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.

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.

AI safety spans containment of agentic systems, epistemic harms from sycophancy, skill atrophy from unreviewed code generation, and macro-level risks from rapid capability growth — each requiring different mitigations.

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.