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.

Benchmarks measure model or system capability, but their results are only as meaningful as their design — a recurring problem across LLM, multi-agent, and vision tasks, where tests built for one context are routinely applied to contexts they cannot capture.

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.

AI systems that process and generate across multiple input or output modalities, including text, images, video, and audio, now powering everything from local desktop inference to autonomous video production pipelines.

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.