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.

The infrastructure, utilities, and integration layers built around large language models, spanning local inference runtimes, context management, MCP servers, knowledge organization, and provider-agnostic design patterns.

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.

Software architecture shapes how systems behave under pressure, how teams reason about codebases, and how much complexity accumulates over time — spanning module design, state management, deployment topology, and the feedback loops that keep all three honest.