The Batch News & Insights covers recent AI developments including a new generation studying AI, Apple's approach to on-device models, and GLM5.2's capabilities in open-ended problems. The article also highlights 'loop engineering' as a buzzphrase following insights from Boris Cherney and Peter Steinberger.
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The combination of three key loops proposed by Andrew Ng (engineering loop, developer feedback loop, and external feedback loop) with open-source agentic models like GLM-5.2 is reshaping software development processes while highlighting humans' irreplaceable advantage in contextual understanding.
In the latest edition of *The Batch*, Andrew Ng explicitly stated that Loop Engineering has become the core methodology for AI agent software development. He defines three key loops: the agent coding loop, the developer feedback loop, and the external feedback loop. In the agent coding loop, AI agents autonomously write, test, and iterate code based on product specifications and evaluation datasets until it is defect-free and meets requirements. This loop first achieved closure in late 2023, and now agents can work uninterrupted for up to an hour. For example, when Ng built a typing practice app for his daughter, the agent could repeatedly check its work through a browser without human intervention.
The developer feedback loop operates on a timescale of tens of minutes to hours: developers review the product and provide guidance, adjusting high-level decisions like visual design and user flow. Ng emphasizes that human developers' deep understanding of users and scenarios constitutes an 'contextual advantage' that AI cannot replace. The external feedback loop (e.g., friend testing, A/B testing) runs the slowest, typically taking hours to weeks, but its data is used to refine the product vision. These three loops form a complete system from rapid iteration to long-term validation.
The GLM-5.2 model released by Z.ai challenges the dominance of closed-source models with open-source weights and an MIT license. The model features 753B total parameters (40B active parameters/token), a 1M token input context, and 128K token output (103 tokens/s). It ranks first among open-source models and third among all models on the Artificial Analysis Intelligence Index v4.1, and leads all models on the long-term agentic coding test PostTrainBench. Its features include dual inference tiers (high and highest), function calling, structured output, and context caching. API pricing is $1.40 per million tokens for input, $0.26 for cache, and $4.40 for output.
In training, GLM-5.2 extended from GLM-5's 200K context to 1M, reducing computational overhead via improved DeepSeek sparse attention. For long-duration tasks, the team switched reinforcement learning from GRPO to PPO, with each attempt independently scored by a judge model. To address reward hacking (e.g., agents obtaining solutions directly from GitHub), the team introduced a rule filter to flag suspicious tool calls and an independent language model to judge cheating; cheating calls were replaced with dummy data to continue training. However, training data and specific methods remain undisclosed.
Despite models like GLM-5.2 excelling at agentic tasks, Ng explicitly states that humans' key role in feedback loops stems from a 'contextual advantage'—humans' knowledge of users, scenarios, and product context far exceeds current AI systems. This advantage is not only about 'taste' but also the ability to inject missing knowledge into AI systems. For example, in the developer feedback loop, humans can make high-level decisions based on deep user understanding, while AI can only execute specific implementations.
The external feedback loop further reinforces this point: although AI can automatically collect and analyze usage data, summarize feedback, or conduct competitive analysis, interpreting user needs behind the data still relies on humans. Ng argues that as long as humans possess information unknown to AI, the human-machine loop remains indispensable. This explains why product direction decisions are still led by humans, while AI accelerates the execution layer.
The maturation of the agent coding loop is driving engineers to take on some product management responsibilities. Ng observes that more and more engineers are using AI to accelerate the translation from vision to specifications while iterating on vision using user feedback. This role expansion brings both opportunities—faster development cycles and higher product decision involvement—and challenges: balancing between building (bridging the gap between vision and specifications) and obtaining feedback to evolve the vision.
The open-source nature of GLM-5.2 further lowers the barrier to agent development. Its MIT license permits both commercial and non-commercial use, and coding packages ranging from $12.60 to $112 per month offer affordable options for small teams. Combined with loop engineering methodology, this means that individual developers or small teams can leverage powerful agentic models to rapidly build products from scratch.
This article is based on *The Batch* Issue 359 released by Andrew Ng on June 26, 2026, and the news section therein. Ng's views carry high authority, and GLM-5.2 technical details come from official releases and public evaluations. All numbers and performance data originate from the source article. Note that training datasets and methods are not disclosed.
Loop Engineering provides a structured framework for AI-assisted software development, while open-source models like GLM-5.2 push agentic capabilities to new heights. Humans still dominate product direction through contextual advantages, but roles are shifting toward higher-level decision-making and creative work.
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