web-search

3 posts

aws

Top announcements of the AWS Summit in New York, 2026 | Amazon Web Services (opens in new tab)

AWS Summit New York 2026 focused on making AI agents more capable, secure, autonomous, and easier to operate in production. Announcements spanned Amazon Bedrock AgentCore, security automation, developer tooling, workplace agents, and S3 data management. The overall direction is toward agents that can access governed knowledge, act independently, and continuously improve while remaining subject to enterprise controls. ## Building More Capable Agents - **Amazon Bedrock Managed Knowledge Base** simplifies enterprise RAG with native data connectors, Smart Parsing for multi-format data, and an Agentic Retriever for complex, multi-step queries. - **Web Search for Bedrock AgentCore** provides managed, current, cited web knowledge while keeping data within the customer’s secured AWS environment. - **Bedrock AgentCore harness** is now generally available, allowing developers to define an agent’s model, tools, skills, and instructions through configuration rather than custom orchestration loops. - **AWS Context**, coming soon, will map relationships across organizational data into a knowledge graph. Agents will be able to use governed relationships, business rules, and domain knowledge at runtime. - **AWS WAF Bot Control** lets publishers and content owners price, meter, and collect payment from AI bots accessing content and APIs, with scoped access enforced at the edge. ## Securing Agents and Applications - **AWS Continuum**, available in gated preview, aggregates vulnerability findings, ranks them by business impact, verifies exploitability, and routes fixes through existing development processes. - **AWS Security Agent**, now part of Continuum, adds: - Threat modeling based on the STRIDE framework - Pull-request code scanning and remediation across major Git platforms - IDE integrations through Kiro, Claude Code, and MCP - These tools are intended to let developers perform security reviews and address vulnerabilities without leaving their normal workflows. ## AI-Assisted Software Development - **Kiro for iOS**, in gated preview, lets developers start, monitor, steer, and approve Kiro sessions from a phone, including reviewing diffs and approving changes without keeping a laptop running. - **AWS DevOps Agent** adds release readiness reviews and autonomous release testing. It evaluates changes against natural-language standards and tests them in production-like environments. - **AWS Transform continuous modernization**, in preview, scans repositories against configurable technical-debt baselines and can autonomously generate remediation pull requests. ## Autonomous Workplace Agents - New **Amazon Quick autonomous agents** can work in the background with defined expertise, tone, permissions, and tools. - Example use cases include: - A finance agent processing incoming orders - A sales agent analyzing CRM, email, and Slack activity - Agents drafting follow-ups, identifying risks, and recommending next steps - A new **activity feed** combines email, messaging, calendars, and tasks into a prioritized view that adapts to the user’s communication and work patterns. ## Richer Metadata for Amazon S3 - **S3 annotations** allow up to 1 GB of mutable, queryable context to be attached directly to an object. - The feature targets AI agents and autonomous workflows that need to discover and interpret data without maintaining separate metadata systems. AWS’s announcements point toward an agent ecosystem that combines managed knowledge retrieval, web access, security automation, autonomous development workflows, and persistent workplace assistance. Organizations adopting these capabilities should pair autonomy with strong governance, scoped permissions, and continuous validation in production.

aws

Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge | Amazon Web Services (opens in new tab)

Amazon Bedrock AgentCore Web Search is now generally available, giving AI agents access to current, cited web information without sending prompts or queries outside a customer’s AWS environment. Built into AgentCore Gateway through the Model Context Protocol (MCP), it combines Amazon’s web index with structured knowledge graph data to provide relevant snippets, URLs, titles, dates, and verified facts. The service lets developers ground agent responses in up-to-date information without building or managing search infrastructure themselves. ## How Web Search Works - Agents submit natural-language queries through an AgentCore Gateway. - The tool returns: - Relevant web snippets - Source URLs - Page titles - Publication dates - Results are powered by: - Amazon’s web search index - Amazon Knowledge Graph data - The combination of web results and structured facts is intended to improve relevance and accuracy over conventional search alone. - Retrieved information can support both grounded responses and follow-up agent actions. ## Secure, Managed Agent Integration - Web Search is a fully managed connector target for Bedrock AgentCore Gateway. - Customer prompts and retrieval queries remain within the AWS environment rather than being sent to external search API providers. - Developers avoid implementing search integrations and operating related infrastructure. - The service supports enterprise governance requirements and secured data workflows. ## Getting Started - Create or open a Bedrock AgentCore Gateway in the AWS console. - Select: - **MCP** as the target protocol - **Connectors** as the target type - **Web Search** as the preconfigured target - After the Gateway URL is created, interact with the tool using: - API calls - AWS CLI - MCP Python SDK - Strands MCP Client - MCP Inspector - MCP Inspector can be used to connect to the Gateway, enter a query, and run the Web Search tool for testing and debugging. - Web Search can also be added to an existing Gateway. ## Early Customer Use Cases - **Benchling** uses Web Search to combine institutional scientific data with published literature, helping researchers generate more complete answers and hypotheses in a governed environment. - **Gen Digital** uses the tool in Norton Revamp to create current online reputation content based on real-world developments. - Customers highlighted the value of current information, AWS-native security, and keeping search queries within their trusted environment. ## Availability and Pricing - Web Search is generally available in **US East (N. Virginia)**. - It is offered at no additional cost initially. - Customers pay applicable Gateway data transfer charges. - New AWS customers may receive up to $200 in Free Tier credits. Developers can try Web Search through the Amazon Bedrock AgentCore console and consult the Gateway documentation for implementation details.

figma

What Would You Ask If No One Could Judge You? | Figma Blog (opens in new tab)

Perplexity’s founders envision it as an “answer engine” that turns web-scale information into concise, sourced explanations rather than lists of links. The product grew from a personal need for judgment-free learning and was shaped by the shortcomings of early conversational AI, especially outdated knowledge and hallucinations. Its broader goal is to make curiosity easier to express and pursue. ## Building a Judgment-Free Knowledge Tool - Aravind Srinivas was inspired by childhood “Wikipedia rabbit holes” and the evolution from printed encyclopedias to AI-powered knowledge tools. - Perplexity aims to make learning engaging through curiosity rather than attention-grabbing entertainment. - The company wants users to ask anything without worrying about appearing uninformed or being judged. ## From Private Slackbot to Public Product - The founders initially built a Slackbot to answer practical questions about fundraising, employee health insurance, and running a company. - They hesitated to launch because they feared criticism for attempting to compete with Google. - Investor Nat Friedman encouraged them to view the effort as an asymmetric bet: little downside, but potentially enormous upside. - Perplexity launched shortly after ChatGPT, despite the founders having no previous company-building experience. ## An Answer Engine with Sources - ChatGPT highlighted problems with knowledge cutoffs, hallucinations, and unsupported answers. - Perplexity responded by combining: - Natural-language interaction - Web search and indexing - Large language models - Inline sources and footnotes - Its goal is to provide a direct answer while allowing users to verify the underlying information. - Srinivas describes the product as a combination of Wikipedia and conversational chat, with information drawn from across the internet. ## Making Complex Information Approachable - Perplexity follows an 80/20 approach: identify the most important concepts and deliver most of the useful understanding quickly. - It synthesizes information from multiple web pages into a concise explanation instead of requiring users to read extensively. - The product aims to simplify information without reducing it to misleading or overly shallow conclusions. ## Turning Answers into Further Curiosity - Each response includes three related follow-up questions to encourage exploration. - Srinivas argues that people are naturally curious but often lack the confidence, vocabulary, or precision to formulate good questions. - Perplexity’s design assumes that the user is never wrong; the system should help clarify and develop a person’s curiosity rather than blame them for asking imperfectly. Perplexity’s central recommendation is implicit in its design: make knowledge easier to access, verify, and explore, while removing the social fear that prevents people from asking questions in the first place.