Machine Learning Mastery released a guide on context engineering for reliable AI agents

Machine Learning Mastery released a practical guide on context engineering for AI agents — a discipline that determines which data a model sees at each moment of operation. The main thesis of the article: production issues in agentic systems are more often related not to model quality, but to how developers manage context, history, and tokens.

Why agents break

The author suggests viewing the context window as a limited computational resource, not as a technical detail that can be ignored. Tokens have not only a monetary cost, because each model call is paid for, but also a cognitive one: a long and poorly structured input reduces the quality of reasoning. The model pays more attention to the beginning and end of the context, while the middle often loses influence, even if formally everything fits within the limit.

The context window is not a workaround constraint, but the main design

parameter of an agentic system.

Hence the typical failure scenario: the agent simply has everything "glued on" — old answers, raw tool outputs, duplicate fragments from retrieval, and outdated solutions. As a result, latency and cost grow, while the useful signal is drowned out in noise. The article compares this to RAM: fast memory is powerful, but finite. Everything the agent doesn't need right now should be stored in external memory and enter the context only on demand.

How to assemble context

The most useful architectural idea from the guide is to strictly separate static and dynamic context. The static part includes system instructions, agent role, rules, tool descriptions, and response format. This data barely changes, so it can be cached as a prefix. The dynamic part is the current user query, fresh tool results, the agent's latest steps, and documents that are actually needed at this stage.

Before assembling the prompt, the author suggests doing an audit of all layers that typically fill the context window:

• system instructions and few-shot examples;
• dialog history, agent responses, and tool call results;
• external data from knowledge bases, files, or search;
• working state: intermediate conclusions, plan, next steps.

The practical takeaway is simple: you don't need to minimize each layer at any cost; you only need to remove what doesn't help the current step. A two-pass scheme looks useful. First, the system raises the permanent framework: system prompt, cacheable rules, long-lived summary. Then it loads the variable part: relevant task state, fresh retrieval, and a short relevant tail of history. This assembly also simplifies debugging, because you can immediately see whether the problem is in the configuration or in the current session's data.

How to control quality

A separate section of the article is devoted to two areas where agents degrade fastest: dialog history and retrieval. Simple accumulation of all conversation quickly inflates the context and cements model errors as if they were facts. The author recommends moving away from raw history to rolling summary or even structured session state, where user intent, decisions made, actions completed, and next steps are recorded separately. This gives the agent memory without endless token growth.

The logic with retrieval is similar: each found batch of data consumes budget, so it cannot be considered free. The article recommends filtering results before inserting them into the prompt, using semantic chunking instead of fixed-size cutting, and where necessary, combining semantic search with keyword or metadata filters. For mature systems, agent-controlled retrieval is considered a stronger option — the agent itself calls the search only at the moment it's really needed, not automatically on every move.

For production, the author suggests measuring not only the final answer but also the quality of the context itself. Among useful metrics are token budget utilization, compression rate after summary, retrieval accuracy, and signs of context drift, when the agent starts rereading already processed files or deviates from the original task.

Another practical technique is probe-based evaluation: after compression or retrieval, the system is asked control questions to verify that needed facts, artifacts, and the ability to continue a multi-step task from the same point are preserved.

What this means

The Machine Learning Mastery guide does a good job of capturing the shift in agentic development: the quality of an AI agent now depends not only on the choice of model, but also on how disciplinedly memory, retrieval, and token budget are organized. For teams deploying agents to production, this is a direct signal to design context as a separate layer of architecture, not as a tail of the prompt.