Context Engineering: Why Most AI Systems Fail

Are you struggling with AI systems that fail despite using advanced models? Lack of context engineering is actually the root cause of most AI failures today. Even the most sophisticated large language models underperform when provided with incomplete information.

When you build AI applications, the quality of context you provide matters significantly more than complex code. In fact, the secret to creating effective AI agents lies in the richness and relevance of the context they receive. Without proper context engineering, your AI systems will likely produce hallucinations, outdated answers, and inconsistent results.

That’s where tools like Stash come in. Stash helps engineering teams design and maintain the right context flow, so developers and AI agents work with complete, reliable information.

Throughout this article, you'll discover what context engineering is, why it differs from prompt engineering, and how it addresses the fundamental reasons behind AI system failures. You'll also learn practical techniques to implement robust context engineering in your projects, ensuring your AI agents have the complete view of the world they need to perform optimally.

What is Context Engineering in AI?

Context engineering represents a fundamental shift in how you build effective AI systems. Unlike simple prompt crafting, context engineering focuses on finding the most relevant information from knowledge silos that AI models receive before generating responses.

Context engineering definition in AI systems

Context engineering is the art and science of filling an AI model's context window with precisely the right information for a specific task. Essentially, it involves designing systems that supply LLMs with all the knowledge they need to perform effectively. While prompt engineering concentrates on crafting single instructions, context engineering expands this approach to create entire information environments.

The context window functions similarly to RAM in a computer - it serves as the model's working memory. Furthermore, this memory must be carefully curated due to its finite capacity. Throughout the development of AI applications, context engineering determines what fills that window and how information is structured within it.

Context vs prompt: Expanding the input scope

Prompt engineering primarily involves writing accurate instructions that elicit expected responses from an LLM. However, context engineering encompasses a broader scope. It's about constructing the complete information environment.

The key difference lies in what each approach prioritizes:

• Prompt Engineering: Focuses on the input query or instruction; effective for simple, self-contained tasks
• Context Engineering: Concentrates on curating supporting information; essential for multi-step, data-rich tasks

Consider this distinction: if you ask an AI to "write a professional email," that's prompt engineering. Conversely, building a customer service bot that accesses user account details, remembers previous tickets, and maintains conversation history across interactions exemplifies context engineering.

Context engineering for agents and LLMs

For AI agents, context engineering becomes particularly crucial. Agents interleave LLM invocations with tool calls during long-running tasks. Consequently, they require careful context management strategies to handle conversations spanning hundreds of turns.

Context for agents typically includes system instructions, user input, short-term memory (chat history), long-term memory, retrieved information, tool definitions, tool responses, and structured outputs. Managing these components effectively determines whether your agent delivers magical results or disappointing failures.

At Stash, we’ve seen how even small context gaps, like missing file history or the company's way of doing business, can derail an otherwise well-built agent. That’s why we’re building Stash as a context engineering copilot to help teams obtain the right information, at the right time, automatically.

Case study: Cheap demo vs magical agent

When comparing AI agent demonstrations to production deployments, a troubling pattern emerges. According to recent benchmarks, even simple CRM tasks fail up to 75% of the time when agents attempt them repeatedly in real-world scenarios. Generally, what looks impressive in controlled demonstrations often collapses in actual use.

This reliability gap stems from the vast difference between demonstration environments and production settings. Initially, single executions might succeed 50-60% of the time, yet running the same task set repeatedly causes success rates to plummet to as low as 10-20%. Such inconsistency makes these systems unusable for critical business processes.

Missing tools, memory, and user data

The collapse of AI agent reliability often traces back to incomplete context. In one revealing troubleshooting case, developers spent days investigating a model that correctly identified cascading effects but missed the root cause - a code change. After exhaustive model debugging, they discovered the issue wasn't with the model itself but with missing data - an internal export had silently failed.

Additionally, AI agents face several critical context gaps:

• Limited access to real-time data and user history
• No standard way to connect to tools consistently
• Inadequate memory persistence across conversations
• Insufficient integration with company knowledge bases

Why model quality isn't the main problem

Even the most sophisticated models cannot reliably infer information that simply isn't there. As one expert noted, "No amount of model improvement could have identified a pull request that wasn't in the data". Notably, most organizations mistakenly focus on model quality or prompt engineering when context quality actually determines agent reliability.

The optimal range for reliable agent performance is 10-20 tools maximum. Exposing hundreds of API endpoints overwhelms even the most advanced LLMs, destroying success rates. The path forward requires thoughtful context engineering rather than model switching strategies.

Designing Context-Aware AI Systems

Effective context-aware AI systems don't happen by accident; they require deliberate design. Understanding how to structure and organize information for your AI applications is fundamental to creating systems that respond intelligently to user needs. With the proper context engineering techniques discussed below, you can dramatically reduce failures and improve your AI's performance.

Structuring retrieved documents and tool outputs

Retrieved documents require thoughtful structuring to maximize their usefulness. The Layout model offers advanced content extraction capabilities that divide large text into meaningful semantic chunks instead of arbitrary splits. This semantic chunking maintains coherence within each chunk, specifically preserving paragraph boundaries and table structures.

Tool outputs must likewise be formatted consistently. In practice, converting tool responses to Markdown format makes them more "LLM-friendly" and facilitates seamless integration. This structured approach ensures your AI system can effectively process and utilize external information.

Formatting responses with structured output schemas

Structured output schemas constrain AI responses to follow specific formats, enabling precise extraction and standardization of information. Both JSON and enum values can be generated as structured outputs. For JSON generation, two approaches exist:

1. Configure a schema directly on the model
2. Provide a schema within the text prompt

The first method is recommended as it effectively constrains the model output.

Injecting relevant memory and user state

Memory enables AI systems to maintain context across extended interactions. Two key memory types enhance AI adaptability:

• Persistent memory: Retains long-term facts like user preferences
• Episodic memory: Stores recent interactions and contextual information

Effective memory implementation gives users agency through transparency and control over what's stored. Subsequently, this enables AI to continue conversations over time, follow up on unresolved tasks, and tailor responses to individual preferences.

Using RAG to reduce hallucinations

Retrieval Augmented Generation (RAG) stands out as one of the most effective techniques for reducing AI hallucinations. By connecting generative AI models with external knowledge bases, RAG grounds responses in factual information rather than relying solely on training data. This technique enables AI systems to access current data in real-time through APIs and other connections, making them more accurate in domain-specific contexts without requiring fine-tuning.

Several hallucination detection methods work particularly well within RAG systems:

• LLM prompt-based detectors that classify responses into context-conflicting hallucinations and facts
• Semantic similarity detectors that measure alignment between statements and context
• BERT stochastic checkers that use pre-trained contextual embeddings to evaluate consistency

Studies show that combining token similarity detection to filter obvious hallucinations with LLM-based detection offers the best balance between accuracy and cost.

Context compression for large token windows

Even as context windows expand to 100K tokens or more, efficiently managing this space remains crucial. Context compression techniques help maintain performance while reducing computational costs. Pin-Yu Chen, an IBM researcher, suggests that with larger windows, "you can throw in all the books and enterprise documents you want the model to process". However, it does not work in real scenarios where LLMs focus on only some part of a very long textual data.

Therefore, context compression still provides value through:

• Summarization of older messages in multi-turn conversations
• Fact extraction to pull out key information from documents
• Selective token dropping to remove less important tokens

IBM researchers recently developed methods for LLMs to generate synthetic longform instruction data and compress it at different ratios, optimizing information density within context windows.

Conclusion

Context engineering stands at the heart of successful AI implementation, fundamentally shifting how you should approach building effective AI systems. Throughout this article, we've seen that most AI failures stem not from model limitations but from critical context gaps. These gaps occur when AI systems lack the comprehensive information environment needed to perform reliably.

Understanding the distinction between prompt engineering and context engineering proves essential. While prompt crafting focuses on single instructions, context engineering expands this approach to create complete information ecosystems. This distinction explains why impressive demonstrations often fail in production - the context simply isn't rich enough for consistent performance.

Effective context engineering requires deliberate design. You must carefully structure retrieved documents, format responses appropriately, and inject relevant memory and user state. These elements work together to create AI systems that maintain coherent context and deliver reliable results consistently.

RAG techniques significantly reduce hallucinations by grounding AI responses in factual information rather than relying solely on training data. Additionally, context compression techniques help manage the utilization of token windows efficiently.

Therefore, your focus should shift from seeking better models to implementing robust context engineering. Treating context as a product with version control, quality checks, and continuous improvement will dramatically increase your AI system's reliability.

Above all, remember that context quality determines agent reliability. You can transform unreliable AI implementations into consistent, powerful tools by addressing context gaps systematically. The future of effective AI doesn't lie in ever-larger models but in thoughtfully engineered context.

If you’re a developer and asking questions around to understand the ticket assigned to you, struggling with where to start coding for the ticket, Stash gives you the most relevant context you need, designed to surface the right code files, past issues, and related docs so you and your AI-pair programmer (Claude Code ,Copilot or Cursor etc.) can stop searching and start coding in seconds.