Harrison argues that enterprises must own all three components of agent intelligence — model, context, and harness — with the harness as the critical orchestration layer. He presents a spec…

All AI agents share a core LLM-loop architecture, but competitive differentiation comes from customizing the harness via middleware (hooks, plugins) for domain-specific needs like memory, s…

Harbor, an open-source eval runner from the Terminal Bench 2 team, is becoming the industry standard for defining domain-specific benchmarks. It enables sandboxed, parallel task execution w…

Harbor, an open-source eval framework from the Terminal Bench creators, is becoming the industry standard for defining sandboxed agent tasks with verifiers, enabling systematic comparison o…

Harrison reveals that financial services customers reject general-purpose agents (like Deep Agents) as 'too scary' and require custom cognitive architectures with explicit gates and checks…

Regulated enterprises require deterministic, controllable agent flows — favoring bespoke cognitive architectures with explicit gates over flexible general-purpose harnesses like Deep Agents…