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.

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.

Developer tooling spans the full surface area of software construction — version control, testing, shell ergonomics, AI coding assistants, and platform infrastructure — with a consistent theme: reducing friction without sacrificing correctness or security.

LLM orchestration covers the control structures, harness designs, and coordination patterns that govern how language models are invoked, sequenced, and supervised — whether in single-agent loops or across distributed multi-agent pipelines.