🛡️ Quality across every Accory product. From material selection to manufacturing and inspection, we focus on consistency, durability and reliable performance across our product range. 📧 wcong0706@gmail.com #shunboplastic# #qualitycontrol# #manufacturing#

Do you know what really affects the cost and quality of jewelry? 💎 Two pieces may look almost identical, but differences in materials, stones, polishing, plating, and workmanship can lead to very different prices and quality. So how do we make sure the quality stays consistent when working with different factories? It all starts with the right samples and clear quality standards. 👆 Got more questions about CJ jewelry sourcing? Let us know below! #cjdropshipping# #dropshipping# #ecommerce# #jewelry# #jewelrybusiness# #jewelrysourcing# #productsourcing# #qualitycontrol# #ecommercetips# #onlineseller# #ecommerceseller#

Great blog from Kevin Lu at @vllm_project about quality control at the inference engine. Unfortunately, @AnushElangovan has not provided enough stable AMD QA clusters to vLLM, which leads to an order of magnitude worse software quality on AMD versus CUDA. Multiple AMD CI vLLM fleet-wide outages happen every month.

🛒 Freshness is the ultimate differentiator in grocery. Read how Albertsons Companies is taking produce quality control to the next level with its new Intelligent Quality Control tool, built on the Gemini Enterprise Agent Platform →

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 How you orchestrate multiple agents determines the quality and reliability of your entire system. Master LLM-driven and code-driven patterns to build the right architecture for each use case. 📌 Title: Agent orchestration 🔗 URL: 🧩 Overview Multi-agent orchestration comes in two flavors: LLM-driven (the model dynamically decides routing via handoffs) and code-driven (Python code explicitly controls the flow). Combined with the Manager pattern (`as_tool`) and handoff-based delegation, these patterns let you build anything from open-ended research assistants to deterministic content pipelines. 🛠 Usage For LLM-driven orchestration, define `Agent(name="researcher", handoffs=[web_search_agent, code_exec_agent])` and let the model dynamically choose. For code-driven pipelines, define `researcher`, `outliner`, `writer`, and `critic` agents, then in `blog_pipeline(topic)` call `await input=...)` followed by `await input= + [...])` and `await input=...)` sequentially. The generate-evaluate loop uses `for i in range(3)` to have the `critic` review, breaking if `"no issues"` is found, otherwise sending feedback back to `writer`. For parallel execution, use `asyncio.gather(*[ input=data) for a in agents])` to run sentiment, topic, and summary analyses concurrently. The Manager pattern uses `Agent(name="manager", tools=[ to call child agents as tools while retaining control. 🏗 Practical Patterns **LLM-Driven — Open-Ended Tasks** Equip a research agent with web search, file search, code execution, and specialist handoffs. The LLM dynamically selects the best approach based on context. Ideal for "investigate this technology" type queries where you can't predetermine the flow. **Code-Driven — Deterministic Pipelines** When steps are known upfront (research, outline, write, critique, improve), define the pipeline explicitly in Python. Each step's output can be programmatically validated and branched, giving you much tighter quality control. **Generate-Evaluate Loop** Use a `while`/`for` loop to repeatedly generate and evaluate until quality criteria are met. A critic agent reviews output until it passes — achieving quality that single-shot generation can't match. Always set a maximum iteration count. **Parallel Execution with asyncio.gather** Run independent tasks (sentiment analysis, topic extraction, summarization) concurrently with `asyncio.gather`. Dramatically reduces latency compared to sequential execution. **Manager Pattern (as_tool) vs Handoff** `as_tool` keeps the Manager in control — it calls child agents as tools and gets results back to synthesize. Use this when you need to combine outputs from multiple specialists. Handoffs transfer control entirely — use this when the specialist should handle the rest of the conversation independently. 💡 Use Cases 🔬 Research investigation (LLM-driven with dynamic web/paper/code analysis selection) 📝 Content creation pipeline (code-driven: research, write, review, improve) ⚡ Parallel data analysis (asyncio.gather for sentiment/topic/summary) 👔 Manager pattern (synthesize analysis team results into a unified report) ⚠️ Caveats - LLM-driven patterns are flexible but may produce unnecessary handoffs or tool calls due to model judgment errors. Use code-driven control for critical flows. - Always set a maximum iteration count on generate-evaluate loops. Unbounded loops cause runaway token costs. - When using `asyncio.gather` for parallel execution, consider `return_exceptions=True` so one task's failure doesn't cancel others. - Child agents called via `as_tool` return results as text to the Manager. Specify `output_type` if you need structured data. ✨ Combine LLM-driven and code-driven orchestration to build the optimal multi-agent architecture for each use case! #OpenAIAgentSDK# #AIAgent#

Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.