We Don’t Have an AI Problem. We Have a Context Problem. (And MCP Exposes It)

2026-03-26 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

The article argues that current "AI systems," particularly those built with Large Language Models (LLMs), suffer from a "context problem" rather than an "AI problem." The author, an experienced builder of AI features, describes how systems become fragile and unpredictable when context is unstructured and overloaded into prompts. This leads to issues like unreadable prompts, overlapping context, and difficult debugging. The Model Context Protocol (MCP) is introduced as a conceptual shift that treats context as a system rather than just text. MCP advocates for separating concerns within the prompt, categorizing elements like user input, memory, tools, instructions, and environment into distinct layers. This approach, likened to the evolution of APIs, aims to bring structure, boundaries, and predictability to LLM system design, moving beyond mere prompt engineering to a more architectural approach to information flow.

Key takeaway

For NLP Engineers and CTOs building LLM-powered applications, you should shift your focus from optimizing individual prompts to designing a robust context architecture. Treating context as a structured system, rather than a monolithic string, will significantly improve system predictability, debuggability, and scalability. Prioritize separating concerns like memory, tools, and instructions early in your design to avoid future fragility and ensure your systems can evolve effectively.

Key insights

Unstructured context, not model limitations, is the primary bottleneck in scaling and maintaining LLM-based systems.

Principles

• Context should be architected, not merely input.
• Separate memory, tools, and instructions early.
• Design for context evolution.

Method

MCP proposes structuring LLM context into distinct layers like input, memory, tools, instructions, and environment, moving from a monolithic prompt to a modular, designed information flow.

In practice

• Log and inspect context for visibility.
• Prioritize context design over "clever prompts."
• Structure codebase as context for coding assistants.

Topics

• AI System Design
• Context Management
• Model Context Protocol
• Prompt Engineering
• Large Language Models

Best for: NLP Engineer, CTO, VP of Engineering/Data, Machine Learning Engineer, AI Architect, Software Engineer

Related on AIssential

• Your Prompts Aren’t the Problem—Your Context Is — Effective AI performance hinges on managing the model's context window with relevant, timely information, not just prompt wording.
• The hard problems were never language problems. — Production AI agents require robust system architectures beyond just Large Language Models.
• The Context Problem — AI's "context" as a billing unit conflates capacity with coherence, leading to inflated costs and degraded model performance.
• The Architecture of Next-Gen AI: Deep Diving into Model Context Protocol (MCP) — MCP standardizes AI tool integration by decoupling execution from LLM decision-making, enabling modular and secure agent architectures.
• When the Loop Closes: Architectural Limits of In-Context Isolation, Metacognitive Co-option, and the Two-Target Design Problem in Human-LLM Systems — Prompt-level isolation is architecturally insufficient for multi-modal LLM systems due to context contamination within the attention window.
• No AI Agent Orchestration Needed? What? — Modern frontier LLMs can self-orchestrate complex procedural tasks more effectively via in-context prompting than with external agent frameworks.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.