Systems where AI agents execute multi-step tasks autonomously, raising interconnected questions about harness architecture, state management, reliability engineering, human oversight, and the organizational context those agents operate within.

Using LLMs as coding collaborators spans a spectrum from inline suggestion to fully autonomous multi-agent pipelines, with active debate about reliability, skill atrophy, security exposure, and what human oversight must remain.

Benchmarks measure model or system capability, but their results are only as meaningful as their design — a recurring problem across LLM, multi-agent, and vision tasks, where tests built for one context are routinely applied to contexts they cannot capture.

LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.