Inside the Context Window

What makes your work with agents efficient? Chosen model? Harness? Instructions clarity?
I would say that first of all it's the quality of the context you provide.

Context is everything the model sees before it generates a response.
Two facts to know about the context:
1. It's limited (and costs you money 💰 ).
2. The longer the context, the worse the results.

So context engineering is a set of practices to fill the context with just enough information to get the desired results. The main goal is to balance the amount of context given: not too little and vague, not too much and detailed.

The first step in context engineering is to understand what the context actually contains. And it’s not just your prompt.

Typical context structure:
🔸 System prompts & instructions: the hidden layer of system prompts, safety policies, behavioral rules, role definition. Usually it's part of the harness and you cannot change it.
🔸 Project context: AGENT.md\CLAUDE.md, repo structure, settings. It's added as a first prompt to any session you open with the agent.
🔸 Available tools: skill descriptions, MCPs, available CLIs.
🔸 Retrieved information: loaded files, data from RAG system.
🔸 State & history: The current conversation, including user, model and tools responses.
🔸 Reasoning: intermediate reasoning results (thinking mode).
🔸 Long-term memory: knowledge base from previous conversations like user preferences, summaries of working sessions, facts the agent was asked to remember for future use.
🔸 Your prompt: the actual user request.

As you can see, the context is already filled with a lot of information before you even start the real work. To make agents efficient, keep their context clean and focused. Don't overload it with unnecessary information.

#ai #engineering