Context Management

Poor context management causes 'lost in the middle' failures, runaway costs, and truncated tool results that break agent reasoning.

Context management is air traffic control for tokens — every piece of information needs a slot, or the system crashes.

Scenario

A coding agent allocates 60% of context to current file + recent edits, 20% to retrieved docs, 10% to memory, 10% to system prompt — adjusting dynamically as files grow.

Solution

In Agent Memory, apply Context Management to this scenario: A coding agent allocates 60% of context to current file + recent edits, 20% to retrieved docs, 10% to memory, 10% to system prompt — adjusting dynamically as files grow. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Context Management (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Context Management happens in the code.

1# Context Management — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Context Management8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain context management clearly."},11        {"role": "user", "content": f"What is context management?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Context Management as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for context management

Quick recap — the most important points from this module.