Autonomous systems that plan, act, and verify across tool calls and multi-step workflows, with active debate over architecture choices, coordination costs, memory design, state management, and the governance infrastructure needed to make them reliable.

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.

RAG grounds LLM outputs in external knowledge at inference time; recent work questions when vector similarity retrieval is the right tool and what alternatives — hierarchical indexing, KV caching, compiled wikis — better serve different workloads.

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.