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.

The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

Enterprise software sits at the intersection of organizational process, vendor ecosystems, and institutional trust, with recent sources highlighting governance pressures from AI adoption, supply chain risk, onboarding dysfunction, and the fragility of human-scale loyalty.

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.

MCP is an open protocol for exposing tools and context to AI agents; sources debate whether it belongs in developer workflows or enterprise governance layers, while implementations range from code intelligence servers to token-compression proxies.