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.

Discrete software tools that extend what practitioners can build, debug, deploy, or understand, spanning LLM fine-tuning, CI orchestration, documentation, security scanning, Kubernetes management, and more.

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.

Attackers exploit the trust placed in shared code infrastructure, from invisible Unicode payloads in npm packages to self-propagating credential stealers, while defenses range from commit signing to agentic vulnerability scanning.