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.

The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

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.

LLM agents are software systems that pair a language model with tools, memory, and control flow to accomplish multi-step tasks autonomously; the emerging consensus is that reliability requires engineering constraints, not better prompts.

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.