harness-engineering

# Harness Engineering Design the system *around* AI agents for reliable, safe production use. Harness Engineering protects the quality ceiling of Agentic Engineering. It turns fast agent output into controlled execution through permissions, observability, recovery, and adversarial validation. ## Agent Runtime Model Treat the agent harness as the kernel around an LLM OS: | Kernel concern | Agent harness responsibility | |---|---| | Memory management | Curate context, summarize bulky outputs, persist state to wiki/logs. | | Syscall boundary | Expose tools with contracts, allowlists, deny rules, and retries. | | Process isolation | Separate write scopes, sandboxes, credentials, and state per agent. | | Scheduling | Decide sequential, parallel, or event-driven execution. | | Interrupts | Stop, ask approval, rollback, or route to a safer action. | | Observability | Log tool calls, decisions, outputs, costs, and verification evidence. | | Garbage collection | Close idle agents, remove stale tasks, compact context, and record risks. | ## Productized Agent Harness Google I/O '26 added a practical pressure test for harness design: the same runtime pattern now appears in developer tools, personal agents, search, commerce, generative media, and smart glasses. | Product surface | Harness control that must exist | |---|---| | Agent-first IDE | task queue, subagent ownership, hooks, sandbox, test proof | | Personal agent | user mandate, memory scope, tool allowlist, resumable log |...