Agent Harnessing: The Non-Model Infrastructure That Makes AI Agents Actually Work

Everyone building AI agents has access to the same frontier models through the same APIs. What separates production-grade agents from ones that fail silently is the non-model infrastructure surrounding them — the harness. That means a layered memory system that persists state across sessions, a context layer that selects and compresses the right information before it ever reaches the model, control loops with explicit stopping conditions, tool calls structured as code-as-action to keep context lean, independent verification that doesn't trust the agent's self-assessment, and multi-agent coordination protocols that make specialist agents composable without brittle custom bridges. The harness doesn't get the benchmarks. It earns the reliability.