Context management is the real bottleneck in AI-assisted coding
After using Cursor and Claude Code daily, I’ve noticed that when an AI coding agent drifts or forgets constraints, we assume it’s a model limitation.
In many cases, it’s context management.
A few observations: - Tokens are not just limits. They’re attention competition. - Even before hitting the hard window limit, attention dilution happens. - Coding tasks degrade faster than chat because of dependency density and multi-representation juggling (diffs, logs, tests).
I started managing context deliberately: - Always write a contract - Chunk sessions by intent - Snapshot state and restart - Prefer on-demand CLI instead of preloading large MCP responses
It dramatically improved the stability of the agent.
Curious how others are handling context optimization.
I also wrote a detailed breakdown of: - How tokens and context windows actually affect stability - Why coding degrades faster - A practical context stack model - Why on-demand CLI retrieval is often more context-efficient
Full post: https://codeaholicguy.com/2026/02/14/tokens-context-windows-...