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aws

Top announcements of the What’s Next with AWS, 2026 | Amazon Web Services (opens in new tab)

The 2026 “What’s Next with AWS” event focused on how AI agents are reshaping business operations. Major announcements included Amazon Quick, an AI work assistant; four specialized Amazon Connect solutions; and an expanded AWS–OpenAI partnership bringing OpenAI models and Codex to Amazon Bedrock. Together, these offerings emphasize integrated agents that can connect to existing systems, make decisions, and execute tasks within enterprise-controlled infrastructure. ## Amazon Quick Becomes a Broader AI Work Assistant - Amazon Quick connects to workplace information, learns user preferences, and takes action on users’ behalf. - A new desktop app, currently in preview, can access local files, calendars, and communications without requiring a browser. - Free and Plus plans are available without an AWS account. Users can register with a personal email or Google, Apple, GitHub, or Amazon credentials. - Quick can generate documents, presentations, infographics, and images directly within chat. - New native integrations include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams. ## Amazon Connect Expands into Four Agentic AI Products AWS is repositioning Amazon Connect as a portfolio of solutions for specific business workflows: - **Amazon Connect Decisions:** A supply-chain planning and intelligence platform using AI teammates, Amazon’s operational expertise, and more than 25 specialized tools to support proactive planning. - **Amazon Connect Talent:** A hiring solution in preview that provides AI-led interviews, science-backed assessments, and standardized evaluations for large-scale recruiting. - **Amazon Connect Customer:** The renamed customer-experience product, supporting voice, chat, and digital channels. New configuration tools aim to let organizations deploy conversational AI in weeks rather than months. - **Amazon Connect Health:** Automates patient verification, appointments, patient insights, ambient documentation, and medical coding to improve access to care and reduce administrative workloads. ## AWS and OpenAI Expand Their Partnership The companies announced several limited-preview offerings that bring OpenAI capabilities into AWS environments: - **OpenAI models on Amazon Bedrock:** Models including GPT-5.5 and GPT-5.4 will be accessible through existing Bedrock APIs, with AWS security, governance, and cost controls. - **Codex on Amazon Bedrock:** Organizations can run OpenAI’s coding agent using AWS credentials and infrastructure, with usage counting toward AWS cloud commitments. Initial access includes the Codex CLI, desktop app, and Visual Studio Code extension. - **Bedrock Managed Agents powered by OpenAI:** This service combines OpenAI models with AWS-managed infrastructure and the OpenAI harness for building production-ready agents capable of reasoning through long-running tasks. AWS’s announcements point toward a future in which AI agents are embedded directly into workplace tools, operational systems, customer-service platforms, and cloud development environments. Organizations looking to adopt these capabilities should evaluate the available previews, integrations, governance controls, and workflow fit before moving to production.

ai-assistantawscloud-infrastructureopenai+4
line

ODW #3: Boosting Development Efficiency by Safely Utilizing MCP Servers (opens in new tab)

LY Corporation is expanding AI use across its engineering organization through MCP servers, which connect AI assistants with internal and external tools through a common protocol. The company combines this flexibility with allowlists, automated security checks, and internal standards to reduce risk. Its Orchestration Development Workshop demonstrates practical applications such as Jira ticket automation and multi-agent code reviews, while emphasizing shared learning and experimentation as AI practices evolve. ## MCP Servers and Their Benefits - MCP servers act as translators between AI assistants and external systems. - Before MCP, each assistant required a separate integration for every tool. - With MCP, a tool can implement one standardized interface and work with multiple compatible assistants. - This improves interoperability, scalability, and the ability to combine different AI tools. ## Security Risks and LY Corporation’s Controls - A 2025 Astrix Security report found that: - More than 5,200 public MCP servers were analyzed. - 53% relied on long-lived static API keys or personal access tokens. - Only 8.5% used newer authentication methods such as OAuth. - LY Corporation manages externally developed MCP servers through: - An allowlist permitting only approved servers. - Automated security verification based on internal standards. - Internal MCP servers for groupware and business systems are built to meet the company’s security requirements. - Centralized infrastructure lets teams focus on applying AI rather than independently rebuilding integrations and controls. ## Workshop Applications The Orchestration Development Workshop taught participants how to understand, configure, and safely apply MCP servers with AI assistants. - Topics included MCP fundamentals, security risks, internal policies, development rules, and configuration in Claude and Cline. - The internal plugin marketplace was introduced as a way to distribute MCP configurations. - Participants practiced using Claude Code with the internal groupware MCP server to: - Generate a Jira ticket title and summary. - Create the ticket automatically. - The exercise showed how AI can remove repetitive administrative work and free time for higher-value tasks. ## Multi-Agent Code Review Demonstration - A demonstration combined Claude Code, Codex CLI, Context7 MCP, and Codex MCP. - A Sonnet-based agent first analyzed a pull request, including: - Technical stack and relevant documentation. - Code changes and repository context. - Security, performance, and code-quality concerns. - GPT-5 then validated the initial review, identifying missed issues and checking the prioritization of findings. - Using different models provided more varied and potentially objective perspectives on the same code. ## Results and Organizational Learning - Around 1,600 people attended the workshop in real time. - 31.5% had already applied related techniques before the event. - Another 55.7% planned to try them soon. - LY also created “Help LY MCP,” a GPTs-based tool that explains internal MCP rules and helps teams assess whether proposed uses are suitable, including for global subsidiaries. - The workshop’s broader purpose was to create a shared understanding of: - What AI and MCP can currently do. - What risks and pitfalls exist. - How to use the technology meaningfully. ## Continuing to Experiment The article concludes that rapidly changing AI technology makes shared experimentation more valuable than simply announcing new tools. MCP may eventually be surpassed by other approaches, such as skills, so teams should continually reassess the best solution. LY recommends creating a culture where employees can safely try small ideas, learn together, and adapt as new practices emerge.

ai-assistantdatabase-designgithubclaude-code+4
stripe

How agents, digital wallets, and trust are rewriting checkout (opens in new tab)

The internet economy is reshaping checkout around mobile purchasing, digital wallets, local payment preferences, and AI-assisted shopping. Stripe’s analysis of nearly 20,000 B2C businesses shows that customers increasingly complete expensive purchases on mobile, expect region-specific payment options, and are becoming more open to buying through AI agents. Businesses that adapt checkout to local behavior and manage fraud intelligently can improve conversion while reducing unnecessary declines. ## Mobile Checkout Is Expanding to Higher-Value Purchases - Mobile dominates purchases under $50, but shoppers are increasingly using phones for purchases over $500. - This trend is strongest in APAC and EMEA, where mobile is already the preferred checkout device. - In the US, mobile gained share across every purchase range measured over the past two years. - Canada is an exception, with shoppers more likely to switch to desktop for purchases between $100 and $249. ## Digital Wallets Depend on Region and Generation - Digital wallets represent roughly 30% of global point-of-sale volume. - Sixty-one percent of surveyed shoppers said they would use a digital wallet. - Younger shoppers are especially likely to use wallets, including for purchases over $250. - Wallets cut average mobile checkout time in half, making speed a major advantage. - Preferences vary by market, from MB WAY in Portugal and MobilePay in Denmark to Alipay in China. - Businesses need to support the wallet mix that is actually popular in each region rather than relying only on Apple Pay, Google Pay, and similar global options. ## Localization Requires the Right Payment Mix - Forty-five percent of surveyed consumers made at least one international online purchase in the previous year. - International demand does not guarantee conversion; checkout must match local expectations for currency, payment methods, and presentation. - Markets such as Indonesia and Vietnam have fragmented preferences across wallets, bank transfers, debit-linked apps, and other local methods. - In more concentrated markets, conversion may depend heavily on supporting one dominant payment method. - Showing an irrelevant payment option can reduce conversion by up to 15%. - Supporting local leaders can significantly improve results: - BLIK increased Polish checkout conversion by an average of 46%. - Pix increased Brazilian checkout conversion by an average of 31%. ## AI Agents Are Changing Checkout and Payment Risk - Consumers are increasingly open to AI agents helping with purchase decisions. - Shopping and product discovery are moving into tools such as Google Gemini, Microsoft Copilot, visual search systems, and retailer-specific assistants. - Automated fraud, including card testing, is becoming easier to scale. - Overly strict risk controls can reject legitimate customers along with fraudulent transactions. - New payment models use more real-time signals, selective authentication, and improved routing and retries to balance fraud prevention with conversion. - Stripe reports that its AI-driven interventions can reduce fraud by 30% without lowering conversion. ## Checkout Becomes a Verification Layer Checkout is evolving beyond a final payment screen into a system that verifies identity, purchase intent, and authorization. Businesses should prioritize mobile performance, offer payment methods that reflect each market’s behavior, and prepare for transactions initiated by AI agents. The strongest checkout experiences will be fast, locally relevant, and capable of distinguishing legitimate buyers from automated fraud.

ai-assistantdatabase-designstripepayment-methods+4
aws

Introducing OpenClaw on Amazon Lightsail to run your autonomous private AI agents | Amazon Web Services (opens in new tab)

Amazon Lightsail now offers a preconfigured OpenClaw instance for running a private, autonomous AI assistant without managing a complex installation. The setup uses Amazon Bedrock by default and supports browser access plus messaging integrations such as WhatsApp, Discord, and Telegram. AWS aims to simplify deployment while addressing the security concerns of running an agent that can access email, files, and the web. ## Launching OpenClaw on Lightsail - In the Lightsail console, create a new instance. - Select: - A preferred AWS Region and Availability Zone - Linux/Unix as the platform - OpenClaw as the blueprint - A 4 GB memory plan is recommended for performance. - The instance typically reaches a running state within minutes. ## Pairing the Browser - Use **Connect using SSH** from the Lightsail Getting Started tab. - Copy the dashboard URL and security credentials shown in the SSH welcome message. - Open the dashboard and enter the access token in the **Gateway Token** field. - Approve the pairing from the terminal by entering `y`, then `a`. - Once pairing succeeds, the dashboard displays an **OK** status. ## Enabling Amazon Bedrock - OpenClaw is preconfigured to use Amazon Bedrock as its AI provider. - Copy the setup script from the Getting Started tab. - Run it in AWS CloudShell to enable Bedrock API access. - After completion, use the **Chat** section of the dashboard to interact with the assistant. ## Messaging Integrations OpenClaw can connect to services such as Telegram and WhatsApp, allowing users to interact with the assistant from a phone or messaging client. It can perform tasks including email management, web browsing, and file organization. ## Permissions and Costs - The setup script creates an IAM role with permissions to access Bedrock. - IAM policies can be customized, but removing required permissions may stop the assistant from generating responses. - Lightsail charges are based on the selected instance plan’s on-demand hourly rate. - Bedrock usage is billed according to tokens processed. - Third-party models offered through AWS Marketplace may add software charges. ## Security Considerations - Do not expose the OpenClaw gateway directly to the public internet. - Treat the gateway authentication token like a password. - Rotate the token regularly. - Store credentials in environment files rather than hardcoding them in configuration. - Review OpenClaw’s gateway security guidance before granting the agent access to sensitive systems. OpenClaw on Lightsail is available in all commercial AWS Regions where Lightsail operates. It provides a convenient deployment path, but users should carefully control IAM permissions, monitor costs, and secure the gateway before connecting personal data or messaging accounts.

ai-assistantamazon-bedrockaws-iamssh+2
grammarly

10 Best AI Assistants: Top Tools for Work, Writing, and Everyday Tasks (opens in new tab)

Modern AI assistants have evolved from general-purpose chatbots into specialized productivity tools that leverage Natural Language Processing (NLP) and Large Language Models (LLMs) to automate complex workflows. By selecting an assistant based on specific task relevance, integration depth, and technical capabilities like context window size, users can significantly reduce manual effort and context switching. Ultimately, the most effective tools are those that proactively support "in-flow" work rather than requiring users to step away from their primary applications. ### Technical Foundations of AI Assistants * Assistants use NLP to interpret the intent and tone behind everyday language, moving beyond the rigid menu-based structures of traditional software. * Responses are generated by LLMs trained on massive datasets, allowing the tools to recognize linguistic patterns and provide natural-sounding outputs. * Functionality is typically driven by prompts—typed or spoken requests—that allow the AI to summarize documents, refine messaging, or brainstorm project outlines. ### Evaluation Criteria for Professional Use * **Context Awareness:** This refers to the "context window," or the amount of information an AI can hold in its active memory; larger windows allow for the analysis of entire documents or long-term conversation history. * **Proactivity versus On-demand:** Some tools wait for a specific prompt, while others are "proactive," surfacing suggestions and refinements automatically as the user works. * **Integration Ecosystem:** High-value assistants operate as extensions within browsers (Chrome, Edge) or directly inside 100+ third-party apps to pull in relevant background info without manual data entry. * **Accuracy and Verification:** For research-heavy tasks, the best tools offer citations and references to mitigate the risk of "hallucinations" or incorrect data common in LLMs. * **Privacy and Security:** Professional-grade tools provide transparent data handling and storage policies, which is essential for teams managing sensitive information. ### Specialized Assistants and Use Cases * **Go:** A communication-focused assistant that works proactively within existing workflows to draft emails and improve clarity in real-time. * **ChatGPT:** A versatile, general-purpose tool best suited for technical problem-solving, coding support, and creative ideation, though it often requires manual context switching. * **Claude AI:** Optimized for high-volume text processing, making it the preferred choice for deep document analysis and complex, long-form revisions. To achieve the best results, users should audit their daily app usage and primary tasks—such as scheduling, coding, or drafting—before committing to a platform. Prioritizing an assistant that integrates directly into your most-used software will yield the highest productivity gains by eliminating the friction of copying and pasting data between windows.

ai-assistantllmmachine-learningnlp+2
grammarly

How to Create an AI Assistant Step by Step: A Beginner’s Guide (opens in new tab)

Creating a custom AI assistant is no longer restricted to engineers, as modern no-code tools and APIs allow users to build specialized agents for specific personal or professional workflows. By focusing on a narrow scope and selecting the right platform, individuals can gain greater control over data, behavior, and task efficiency than generic tools provide. Ultimately, the shift toward custom assistants reflects a move away from one-size-fits-all software toward personalized AI teammates integrated directly into daily work. ## The Anatomy of an AI Assistant * Digital assistants utilize Natural Language Processing (NLP) to interpret user intent and tone through conversational prompts. * Large Language Models (LLMs) serve as the underlying engine, recognizing language patterns to generate contextually relevant responses. * Advanced implementations, such as the "Go" assistant, operate within existing apps like email and documents to eliminate context switching and manual data entry. ## Strategic Drivers for Customization * **Personalization:** Tailoring the assistant’s tone and behavior ensures it supports specific tasks exactly as the user expects. * **Data Control:** Building a custom solution offers transparency into how data is used, which is critical for teams handling sensitive internal information. * **Efficiency and Innovation:** Customizing an assistant for a niche problem—like summarizing specific document types or automating recurring questions—reduces manual effort more effectively than general tools. * **Independence:** Creating a proprietary tool reduces reliance on third-party platforms that may change their pricing or feature sets. ## Defining the Core Mission * The most successful assistants focus on one primary responsibility rather than trying to handle every possible task. * Effective planning requires answering who the user is and what specific problem the assistant is meant to solve consistently. * Starting with a narrow scope, such as a dedicated writing assistant or a customer service bot, simplifies the testing and refinement process during the initial launch. ## Development Paths and Lifecycles * Users can choose between no-code platforms for rapid deployment or API-based configurations for higher flexibility and integration. * The development process follows a standard lifecycle: strategic planning, technical configuration, launch, and continuous improvement. * Ongoing monitoring is essential to ensure the assistant remains responsible, accurate, and aligned with evolving user needs. To build a successful AI assistant, start by identifying a single high-impact task and selecting a tool that matches your technical comfort level. Prioritizing a narrow focus during the initial build will allow for more effective monitoring and easier scaling as your requirements grow.

ai-assistantllmmachine-learningnlp+3