4 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Cursor's research post breaking down where coding-agent inference spend goes in its production traffic (tool outputs, tool definitions, reasoning) and describing changes it made to how agents manage context and choose subagent models. Figures are Cursor's own.
DeepLearning.AI's intermediate Context Engineering course, about 19 hours, taught by Jeff Huber and Drew Breunig. Free to enroll; a certificate requires the paid PRO plan.
Anthropic's developers share tips, playbooks and opinions on building with Claude and Claude Code, including agents, skills, context engineering and prompt caching.
Why I recommend it: Written by the company that makes Claude, so it's practical but not neutral. Best read by people who already use Claude for coding.
A long explainer on what an "agent harness" is — the loop, tools, memory, sandbox, permissions and budget caps wrapped around an AI model — its six core components, and why the wrapper now matters more than which model you pick. Opens with an agent that burned $400 in API calls overnight in a retry loop.
Why I recommend it: Free to read, no sign-up, and the best single walkthrough of the six pieces if you are trying to understand why agents that demo well fall over in real use. Two honest caveats: it is published by a consultancy that sells help with exactly this, so the framing favors building a harness; and the byline on it is not a person I can verify anywhere independently, which on this site means treat the article as a useful explainer rather than a sourced authority. For neutral definitions, the Wikipedia entry on agent harness and Addy Osmani's write-up cover the same ground.
