Building an Advanced Agentic Harness
This article proposes a structured 'harness' for AI agents, moving beyond simple loops to a system that includes planning, verification, and memory management. It advocates for a modular, composition-based approach to make LLM-driven agents reliable and production-ready.
Why it matters
It provides a blueprint for building more robust and dependable AI systems, which is critical for enterprise-level automation.
That Basic Harness loop is correct, but naive . A lone pilot in a well-built jet might win a dogfight, but nobody runs an air campaign that way. Real operations add mission planners who decide what sorties to fly before anyone takes off, squadrons that fly independent sorties in parallel, fuel budgets and bingo calls that force a return to base before the tanks run dry, flight recorders that make every mission reconstructible after the fact, and after-action reviews that decide whether the mission actually succeeded. None of these replace the pilot. They wrap the pilot in structure so that the whole system stays fast, safe, debuggable, and measurable.
Technical engineering guide focused on system architecture.
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