How to Optimize Your AI Coding Agent Context
Last Updated on February 9, 2026 by Editorial Team
Author(s): Eivind Kjosbakken
Originally published on Towards AI.
Make your coding agents more efficient
The context of your AI coding agent is critical to its performance. It is likely one of the most significant factors determining how many tasks you can perform with a coding agent and your success rate in doing so.

This article discusses techniques to enhance the context provided to AI coding agents, emphasizing the need for continual updates to the AGENTS.md file to improve their performance. It covers the importance of keeping documentation links current and utilizing infrastructure as code (IaC) to streamline processes. The author shares four specific techniques aimed at improving efficiency, explaining how proper context management can significantly enhance the capabilities of coding agents across various tasks in programming.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.