I just love the language of this study...it speaks of the shifting 'community language'...and that is so true...have you noticed the new 'co…

I just love the language of this study...it speaks of the shifting "community language"...and that is so true...have you noticed the new "community language" is "harness", it was "contextual prompting" before that... For now, the center of gravity in AI agents has shifted — and this diagram captures it perfectly. Think of LLM agent capabilities as three stacked layers: Weights ... Where it all started. Pretraining, fine-tuning, RLHF, scaling laws, alignment. This was the 2022 conversation. Context ... The 2023-2024 wave. RAG, memory, long context, chain-of-thought, prompting, and context engineering became the focus. How do we get the right information to the model? Now, Harness ... Where the conversation lives now. MCP, tool ecosystems, function calling, agent infrastructure, protocols, skills, A2A, multi-agent orchestration, workflow graphs, and security. The pattern? Community attention has moved steadily outward, from what's inside the model, to what surrounds it, to the infrastructure that connects and orchestrates it all. The models themselves are becoming a commodity. The differentiation is increasingly in the harness layer, how you wire agents together, what tools they can use, and how they coordinate. We've gone from "how do we make the model smarter?" to "how do we make the system around the model smarter?" That's the real shift. Source: https://arxiv.org/pdf/2604.08224 This aligns with an excellent blog from @hwchase17 ➡️https://x.com/hwchase17/status/2042978500567609738

Source: Harrison Chase (X) | 2026-04-12

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