Machine Learning Mastery publishes context engineering guide for reliable AI agents

Machine Learning Mastery has published a practical guide to context engineering for AI agents, the discipline that determines what data a model sees at each point in its work. The article's central claim: production problems in agentic systems are more often tied not to model quality but to how developers manage context, history and tokens.

Why agents break

The author suggests treating the context window as a limited computational resource, not a technical detail that can be ignored. Tokens carry a cost beyond money, since every model call is paid for: long, poorly structured input also degrades reasoning quality. The model pays more attention to the beginning and end of the context, while the middle often loses influence even when everything formally fits within the limit.

The context window is not a constraint to work around but the main

design parameter of an agentic system.

Hence the typical failure scenario: everything is simply "glued" onto the agent, including old replies, raw tool outputs, duplicate retrieval fragments and outdated decisions. Latency and cost grow, and the useful signal drowns in noise. The article compares this to RAM: fast memory is powerful but finite. Anything the agent does not need right now should live in external memory and enter the context only on request.

How to assemble context

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

Before building the prompt, the author advises auditing all the layers that typically fill the context window:

• system instructions and few-shot examples;
• dialogue history, agent replies and tool call results;
• external data from a knowledge base, files or search;
• working state: intermediate conclusions, the plan, next steps.

The practical takeaway is simple: don't minimize every layer at any cost; remove only what doesn't help the current step.

A two-pass scheme looks useful. First the system loads the permanent scaffold: the system prompt, cacheable rules and long-lived summaries. Then it loads the variable part: the current task state, fresh retrieval and a short, relevant tail of the history. This assembly also simplifies debugging, because it is immediately clear whether the problem lies in the configuration or in the data of the current session.

How to control quality

A separate section of the article covers the two areas where agents degrade fastest: dialogue history and retrieval. Simply accumulating the entire conversation quickly bloats the context and entrenches the model's mistakes as if they were facts. The author advises moving from raw history to a rolling summary, or even to a structured session state that separately records the user's intent, decisions made, actions completed and the next step. This gives the agent memory without endless token growth.

Retrieval follows similar logic: every batch of retrieved data consumes budget, so it can't be treated as free. The article recommends filtering results before inserting them into the prompt, using semantic chunking instead of slicing into fixed-size pieces, and, where needed, combining semantic search with keyword or metadata filters. For mature systems, agent-controlled retrieval is considered the stronger option: the agent itself calls search only at the moment it is really needed, rather than automatically on every turn.

For production, the author proposes measuring not only the final answer but also the quality of the context itself. Useful metrics include token budget utilization, compression ratio after summarization, retrieval precision and signs of context drift, when the agent starts rereading files it has already processed or wanders away from the original task. Another practical technique is probe-based evaluation: after compression or retrieval, the system is asked control questions to check whether the necessary facts and artifacts survived, and whether a multi-step task can continue from the same point.

What this means

The Machine Learning Mastery guide captures a shift in agent development: the quality of an AI agent now depends not only on the choice of model but on how disciplined the design of memory, retrieval and token budget is. For teams moving agents into production, this is a direct signal to design context as a separate architectural layer, not as the tail end of a prompt.