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.

Context engineering is the practice of deliberately constructing what an LLM receives in its context window — structuring, compressing, persisting, and retrieving information so agents produce reliable output across tasks and sessions.

Developer tooling spans the full surface area of software construction — version control, testing, shell ergonomics, AI coding assistants, and platform infrastructure — with a consistent theme: reducing friction without sacrificing correctness or security.

MCP is an open protocol for exposing tools and context to AI agents; sources debate whether it belongs in developer workflows or enterprise governance layers, while implementations range from code intelligence servers to token-compression proxies.