Claude Blog 采集 (2026-05-25)¶
共采集 5 篇文章
📋 文章索引¶
- How enterprises are driving AI transformation with Claude - Oct 01, 2025 (评分: 9.5)
- Eight trends defining how software gets built in 2026 - Jan 21, 2026 (评分: 9.0)
- Evaluate prompts in the developer console - Jul 09, 2024 (评分: 9.0)
- Extending Claude’s capabilities with skills and MCP servers - Dec 19, 2025 (评分: 9.0)
- Fine-tune Claude 3 Haiku in Amazon Bedrock - Jul 10, 2024 (评分: 9.0)
How enterprises are driving AI transformation with Claude¶
来源: Claude Blog 发布日期: Oct 01, 2025 采集时间: 2026-05-25 价值评分: 9.5/10 正文字数: ~11358 字符
摘要¶
As the leader in enterprise AI, Anthropic builds state-of-the-art models specializing in industries where precision matters, from coding and cybersecurity to financial services. Claude Sonnet 4.
正文内容¶
As the leader in enterprise AI, Anthropic builds state-of-the-art models specializing in industries where precision matters, from coding and cybersecurity to financial services. Claude Sonnet 4.5 , our latest release, delivers measurable results—reducing vulnerability response time by 44% for security organizations like HackerOne, enabling investment-grade financial analysis at institutional firms like NBIM, and helping developers at Netflix and GitHub tackle complex, codebase-spanning tasks with unprecedented accuracy. Here's how five leading organizations are using Claude to drive transformation across their businesses. How Novo Nordisk drives breakthrough drug development Pharmaceutical development moves at the speed of documentation—and for Novo Nordisk, creator of Ozempic, that documentation was a critical bottleneck. Clinical study reports can run 300 pages, and staff writers averaged just 2.3 reports annually. Each day of delay costs up to $15 million in potential revenue. More importantly, patients with chronic conditions wait longer for treatments that could transform their lives. After benchmarking AI providers on clinical data accuracy, Novo Nordisk built NovoScribe—an AI-powered documentation platform using Claude models on Amazon Bedrock, Claude Code, and MongoDB Atlas. The platform combines semantic search with domain expert-approved text to produce regulatory-grade documentation that consistently earns positive feedback from regulators. The transformation was immediate: documentation that took 10+ weeks now takes 10 minutes —a 90% reduction in writing time. Device verification protocols that previously required entire departments need just one user. Review cycles dropped 50% as quality improved. The team expanded NovoScribe beyond clinical study reports to device protocols and patient materials, generating complete study booklets in under a minute—work that previously took months with external agencies. With Claude Code, even non-technical team members prototype features in hours instead of weeks, enabling their 11-person development team to avoid scaling up while dramatically expanding capabilities. "In a highly regulated industry, we can't just throw our data into a large language model and hope for the best," said Waheed Jowiya, Digitalization Strategy Director at Novo Nordisk. "Our conversations with Anthropic guided us on how to securely use Claude for planning, strategic tasks, and code generation." How Cox Automotive personalizes the car-buying experience Cox Automotive , the world's largest automotive services and technology provider, needed to transform the car-buying experience across its dealer network. The company integrated Claude across VinSolutions CRM, Autotrader PSX, and Dealer.com—choosing Claude for its performance across three critical metrics: latency, cost, and accuracy. They leverage Sonnet for complex tasks requiring deeper comprehension and Haiku for high-volume, rapid-response scenarios. The impact was immediate. Through VinSolutions CRM, consumer lead responses and test drive appointments more than doubled. AI-generated vehicle listings achieved 80% positive feedback from sellers. The managed services platform compressed dealer website content creation from weeks to same-day deliverables, generating over 9,000 client deliverables to date. "Claude consistently ranks among the most advanced generative AI models available, and continues to push innovation forward with each update," said Ben Flusberg, Chief Data Officer at Cox Automotive. Palo Alto Networks accelerates secure software development For Palo Alto Networks , the world's largest cybersecurity company, the challenge was clear: developers spent 30-35% of their time in initial development—precisely where the most critical bugs emerged. New developers took months to understand and contribute to complex codebases. As a cybersecurity leader, they needed an AI solution that prioritized safety and security while accelerating their global engineering organization. After evaluating multiple providers, they chose Claude on Google Cloud's Vertex AI for its coding performance, security standards, and seamless integration. The results: a 20-30% increase in feature development velocity and dramatically reduced onboarding time—from months to weeks. They've onboarded 2,500 developers to work with Claude, with plans to reach 3,500. Junior developers with no prior knowledge of complex products completed integration tasks 70% faster with Claude's assistance. The company is now pioneering an AI post-processing system in CI/CD that automatically improves variable names, adds comments, and generates unit tests. "Anthropic prioritized safety and security a lot more than other LLMs," said Gunjan Patel, Director of Engineering at Palo Alto Networks. "They discuss security implications in every meeting. As the largest cybersecurity company, that's a big deal for us." Salesforce powers autonomous AI agents Enterprises increasingly need AI systems that can do more than assist—they need autonomous agents that can reason through complex business scenarios, make decisions, and take action across systems without constant human intervention. Salesforce integrated Claude models to power Agentforce Agents through Einstein 1 Studio, creating a platform where AI can plan and execute on behalf of employees and customers. All interactions with Claude flow through Salesforce's secure AI systems, with safeguards like dynamic grounding and toxicity detection through the Einstein Trust Layer ensuring responsible AI use even in highly regulated industries. Salesforce customers are now deploying autonomous agents that orchestrate complete workflows end-to-end: analyzing customer data to identify opportunities, executing transactions, and updating records across multiple platforms—all without human intervention. This also represents a fundamental shift from AI-as-assistant to AI-as-autonomous-collaborator. Developers can customize agents for industry-specific use cases across sales, marketing, and customer service, with the flexibility to choose Claude models that balance intelligence, speed, and cost for each specific workflow. "Through our partnership with Anthropic, customers gain the flexibility to integrate their own LLMs, introducing Claude models with diverse levels of intelligence, speed, and cost-effectiveness," said Kaushal Kurapati, Senior Vice President of Product for AI at Salesforce. "This empowers users to tailor their CRM applications to their unique requirements." How IG Group accelerates marketing, analytics, and global operations When IG Group , a global leader in online trading, tested multiple AI providers, Claude consistently outperformed competitors across their most demanding use cases. The Data and AI transformation team deployed Claude strategically: automating complex analytics workflows, helping HR managers generate consistent performance feedback across regions, and enabling marketing teams to produce multilingual content while navigating strict regulatory requirements. The results exceeded expectations. Analytics teams now save 70 hours weekly , redirecting that capacity toward higher-value strategic work. In certain use cases, productivity doubled . Marketing achieved triple-digit speed-to-market improvements while reducing agency dependency. The company hit full ROI within three months. "Anthropic is the only generative AI company that delivered [results] on time, all the time," said Olga Pirog, Global Head of Data and AI Transformation at IG Group. "For an organization driving business transformation, having a reliable partner is priceless." The evolution of enterprise AI with Claude From pilot programs to production-scale deployments, enterprises are moving beyond experimentation to embedding Claude into their core operations and customer-facing products. The companies seeing real impact share a few characteristics: They start with concrete business problems. Rather than deploying AI for its own sake, they target specific bottlenecks—analyst workflows that consume hours of manual work, documentation backlogs that slow product launches, or onboarding processes that take months instead of weeks. They invest in people, not just technology. Comprehensive training programs and champion networks help employees integrate AI into their daily work. When teams understand not just how to use AI but why it matters to their specific role, adoption accelerates organically. Learn more about how enterprises are using AI and its impact on their work in our Economic Index . They measure what matters. Tracking concrete metrics—productivity gains, time savings, quality improvements—turns impressive demos into defensible business cases. This evidence-based approach ensures continuous refinement and proves ROI to stakeholders. They build for scale from day one. Integration, security, compliance, and trust aren't afterthoughts. Treating AI as an enterprise transformation rather than a technology experiment leads to faster adoption and more sustainable outcomes. Organizations like Novo Nordisk, IG Group, Palo Alto Networks, Cox Automotive, and Salesforce are pioneering this approach with Claude, expanding use cases across teams, building agentic systems that reshape workflows, and delivering true AI transformation. Research-driven reliability at enterprise scale This enterprise transformation is made possible by Anthropic's foundational research in AI safety, interpretability, and alignment. Our work in mechanistic interpretability —understanding how AI systems reason and make decisions—enables us to build models that are not just powerful, but predictable and auditable. Through alignment research , we've developed techniques that make Claude inherently steerable, allowing organizations to align AI behavior with their specific values and requirements without extensive fine-tuning. For enterprises deploying autonomous agents in regulated industries or mission-critical operations, this research translates directly into operational confidence. The result is an AI partner that enterprises can trust with increasingly autonomous responsibilities, knowing that the same research rigor underlying Claude's capabilities also ensures its safety and controllability at scale. Getting started on your enterprise AI journey Whether you're just beginning to explore AI or already deploying agents at scale, Anthropic provides the resources to help you succeed: How Anthropic teams use Claude Code: Learn how our own teams leverage AI to accelerate development, with practical examples and implementation strategies. Customer case studies : More stories of how organizations across financial services, healthcare, cybersecurity, and other industries are driving transformation with Claude. Anthropic Academy : Access our AI Fluency and Building Agents courses to develop the skills your team needs to work effectively with AI. Contact our Sales team to learn more.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Eight trends defining how software gets built in 2026¶
来源: Claude Blog 发布日期: Jan 21, 2026 采集时间: 2026-05-25 价值评分: 9.0/10 正文字数: ~3055 字符
摘要¶
How engineering teams are shifting from writing code to orchestrating agents. Eight trends, real-world case studies, and predictions for 2026.
正文内容¶
How is AI changing the way software gets built—and what should engineering leaders expect in 2026? We analyzed the patterns emerging across the industry.
Coding agents are now collaborators. In 2025, engineering teams discovered AI could handle entire implementation workflows: writing tests, debugging failures, navigating complex codebases. In 2026, we predict these capabilities will expand significantly. Our new report identifies eight trends we predict will define agentic coding this year, organized into three categories: foundation trends that change how development happens, capability trends expanding what agents accomplish, and impact trends affecting business outcomes. The organizations pulling ahead aren't removing engineers from the loop, they're making engineer expertise count where it matters most. What we're seeing The software development lifecycle is undergoing one of its most significant changes since the graphical user interface. Engineers are shifting from writing code to coordinating agents that write code, focusing their own expertise on architecture, system design, and strategic decisions. A critical nuance has emerged from studying how developers actually work with AI: this transformation relies on active collaboration. Research from our Societal Impacts team reveals that while developers use AI in roughly 60% of their work, they report being able to "fully delegate" only 0-20% of tasks. AI serves as a constant collaborator, but using it effectively requires supervision, validation, and human judgment. What this looks like in practice Organizations across industries are putting these patterns into practice, balancing agent autonomy with human oversight to ship faster without sacrificing quality. Rakuten engineers tested Claude Code on a complex technical task: implementing an activation vector extraction method in vLLM, a 12.5-million-line codebase. Claude Code finished the job in seven hours of autonomous work, achieving 99.9% numerical accuracy. TELUS teams created over 13,000 custom AI solutions while shipping engineering code 30% faster, saving over 500,000 hours total. Zapier achieved 89% AI adoption across their entire organization with 800+ agents deployed internally. The path forward For organizations planning their 2026 priorities, four areas demand immediate attention: mastering multi-agent coordination, scaling human-agent oversight through AI-automated review, extending agentic coding beyond engineering teams, and embedding security architecture from the earliest stages. Organizations that treat agentic coding as a strategic priority will define what becomes possible. Read the full 2026 Agentic Coding Trends Report here .
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Evaluate prompts in the developer console¶
来源: Claude Blog 发布日期: Jul 09, 2024 采集时间: 2026-05-25 价值评分: 9.0/10 正文字数: ~1442 字符
摘要¶
Generate, test, and evaluate prompts directly in the Anthropic Console with automatic test case generation and side-by-side output comparison. When building AI-powered applications, prompt quality significantly impacts results.
正文内容¶
Generate, test, and evaluate prompts directly in the Anthropic Console with automatic test case generation and side-by-side output comparison.
When building AI-powered applications, prompt quality significantly impacts results. But crafting high quality prompts is challenging, requiring deep knowledge of your application's needs and expertise with large language models. To speed up development and improve outcomes, we've streamlined this process to make it easier for users to produce high quality prompts. You can now generate, test, and evaluate your prompts in the Anthropic Console. We've added new features, including the ability to generate automatic test cases and compare outputs, that allow you to leverage Claude to generate the very best responses for your needs. Generate prompts Writing a great prompt can be as simple as describing a task to Claude. The Console offers a built-in prompt generator , powered by Claude 3.5 Sonnet, that allows you to describe your task (e.g. “Triage inbound customer support requests”) and have Claude generate a high-quality prompt for you.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Extending Claude’s capabilities with skills and MCP servers¶
来源: Claude Blog 发布日期: Dec 19, 2025 采集时间: 2026-05-25 价值评分: 9.0/10 正文字数: ~4454 字符
摘要¶
Learn how skills and MCP work together to build agents that follow your workflows and use external tools effectively. Best practices and real-world examples.
正文内容¶
Learn how skills and MCP work together to build agents that follow your workflows and use external systems and platforms effectively.
Update: We've published Agent Skills as an open standard for cross-platform portability. (December 18, 2025) Since launching Skills , two of the biggest questions we’ve heard from customers are: "How do skills and MCP work together? When should I use one versus the other?" Model Context Protocol (MCP) connects Claude to third-party tools, and skills teach Claude how to use them well. When you combine both, you can build agents that follow your team’s workflows, not generic processes that require constant correction. For example, an MCP connection to Notion lets Claude search your workspace. Add a skill for meeting prep, and Claude knows which pages to pull from, how to format the prep document, and what your team’s standards are for delivering meeting notes. The connection becomes useful instead of just available. In this article, we break down the relationship between skills and MCP, how to combine them to build agents that follow your workflows to produce consistent outputs, and walk through a few real-world examples of how they work together in practice. Understanding skills and MCP You walk into a hardware store looking to fix a broken cabinet. The store has everything you need (wood glue, clamps, replacement hinges) but knowing what items to buy and how to use them is a different problem. MCP is like having access to the aisles. Skills, meanwhile, are like an employee's expertise. All the inventory in the world won't help if you don't know which items you need or how to use them. A skill is like the helpful employee who walks you through the repair process, points you to the right supplies, and shows you proper technique. Put more concretely, an MCP server gives Claude access to your external systems, services, and platforms, while skills provide the context Claude needs to use those connections effectively, teaching Claude what to do now that it has this access. Without the context that skills provide, Claude has to guess at what you want. With a skill, Claude can follow your playbook instead. Why skills and MCP work well together MCP handles connectivity: secure, standardized access to external systems. Whether you're connecting to GitHub, Salesforce, Notion, or your own internal APIs, MCP servers give Claude the ability to reach your tools and data. Skills handle expertise: the domain knowledge and workflow logic that turn raw tool access into reliable outcomes. A skill knows when to query your CRM, what to look for in the results, how to format the output, and which edge cases require different handling. This separation keeps the architecture composable. A single skill can orchestrate multiple MCP servers, while a single MCP server can support dozens of different skills. Add a new connection, and existing skills can incorporate it. Refine a skill, and it works across all your connected tools. When you combine skills and MCP, you get: Clear discovery : Claude stops guessing where to look. A meeting prep skill might specify: check the project page first, then previous meeting notes, then stakeholder profiles. A research skill might say: start with the shared drive, cross-reference against the CRM, then fill gaps with web search. The skill encodes institutional knowledge about which sources matter for which tasks. Reliable orchestration : Multi-step workflows become predictable. Without a skill, Claude might pull data and format it before checking whether it has everything. Skills define the sequence explicitly, so Claude executes the workflow the same way every time. Consistent performance : Outputs actually meet standards. Generic results need editing. Skills define what "done" looks like for your team: the right structure, the right level of detail, the right tone for your audience. Over time, teams build up collections of interrelated skills and connections that give Claude expertise in their specific domain. Further reading : Tim O'Reilly on what MCP and skills mean for open source AI
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Fine-tune Claude 3 Haiku in Amazon Bedrock¶
来源: Claude Blog 发布日期: Jul 10, 2024 采集时间: 2026-05-25 价值评分: 9.0/10 正文字数: ~490 字符
摘要¶
Claude 3 Haiku can now be fine-tuned in Amazon Bedrock with custom training data, enabling faster, more accurate performance at lower cost. Update: Fine-tuning Claude 3 Haiku in Amazon Bedrock is generally available.
正文内容¶
Claude 3 Haiku can now be fine-tuned in Amazon Bedrock with custom training data, enabling faster, more accurate performance at lower cost.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集