference-bearing knowledge graphs can show the 'reasoning path' behind an AI agent's decision" providing transparency crucial for "auditing and building trust." Knowledge graphs enable "explainability for AI" by providing "source attribution & provenance" where "each fact comes from, linking it to its origin source for auditability."

Knowledge graphs "drastically reduce fabricated responses" by "grounding AI agents in a verifiable, inference-bearing knowledge graph."

Enterprise Knowledge Graph Implementation

Enterprise knowledge graphs map "the relationships between every piece of enterprise data, wherever it resides" creating "a single, always-current source of truth." Knowledge graphs are "the appropriate target for exploiting LLMs for business value, since they are machine-readable data structures representing semantic knowledge of the physical and digital worlds."

Financial risk modeling: knowledge graphs "help reconcile conflicting model outputs by tracing each prediction to its source, applying historical accuracy and credibility weightings."

Enterprise RAG at Scale

Why RAG Breaks at Enterprise Scale

"Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline." The real challenges emerge not in prompting or model selection, but in ingestion, retrieval optimization, metadata management, versioning, indexing, evaluation, and long-term governance.

Only 38.1% of studies address encryption, access control, or federated retrieval of proprietary data.

The RAG Sprawl Problem

"RAG Sprawl occurs when multiple teams independently develop their own RAG implementations using disparate technology stacks, custom connections to data sources, and inconsistent methodologies." A centralized RAG platform "ensures the components used in each application are standardized and of consistently high quality" including "data ingest, document processing, text chunking, vector search, hybrid search, reranking, hallucination detection and LLM interactions."

Enterprise RAG Performance Benchmarks

An enterprise RAG system searching "50+ million records" achieves answers in "10-30 seconds, with a 90% five-star user rating from busy customer service reps." 63.6% of implementations utilize GPT based models, and 80.5% rely on standard retrieval frameworks such as FAISS or Elasticsearch.

Single Source of Truth and Data Integration

The Enterprise Data Integration Challenge

"Many organizations have more than 900 applications that need to be connected to establish a single source of truth." Integration is often the biggest hurdle when looking to achieve a SSOT given that an integration project of this magnitude would be a burden on IT.

"Only 4% of organizations have real-time insights into their data" according to TechTarget Enterprise Strategy Group.

Data Silos and Fragmentation

"Data silos occur when data is isolated and inaccessible to other parts of organizational teams" leading to "fragmented insights and difficult collaboration." Organizations face "fragmented data landscapes, where information is scattered across disparate systems, databases, and applications" creating "inconsistencies and discrepancies."

Key SSOT challenges include "integration complexity, high costs, scalability, and cultural resistance."

SSOT Implementation Results

VMware's SSOT implementation loaded "12.7 million transactional data" and reduced it to "20% of the total number" as SSOT records, resulting in "38% improvement in lead conversion with around 50% of order booking without manual intervention." Publicis Sport and Entertainment "cut client onboarding from six months to just two or three weeks, and saved over 1,000 hours of manual work" using ThoughtSpot connected to their SSOT.

Master Data Management and Entity Resolution

Entity Resolution as MDM Foundation

Entity resolution is "the steppingstone to MDM" according to Gartner's 2024 Market Guide for Master Data Management Solutions. There has been a growing trend of clients beginning their MDM journey with entity resolution to ensure a cleansed and harmonized set of data before launching their MDM program.

Entity resolution addresses "determining when different data records refer to the same real-world entity, despite variations in how they're described."

Traditional MDM Limitations

Traditional MDM programs "often take years before they add value" and are "fraught with challenges." "Entity resolution processes required master data to be replicated into a single MDM hub, where compute-intensive, 'fuzzy matching' algorithms could slowly churn through data."

The 1-10-100 rule: "It takes $1 to verify a record as it's entered, $10 to cleanse and dedupe it and $100 if nothing is done."

Modern Entity Resolution Approaches

"Modern technologies, such as knowledge graphs, will also be utilized in matching processes to help drive new levels of efficiencies and insights from legacy match processes." Entity resolution "provides immediate value through improved data quality and relationship insights, delivering early wins that help build momentum for broader MDM initiatives."

How Galaxy Enables Context-Engineered AI Agents

Galaxy addresses the context engineering challenge by modeling your business as a connected system rather than disconnected tables. When AI agents query Galaxy, they receive structured entities, relationships, and business semantics instead of raw data dumps that pollute context windows.

Semantic Foundation for Agent Context

Galaxy builds an explicit semantic layer that captures how your organization defines customers, products, transactions, and their relationships across systems. This semantic foundation solves the "data meaning disconnect" where a single business term has multiple conflicting definitions. AI agents querying Galaxy receive consistent, contextually grounded information that reduces hallucinations and improves reasoning accuracy.

Galaxy's entity resolution creates a unified view of business entities across fragmented data sources. Instead of agents receiving duplicate or conflicting records that bloat context windows, they work with resolved entities that represent the true state of your business. This dramatically reduces context pollution while improving signal quality.

Just-In-Time Context Retrieval

Galaxy enables just-in-time context patterns by maintaining lightweight entity identifiers and relationship pointers that agents can traverse dynamically. Rather than stuffing entire datasets into context upfront, agents query Galaxy's knowledge graph to retrieve precisely the entities and relationships needed for each reasoning step. This progressive disclosure architecture keeps context windows lean while maintaining access to comprehensive business knowledge.

Galaxy's graph structure supports multi-hop reasoning where agents can traverse relationships to answer complex questions without loading intermediate data into context. When an agent needs to understand how a customer's purchase history relates to product recommendations and inventory availability, it queries the relationship graph rather than joining raw tables.

Long-Horizon Task Support

For long-running agent workflows, Galaxy provides persistent memory outside the context window. Agents can write structured notes about entities, decisions, and intermediate findings to Galaxy's knowledge graph, then retrieve this information in future sessions without context window limitations. This structured note-taking approach mirrors A-MEM's Zettelkasten method but grounds memory in your actual business entities and relationships.

Galaxy's versioning and provenance tracking enable agents to explain their reasoning by tracing back through the entity relationships and data sources that informed each decision. This explainability becomes critical for auditing agent behavior and building trust in production systems.

Integration with Enterprise Data Systems

Galaxy connects directly to existing data sources rather than requiring data replication into a separate hub. This federated approach means agents access current business state without the latency and consistency problems of traditional MDM implementations. Galaxy maintains the semantic layer and entity resolution while data remains in source systems.

Galaxy's API architecture supports both synchronous queries for real-time agent reasoning and batch operations for building agent memory stores. Teams can implement compaction strategies where agents periodically summarize their working context and persist key findings to Galaxy's knowledge graph, then reinitialize with clean context windows for continued work.

Implementing Context Engineering in Your Organization

Audit Your Current Context Architecture

Map all context sources agents currently use: system prompts, tool descriptions, examples, retrieved documents, conversation history. Measure token usage patterns across agent sessions to identify context bloat hotspots. Identify context pollution sources: redundant tool outputs, overlapping retrieval results, verbose prompts.

Prioritize Signal-to-Noise Improvements

Replace upfront context loading with just-in-time retrieval using lightweight identifiers. Implement token-efficient tools with minimal functional overlap. Establish a semantic layer or knowledge graph to provide structured business context.

Build Long-Horizon Task Infrastructure

Implement a compaction strategy for summarizing and reinitializing context windows. Deploy a structured note-taking system for persisting agent memory outside context. Evaluate multi-agent architecture for tasks requiring parallel reasoning or exceeding single context window capacity.

Establish Measurement and Governance

Track effective context window size where agents perform at target quality. Monitor token costs per task type to identify inefficient context patterns. Create governance for semantic layer updates, entity resolution rules, and knowledge graph schema evolution.

Key Takeaways

Context engineering treats context as a finite resource requiring deliberate curation across system prompts, tools, memory, and retrieved knowledge. Context rot and attention dilution mean effective context windows are smaller than advertised limits—currently less than 256k tokens for most models.

Just-in-time retrieval, compaction, structured note-taking, and sub-agent architectures enable long-horizon tasks without context overflow. Semantic layers and knowledge graphs ground agents in business logic, showing 54-100% accuracy improvements over raw data approaches. Enterprise AI requires treating context management as a platform discipline alongside ingestion, governance, and evaluation.

Conclusion

Context engineering shifts AI agent development from prompt iteration to architectural discipline—managing token budgets, semantic grounding, and memory persistence. The teams building reliable production agents recognize that context windows are scarce resources requiring the same careful management as memory and CPU cycles in traditional systems.

Organizations building production AI agents must implement context retrieval patterns, semantic layers for business logic, and governance for long-term reliability at scale. The difference between agents that work in demos and agents that work in production comes down to how deliberately you architect context flow across the system.