@rhymeleon: When I first skimmed through it, I only got a rough idea. It wasn't until I delved deeper into agents recently, combined with some questions from interviewers, that I truly realized the value of this article. The article provides in-depth explanations of agent loops, memory mechanisms, harness engineering, and agent evaluation. Highly recommended for anyone looking to get a thorough understanding.
Summary
User recommends an article that delves into agent loops, memory mechanisms, harness engineering, and agent evaluation, highlighting its substantial value for readers who are studying agents in depth.
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@fliaping: Full blog version: https://blog.fliaping.com/domain-agent-harness-engineering/… Building a good vertical-domain Agent requires connecting base model selection, memory, permissions, execution recovery, and evaluation. I've written a long-form engineering piece discussing R…
A long-form engineering article discussing harness design for vertical-domain Agents: how to integrate project knowledge, workspaces, identity authentication, and long-task recovery on top of a Deep Agents foundation, and how to iterate on adding or removing capabilities based on evaluation results.
@hylarucoder: The best agent infrastructure article I've seen recently
Recommend an excellent article about AI agent infrastructure.
@vintcessun: Tonight I came across a learning roadmap project that redefined where to start learning Agent. I used to think Agent was just a pile of tools and frameworks, but its core is the "observe-think-execute" loop and the harness engineering's organization of permissions, state, and backtracking. It breaks down learning into building a minimal Agent loop from scratch all the way to deploying a real Agent, with 8 stages, each with clear deliverables and recommended resources — not just links but an actionable todo list. This systematic approach made me realize my previous learning was too fragmented.
An open-source learning roadmap project called Agent-Learning-Hub, which breaks down AI Agent learning into 8 stages from building a minimal Agent loop to production deployment, providing executable todo lists and recommended resources, maintained by members of the Datawhale community.
@yibie: Recommended: A multi-agent system design guide by LangChain CEO Harrison Chase. The core argument is extremely simple but rarely made clear: multi-agent systems can be divided into "reading" and "writing" — reading is easy to parallelize, writing is prone to conflicts. That's why Anthropic's res…
Recommending Harrison Chase's blog post on multi-agent system design. The key insight is that multi-agent systems split into 'reading' and 'writing' tasks; reading is easy to parallelize while writing leads to conflicts. It also summarizes suitable and unsuitable scenarios and engineering prerequisites.
@yanhua1010: Are you also confused by the many concepts of AI Agent (Harness, Scaffold, Context Engineering...)? Recently, I saw an article from Huggingface in the @teach_fireworks teacher community explaining Agents...
Recommending a Huggingface article that explains common terms of AI Agent to help clarify concepts like Harness, Scaffold, Context Engineering, etc.