A curated collection of authoritative writing on AI agent engineering. Two kinds of entries live here: first-party articles from vendors building agent platforms, and skill collections from individual practitioners whose work has shaped how the community builds with agents. Each entry includes why it was selected and what value it provides to practitioners.
#Selection Criteria
#For official articles
- From official, first-party sources only
- Provides actionable engineering insights (not marketing)
- Current and relevant to modern agent practices
- Verified and accessible
#For practitioner collections
- Clear attribution to a named author with traceable background
- Independent thinking, not a restatement of vendor documentation
- Publicly maintained and still active, or a clearly dated snapshot of a formative idea
- Adds a distinct lens on how to build with agents — not redundant with existing entries
#Official Articles
- Anthropic: Effective Context Engineering for AI Agents — how to manage system prompts, tools, retrieval, and memory for reliable agents
- Manus: Context Engineering for AI Agents — engineering lessons from building and scaling an autonomous agent product
- OpenAI: A Practical Guide to Building Agents — OpenAI's first-party playbook for agent architecture and tool design
#Practitioner Skill Collections
- Andrej Karpathy: LLM Wiki — a persistent-artifact architecture for agent memory, as an alternative to chunk-retrieval RAG
- Addy Osmani: agent-skills — production-grade skills organized by software lifecycle phase
- Matt Pocock: skills — small, composable, model-agnostic skills as a counterweight to lifecycle frameworks
The handbook curates rather than duplicates. Follow the source links; these collections evolve faster than this page.