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.

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.

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.

Open source spans infrastructure, tooling, security risk, and platform trust — the cited sources collectively show it as a foundation for local AI, developer tooling, and code forges, with its benefits shadowed by real supply-chain and stewardship threats.

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.