The WeBuild-AI Perspective on Context Engineering

"Prompt engineering is dead," declared the internet with its usual theatrical flair. "Long live context engineering!"

Of course, if there's one rule about the internet, it's this: don't believe everything you read on it. Prompt engineering isn't dead. It's simply been promoted. Or perhaps more accurately, we've finally admitted that writing a clever prompt was never the whole story.

As enterprises deploy increasingly sophisticated AI systems, a new discipline has crystallised: Context Engineering. The concept makes intuitive sense: provide more context, achieve better results. Yet the practical reality involves significant trade-offs that enterprise leaders must navigate strategically, and these trade-offs are far more substantial than the breathless LinkedIn posts suggest.

The Rise of Context Engineering

The term "context engineering," popularised by thought leaders including Andrej Karpathy and Shopify CEO Tobi Lütke, represents a fundamental shift in how we conceptualise AI system development. As Karpathy eloquently described it: context engineering is "the delicate art and science of filling the context window with just the right information for the next step."

This isn't merely semantic refinement. It marks the maturation of our approach from crafting individual prompts to architecting complete information ecosystems around LLMs. Recent academic work, including a comprehensive survey analysing over 1,300 research papers, formalises Context Engineering as a systematic discipline encompassing context retrieval and generation, context processing, and context management, all integrated into sophisticated implementations like retrieval-augmented generation (RAG), memory systems, and multi-agent architectures.

The Compelling Logic

The theoretical appeal of context engineering is immediately apparent. Karpathy's analogy resonates strongly with technical leaders: LLMs function like an operating system, where the model acts as the CPU and its context window serves as RAM. Just as an operating system curates what fits into working memory, context engineering determines what information the model can access to perform its tasks.

The evidence supports this intuition. Research demonstrates that providing structured context and "cognitive tools" can dramatically increase model performance on challenging benchmarks. In some cases nearly doubling success rates. The message appears clear: richer context yields superior performance.

Moreover, context engineering addresses critical limitations of simple prompt-based approaches:

Cross-System Intelligence: Enterprise insights rarely exist in isolation. Strategic questions require synthesising information across customer support systems, sales platforms, product analytics, and financial records. Context engineering provides the framework for this comprehensive synthesis.

Unified Semantic Understanding: Advanced context systems create semantic representations that enable AI to understand conceptual relationships across disparate information sources, moving beyond simple keyword matching to genuine comprehension.

Temporal Coherence: Memory systems and state management allow AI agents to maintain context across multi-turn interactions, building relationships and understanding that develop over time rather than resetting with each query.

The promise is tantalising: provide comprehensive context, and AI systems transcend simple task execution to deliver genuine strategic intelligence.

The Cost of Context

However, our extensive experience deploying context-rich AI systems across enterprise clients reveals a crucial tension that academic discussions often ignore: context engineering's benefits come with significant costs that fundamentally shape what's practical at scale.

Every piece of context consumed represents tangible cost. Modern LLMs charge per token processed, and sophisticated context engineering can dramatically increase token consumption. Consider a typical enterprise scenario:

A basic prompt might consume 200 tokens. Add comprehensive RAG retrieval (3,000 tokens), conversational history (2,000 tokens), tool descriptions (1,500 tokens), few-shot examples (1,000 tokens), and system instructions (500 tokens), and you've increased your context payload to 8,200 tokens. A 41x increase in input cost per query.

At scale, this multiplier transforms cost structures. An application serving 10,000 queries daily shifts from manageable to prohibitively expensive for many The theoretical superiority of rich context confronts the practical constraint of budget realities for enterprise organisations.

Speed Versus Sophistication

Token processing imposes latency costs that compound as context grows. Retrieving information from multiple systems, constructing semantic representations, and managing state all add milliseconds that aggregate into seconds. The difference between responsive systems and frustrating user experiences.

This isn't merely about user patience. In interactive applications where AI agents engage in multi-turn reasoning, latency multiplies across iterations. A five-turn conversation with 500ms added latency per turn introduces 2.5 seconds of dead time. Enough to fundamentally alter perceived application responsiveness.

The MCP Imperative

Effective context engineering at enterprise scale demands sophisticated infrastructure that few organisations currently possess. Model Context Protocol (MCP) has emerged as the de facto standard for connecting AI systems to enterprise data sources, but implementation requires substantial investment.

MCP enables the real-time, permission-aware data access that makes context engineering practical. Yet deploying MCP across an enterprise stack involves:

Integration Complexity: Each enterprise system requires custom MCP server implementation. SharePoint, Salesforce, ServiceNow, GitHub. Every data source needs bespoke integration that respects existing security frameworks whilst providing queryable interfaces.

Governance Frameworks: MCP implementations must maintain comprehensive audit trails, implement consistent permission models, and provide oversight capabilities that enterprise governance requires. This isn't middleware; it's fundamental architecture.

Operational Overhead: Running production MCP infrastructure means managing server deployments, monitoring performance, handling authentication, and coordinating updates across multiple systems. Ongoing operational complexity that demands dedicated resources.

Building this infrastructure isn't measured in weeks. For mid-to-large enterprises, comprehensive MCP deployment typically requires 6-12 months of focused engineering effort. Time during which competitive pressure mounts and stakeholder patience wanes.

Optimising for Reality

The question facing enterprise leaders isn't whether context engineering delivers value. Evidence clearly demonstrates it does. The question is: how do you balance the undeniable benefits of rich context against the practical constraints of cost, latency, and infrastructure investment?

Before diving into implementation strategies, it's crucial to understand where context engineering delivers transformational value versus where simpler approaches suffice. This distinction determines whether you're making strategic investments or simply over engineering solutions to problems that don't warrant the complexity.

Context engineering finds its most compelling application in agentic AI systems. These are autonomous or semi-autonomous systems that perform multi-step tasks, make decisions, and interact with tools and data sources to achieve specific outcomes. Here, context engineering isn't optional luxury. It's fundamental requirement.

Why Agents Need Rich Context:

Agents operate across extended time horizons, making multiple decisions and taking numerous actions to complete complex workflows. Unlike single-shot queries, agents must maintain state, remember previous actions, understand their goals, and adapt their approach based on intermediate results. This demands sophisticated context management:

• Memory and State: Agents need episodic memory (what actions they've taken), semantic memory (relevant facts and procedures), and working memory (current task state). Without proper context engineering, agents lose coherence across multi-turn interactions, repeating actions or losing track of their objectives.

• Tool Orchestration: Enterprise agents typically have access to dozens of tools and data sources. They need rich context about what each tool does, when to use it, how to interpret results, and how different tools relate to each other. This tool context often comprises thousands of tokens but enables agents to make intelligent decisions about which capabilities to invoke.

• Domain Knowledge: Agents performing specialised tasks require deep domain context. A financial analysis agent needs accounting principles, regulatory requirements, and company-specific policies. A customer service agent requires product knowledge, support procedures, and historical customer interactions. This domain context, delivered through RAG and MCP, transforms generic models into specialised experts.

• Error Recovery: When agents encounter failures or unexpected results, they need sufficient context to understand what went wrong and how to adapt. Rich context about previous attempts, error patterns, and alternative approaches enables sophisticated error recovery that simple prompting cannot achieve.

Consider a procurement agent tasked with sourcing components for manufacturing. It must understand company procurement policies (procedural memory), access current supplier catalogues (real-time data via MCP), remember previous negotiations with vendors (episodic memory), apply regulatory compliance requirements (domain knowledge), and coordinate across purchasing, finance, and operations systems. Context engineering provides the infrastructure that makes such complexity manageable.

Where Prompting Still Reigns Supreme

Yet context engineering isn't universally appropriate. For everyday knowledge workers using AI embedded in their O365 suite or interacting with ChatGPT, Copilot, or Claude for routine tasks, traditional prompting remains not just adequate but often preferable.

• Single-Turn Interactions: When users need quick answers, document summaries, or one-off assistance, the overhead of context engineering delivers marginal benefit. "Summarise this email thread" or "Draft a response to this customer inquiry" work brilliantly with well-crafted prompts and don't justify complex infrastructure.

• Personal Productivity Tools: AI assistants embedded in Word, Excel, or Outlook operate in well-defined contexts with limited scope. The document you're editing or the email you're composing provides sufficient context naturally. Additional infrastructure adds complexity without commensurate value.

• Creative and Exploratory Work: Writers, designers, and strategic thinkers often use AI for brainstorming, ideation, and creative exploration. These applications benefit from focused, well-crafted prompts that provide just enough context to guide without constraining. Overloading with context can actually stifle the creative divergence these users seek.

• The Craft of Prompting: Mastering prompt engineering remains an essential skill for knowledge workers. Understanding how to structure requests, provide relevant examples, specify desired formats, and guide model behaviour delivers immediate productivity gains without requiring enterprise infrastructure investments.

  A marketing manager drafting campaign copy, an analyst exploring data patterns, or a consultant preparing client presentations can achieve excellent results through skilled prompting. They don't need MCP servers or RAG pipelines. They need understanding of how to communicate effectively with AI systems through well-constructed prompts.

The Spectrum of Context Sophistication

The reality is that AI applications exist on a spectrum:

At one end, simple prompting suffices for discrete, well-bounded tasks where users can provide necessary context directly. At the other end, autonomous agents performing complex, multi-step workflows absolutely require sophisticated context engineering infrastructure.

In between lies a vast middle ground where the right approach depends on specific characteristics: task complexity, information scope, interaction patterns, and strategic importance. The key is matching context sophistication to actual requirements rather than defaulting to either extreme.

Enterprise leaders should resist the temptation to either dismiss context engineering as unnecessary complexity or to assume every AI application demands full infrastructure investment. Strategic assessment of where rich context delivers transformational value versus where skilled prompting suffices determines resource allocation and implementation priorities.

Pragmatic Context Engineering

Context engineering represents genuine advancement in enterprise AI capabilities. The principle is sound: provide AI systems with comprehensive, well-structured context, and they deliver dramatically superior outcomes. The mathematics confirm this. The use cases validate it. The competitive advantages are real.

However, the path from principle to practice traverses terrain more complex than early enthusiasm suggested. Token economics impose real costs. Latency constraints demand careful optimisation. Infrastructure requirements necessitate substantial investment. These aren't temporary hurdles to overcome; they're permanent considerations that shape what's feasible.

The organisations succeeding with context engineering recognise this reality. They approach implementation strategically, balancing ambition with pragmatism. They invest in infrastructure like MCP not because it's exciting but because it's necessary. They implement hybrid architectures that optimise context depth based on actual requirements rather than maximising context universally.

Most importantly, they view context engineering as a capability to build progressively over time rather than a technology to adopt wholesale immediately. They start with high-value use cases, prove the concept, build infrastructure systematically, and scale thoughtfully based on demonstrated results.

Conclusion

Yes, context engineering makes sense as a concept. Yes, more context generally produces better results. Yes, the theoretical benefits are compelling.

But success requires acknowledging the practical constraints that shape what's achievable. It requires substantial investment in infrastructure like MCP. It requires careful management of token economics and latency impacts. It requires time, measured in quarters and years, not weeks.

For enterprise leaders navigating these decisions, the key insight is this: context engineering delivers genuine competitive advantage, but only when implemented with clear-eyed recognition of its costs and requirements. The winning strategy isn't about choosing perfect context universally; it's about building adaptable systems that optimise context strategically based on actual business requirements.

The question isn't whether your organisation should pursue context engineering. Evidence overwhelmingly suggests you should. The question is: how quickly can you build the capabilities required to do it effectively, and what path will get you there most reliably?

For organisations ready to navigate this journey with experienced guidance, the time to begin is now. The competitive advantages are real. The infrastructure requirements are substantial. The investment timelines are measured. But for those willing to approach context engineering with strategic discipline rather than uncritical enthusiasm, the results justify the effort.