advanced-context-engineering-for-coding-agents

kept by eddie jul 24

AI coding agents alone can't sustain high-quality software factories because model limitations and review bottlenecks remain, requiring better context engineering rather than just more loops.

A Complete Guide To AGENTS.md
An AGENTS.md file configures AI coding agent behavior, and should be kept minimal using progressive disclosure—only essential instructions like project description and package manager belong at the root, with other rules delegated to separate files.
also on HumanLayer, #ai-coding, #software-engineering, #productivity, #developer-tools
The cost of caring about software - Alex Rios Substack
AI reduces code-writing effort but shifts verification cost onto reviewers, creating an externality where comprehension debt rises despite faster generation.
also on Faros AI, #ai-coding, #software-engineering, #productivity, #developer-tools
The 80% Problem in Agentic Coding - by Addy Osmani
Addy Osmani argues that while AI agents now write 80% of code for early adopters, this shifts problems to "comprehension debt," where developers understand less of their own codebases.
also on Faros AI, #ai-coding, #software-engineering, #productivity, #developer-tools
Agency and Agents - by Ethan Mollick - One Useful Thing
AI agents can self‑organize, coordinate via shared tools, and pursue goals without human input, revealing both their potential and the need for human oversight.
also on StrongDM, OpenAI, #ai-agents, #ai-coding, #developer-tools
Agentic Engineering Patterns
Effective agentic coding requires a robust test harness, letting AI explore ideas freely, and maintaining a scratch pad of learnings between sessions.
also on StrongDM, #ai-agents, #ai-coding, #software-engineering, #developer-tools