Agent

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：You’ve shipped a working chat feature. Now comes the hard part: figuring out which framework actually fits your production architecture. The tooling landscape has fractured, and comparing AI agent frameworks in a vacuum…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Open-weight model runner Ollama has reintroduced an integration with Claude Desktop that lets users connect Anthropic’s app to models served The post Claude Desktop can now easily run Qwen, DeepSeek and Kimi models — after Ollama’s first effort stalled appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Dive into LangSmith product usage patterns that show how the AI ecosystem and the way people are building LLM apps is evolving.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Reflections on how LangChain has evolved — including our products, ecosystem, and community — over the past two years, and where we're headed next.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：See how Podium tests across the lifecycle development of their AI employee agent, using LangSmith for dataset curation and finetuning. They improved agent F1 response quality to 98% and reduced the need for engineering intervention by 90%.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Our new infrastructure for running agents at scale, LangGraph Cloud, is available in beta. We also have a new stable release of LangGraph.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results across every dimension of the business: 15,000 hours saved annually, 50% reduction in dashboard count, rendering times cut to under 5 seconds, and AI-powered self-service analytics now accessible to every employee.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn how Natera built an automated voice agent on Amazon Bedrock AgentCore that lets patients book mobile phlebotomy appointments through natural conversation. The post covers the dual-WebSocket bridge, event-driven latency masking, and progressive-trust authentication behind 100% tool-calling accuracy and sub-7-second latency.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest and a multi-GPU Stable Diffusion 3.5 LoRA fine-tune, showing how SourceCode syncs your local code into any container at runtime so you can iterate without rebuilding Docker images.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Ox-alpha, the stealth model that quickly became the most popular model on OpenRouter in the last few days, is actually The post Z.ai’s GLM-5.3 Flash is cheap, good, and served on Chinese chips appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The gist Auto Browse, the Gemini 3 agentic mode Google started rolling out in Chrome on 28 January 2026, is rationed: 20 multi-step requests a day on Google AI Pro, 200 a day on AI Ultra. That ration is the most informa…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Build, deploy, and monitor production-grade AI agents at scale with LangChain's enterprise agentic AI platform integrated with NVIDIA.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission. Most MCP demos feature a single server connecting to a single client. For example, you might wire up a Gmail MCP server to Claude Code. It works! It triages your inbox, drafts replies, finds that thing from three […]

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn proven strategies to speed up your AI agent: reduce latency, optimize LLM calls, enable parallelism, and improve UX. Expert tips from LangChain.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：LangGraph Platform, our infrastructure for deploying and managing agents at scale, is now generally available. Learn how to deploy

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In a year of iterative upgrades, Google is introducing smart features that make the base Pixel still the one to buy for most people.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without copying source data. This post covers the architecture, security boundary, and two orchestration models: a code-based Strands agent and a declarative AgentCore harness.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We raised $125M at a $1.25B valuation to build the platform for agent engineering.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We look at Qwen3.8-Flash-Next, Alibaba's open-weight multimodal Mixture-of-Experts model and an early preview of the Qwen4 architecture. We break down where the 180B parameters actually sit: a 125B backbone, a 51B N-gram embedding table, and a 4B multi-token prediction module, with only 6B active per token. We walk through the four architectural changes — the Gated DeltaNet and Qwen Sparse Attention hybrid, Gated Residual, N-gram Embedding, and the Muon optimizer. We also cover the benchmark results, the reported 1/9 training cost against Qwen3.7-Plus, and what self-hosting a 172.78 GiB FP8 checkpoint really demands. The post Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We built WikiBench to test whether generated wikis help coding agents. Pairing a wiki with source code scored higher than source alone, at lower cost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We shouldn't ignore agentic AI ransomware threats, but the claims of a new ransomware group might be skewing the numbers.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Why LangChain believes in open, customizable cognitive architectures over closed systems. Build reliable LLM agents with OpenGPTs and LangSmith.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications. "In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration." That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers. "Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business." Why orchestration is replacing automation as the top CX priority As that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration. "Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records." As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systems Companies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides. Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business. The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms. Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints. That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications. The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences. But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels. "The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless." Making AI a better partner for human agents Effective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow. That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy. "If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems." In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty. Building a unified CX architecture Moving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform. "IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level. At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps. How AI agents will shape the future of CX Customer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time. "The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency." Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions. "Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discover Connery: open-source plugin infrastructure for LLM apps. Secure integrations, personalization, and human-in-the-loop control for AI agents.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Qdrant and LangChain deliver production-ready RAG performance with async support, optimized resource usage, and scalable vector search for LLM apps.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Data-driven-characters is a repo for creating, debugging, and interacting your own chatbots conditioned on your own story corpora.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Access multiple LLMs, embeddings, and AI tools through Eden AI's LangChain integration. Unified API for text generation, OCR, speech-to-text, and more.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discover how developers build LLM applications in 2023. Insights on popular models, vectorstores, retrieval strategies, and testing methods from LangSmith.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Four falsifiable conditions for agentic coding replacing juniors, tested against METR, OpenAI, DORA and Stanford primary source evidence The post What Would Have to Be True for Agentic Coding to Replace Junior Engineers appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Build production-ready AI agents with LangChain. Technical guide covering OpenAI functions, tools, prompts, and architecture for Cal.ai's scheduling assistant.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 14 Commits 14 Commits Folders and files NameName Last commit message Last commi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Auto-evaluate LLM question-answer chains with LangChain's free tool. Generate test sets, grade answers, and optimize chain performance.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Implement OpenAI's proven RAG strategies with LangChain. Explore query transformations, routing, post-processing, and evaluation methods for optimal retrieval.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Explore how LangChain implements autonomous agents like AutoGPT and BabyAGI. Learn about planning techniques, memory systems, and agent simulations.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Intel Crescent Island GPU Render - Image: Intel What kind of hardware do you need for AI processing? Well, every kind, because "AI processing" is a very broad term. Unlike a lot of specialized AI chips (e.g. d-Matrix Ra…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Photo: Steve A Johnson / Pexels Nvidia’s Vera CPU outpaces AMD EPYC 9655P in Linux kernel compilation at Hot Chips 2026 The chipmaker's new Vera CPU, Rubin GPU, and networking stack represent a coordinated bet that agen…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Bruin for Startups Open-source data stack and AI data analyst for early-stage startups. A Bruin engineer onboards you and sets everything up with open-source tools. Run it locally or self-host it, then start analyzing y…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：This article is sponsored by Unframe and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page. Retail has an AI operationalization bottleneck, converting AI investment and experimentation into governed, integrated production capabilities that deliver measurable […]

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The EU AI Act: A Strategic Roadmap for CIOs and CTOs August 26, 2026 · 1,572 words Every CIO and CTO must read this, Not because the EU AI Act is another compliance checkbox to file away with GDPR, but because it is abo…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The AI agent aligns with the move to more AI on local computers.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The rise of generative and agentic AI has dramatically changed the software-building process - and the skills required.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In fact, AI projects are not built by simply choosing a model and feeding it data. Furthermore, a successful AI system goes through multiple stages, starting with identifying the right problem and ending with deployment, monitoring, and continuous improvement. This structured journey is known as the AI Project Cycle. It helps teams move from an […] The post Mastering the AI Project Cycle: From Concept to Production appeared first on Analytics Vidhya.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Glean Technologies Inc. today unveiled Glean Tau, a desktop workspace that connects the company’s enterprise artificial intelligence to a user’s local files, applications and code. The launch anchors a broad slate of product news at Glean:GO, the company’s conference this week in San Francisco. Packaged with it were benchmark numbers aimed at Anthropic PBC. Glean said […] The post Glean unveils Tau desktop workspace, claims token-cost edge over Claude appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：tldr AI agents create and edit PowerPoint by writing code against libraries with serious limitations. Even basic edits end up slow, expensive, and prone to file corruption. We built a PowerPoint API that addresses these…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Everyone's using AI coding agents. Here's how to make yours actually useful.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Prompt Claude, ChatGPT, Gemini, or any other popular large language model (LLM) with a question like “What is the best film ever made?” and the response will vary, and you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as a recent incident where OpenAI could not explain why its advanced pre-release model hacked AI company Hugging Face highlighted—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI ‘thinking’ and outputs has never been greater. Goodfire, an AI lab focused solely on this very problem, recently made its cutting-edge Silico platform, filled with tools to interpret the behavior of AI, generally available to the public. As part of this, the company recently announced a new grant program offering $1 million in free Silico usage for academic and nonprofit interpretability researchers. These efforts aim to democratize AI interpretability, placing techniques previously available to a clutch of elite labs into the hands of ambitious research teams and startups that want to build and understand their own models or adapt open-source models for different purposes. Mechanistic interpretability Founded in 2024 and based in San Francisco, Goodfire aims to provide the tools that build the next generation of safe and powerful AI by understanding the structures inside them instead of treating AI models as black boxes. “Treating models like black boxes isn’t inevitable, it’s a choice,” says Eric Ho, Goodfire co-founder and CEO. “With the right interpretability tools, we can see how models actually work.” The tools Ho refers to are built around a concept called mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by interpreting the model’s weights, activations, and attention patterns, and mapping its neurons and the pathways between them. Mechanistic interpretability tools span the gamut. One approach is mapping a model’s activations in response to controlled prompts, and matching those activation patterns to a set of human-understandable concepts. Another tack is tracking changes in model weights before and after a specific training run in order to spot and understand what changed. Yet another option is changing specific model weights or activations and observing how that affects the model’s output. With Silico, uSilico combines a broad range of these tools, and provides a layer of AI agents to help users understand their model. Users describe what they want to investigate about their AI model in plain language, asking things like ‘Find out when and why my model is hallucinating.’ The platform then autonomously builds an experimental plan involving a host of tasks that can be performed using the various interpretability tools and techniques at its disposal. It then sends out agents to perform these tasks in parallel. Completion of these subtasks should add up to an answer to the original prompt, or at least insights that can be inspected and built upon. Ho says: “In a sense, Silico is like a microscope to peer inside an AI model to understand which parts are responsible for what behavior, and even edit those parts directly.” Understanding Alzheimer’s and AI These tools have already been used to make some impressive advances in a host of fields. In medicine, for instance, Prima Mente, a UK-based AI company, worked with Goodfire to understand its Pleiades epigenetic foundation model. The model performed well at its task of detecting Alzheimer’s disease from blood samples, but the company didn’t know why. “We reverse-engineered Pleiades and found it was using DNA fragment-length patterns to make its predictions—a signal humans hadn’t used to detect Alzheimer’s before,” recalls Ho. In other words, the team had discovered that Pleiades was using a completely new biomarker for the disease. “As far as we know, it’s the first significant finding in the natural sciences discovered purely by reverse-engineering a foundation model,” Ho adds. Elsewhere, Silico is being used to explore deep questions surrounding AI. Cameron Berg, Founder and Director of Reciprocal Research (a New York nonprofit research organization he founded to explore methods of gauging AI cognition), says that Silico almost fell out of the sky at the right time for him and his research. “Silico has been really helpful for operationalizing my research agenda and executing on it way faster than I would have expected,” he says. “ I feel like I have basically become the PI and my research scientists and research engineers are AI systems.” Berg sees general access to Silico and tools like it leading to greater trust in AI’s ability to conduct research tasks, which will accelerate the scientific process across the board. But beyond scientific research, the widespread release of Silico could signal a shift in how AI innovators build, debug, and deploy their models. “I think it’s a mistake to not understand the most consequential technology of our time, particularly given the emergent behavior we’re seeing from increasingly capable AI agents,” says Ho. “If we truly understand how AI models think, instead of discovering and trying to correct their behavior retroactively, we can design them intentionally and shape how models behave to be safer and more reliable.”

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AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Distilling three major AI releases to keep you current.