AI coding workflows range from approving every line to running autonomous overnight loops. Here is how they evolved from micromanagement to fully autonomous agents.

An agents.md file bloats and goes stale over time. Here is how to prune and maintain it so every token earns its place inside a limited context window.

Should you hand-craft context or trust agents to self-correct? The mindset shifted from 2025 to 2026. Here is how to balance careful context with agent autonomy.

agents.md, claude.md, gemini.md, and Cursor rules all brief coding agents. Here is how they compare and why agents.md is becoming the de facto standard.

A bloated system prompt wastes context and confuses the model. Here are the best practices for writing concise, high-signal system prompts that maximize your window.

A single agents.md rarely fits a large project. Here is how to structure a hierarchy of agents.md files so agents get the right context in each area of a codebase.

An agents.md file is the backbone of context engineering for coding agents. Here is why this small file is the highest-leverage way to control what an agent knows.

Stuffing the context window seems safe but often backfires. Here is why putting less in the context frequently produces better, more accurate AI coding results.

Context compacting summarizes an agent's history when the window fills up. Here is what it is, when to trust it, and when to manage context yourself.

A context window is the maximum tokens a model can consider at once. Here is how it works and the current token limits for GPT 5.2, Claude Sonnet 4.5, and Gemini.