Research
Context Minimization Principle
The core theory in the Context Cost research program.
Explore CMPContext Cost
Insights on software engineering in the AI era.
Research and writing on software design, context cost, and AI-assisted engineering.
By Leric ZhangExplore
Writing
Essays
Short arguments on software architecture, AI coding agents, and context engineering.
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Airic
Markdown-configured agent kernel for your workspace via ACP — modes, processes, and tool guidance in editable `.airic/` specs.
Visit AiricAgents
Skills
Installable CMP agent skills — a pre-task planner and a post-task design reflection loop.
Browse skillsRecent Essays
View all- Will Your Coding Agent Quietly Make Business Decisions for You?
When policy and mechanism sit mixed together, an agent can rewrite business rules while it rewrites implementation — and you may never see it. Clean Architecture Planner splits the two before any code is written.
- Post-Change Design Reflection: Making Every Task Leave the Codebase Cleaner
How a CMP practice turns the context an agent just acquired into design feedback—syncing docs, flagging duplication, and leaving ownership decisions for humans—so each change leaves the codebase easier to modify.
- What's Left for Programmers After AI Takes Over the Code?
Code generation by AI is no longer a prediction—it's observable reality. But the work of solving real-world problems with software isn't going away. A framework for understanding what remains human territory: value judgments, problem definition, and the future of the software practitioner.
- Multi-Agent Systems: From Division of Labor to Division of Attention
Why do we need multiple AI agents when one LLM can do everything? The answer isn't about skills—it's about memory isolation and context sharding. Exploring how Multi-Agent systems solve the attention problem through context separation.
Written by Leric Zhang. Find more on GitHub.