How Gen Digital built Norton Revamp’s AI-curated News Engine with Amazon Bedrock AgentCore Web Search

Your professional reputation now lives online first. Recruiters check LinkedIn before returning a call. Clients review your digital presence before booking a first meeting. For ambitious professionals, that shift is an opportunity: a well-crafted online voice can open doors that a résumé alone never could. But standing out takes more than content generation. It takes real-time awareness of what’s happening right now. A professional building a reputation as a generative AI thought leader needs to react to today’s breakthroughs, not last week’s headlines. And with feeds flooded by AI-generated noise, audiences reward authenticity and timeliness more than ever. Web Search on Amazon Bedrock AgentCore made that possible. We built Norton Revamp , a generative AI-powered professional reputation management platform. At its heart is an AI-curated News Engine, powered by AgentCore Web Search, that grounds every content suggestion, goal recommendation, and news feed item in current, cited web knowledge. The platform launched in March 2026, built by a small group of engineers. In this post, we show you how the Gen team builds an AI-curated News Engine in Norton Revamp with Amazon Bedrock AgentCore Web Search to help customers turn ideas into engaging social media posts. The challenge: Real-time grounding for professional content Unlike chatbots that rely on static knowledge bases, Norton Revamp needs to understand what’s trending right now in each user’s professional domain. A cyber security expert needs today’s threat intelligence. A marketing leader needs this week’s industry shifts. Traditional approaches (custom web crawlers, third-party news APIs, static content databases) each carry trade-offs: infrastructure overhead, stale data, vendor complexity, or data egress risk. We evaluated three alternatives before choosing AgentCore Web Search. Custom crawlers require ongoing maintenance and lag real time. Third-party news APIs added vendor contracts, API key management, and data leaving our AWS environment. Static knowledge bases were perpetually stale. AgentCore Web Search eliminated these concerns. It’s fully managed with zero infrastructure overhead, operates entirely within AWS with zero data egress, and returns cited sources with URLs and publication dates. This makes personalized, real-time news feeds viable at scale. How AgentCore Web Search powers Norton Revamp Web Search on Amazon Bedrock AgentCore is a fully managed web search capability, compliant with the Model Context Protocol (MCP). It’s available as a managed connector on the AgentCore Gateway, a capability of Amazon Bedrock AgentCore. Our agent sends a natural-language query, and Web Search returns relevant snippets, source URLs, titles, and publication dates that the model uses to produce grounded responses. This capability powers three core features: AI-curated news feed Every user gets a personalized content stream surfacing the latest articles and trending topics tied to their goal. If you’re building a reputation as a generative AI thought leader, your feed prioritizes AI breakthroughs, industry analysis, and emerging use cases, sourced in real time through AgentCore Web Search. Unlike a static RSS aggregator, the feed is a goal-aware discovery engine that refreshes with every session. Smart content suggestions Users receive AI-generated content ideas tailored to trending topics in their space, written in their own authentic voice, with optional AI-generated visuals. AgentCore Web Search grounds every suggestion on what’s happening today . A three-step coaching flow (Analyze, Refine, Post) walks users from initial ideas to published content on LinkedIn and X, with a feedback loop, so the AI gets sharper with every iteration. Trend-aware goal setting Users define a goal. The AI builds a personalized roadmap with weekly themes and milestones. AgentCore Web Search feeds real-time trend data into goal generation, so weekly themes reflect current industry movements. The system learns from the past four weeks of activity, refreshing recommendations as users grow. Architecture and tech stack Figure 1: Event-driven architecture of the Norton Revamp News Engine The integration follows an event-driven architecture. When a user sets or updates their professional goal, Amazon Nova (through Amazon Bedrock) generates targeted search queries based on the goal, recent activity, and engagement patterns. The agent sends these queries to AgentCore Web Search, which returns cited results entirely within our AWS environment. Our AI Learning System ranks the results for relevance, recency, and source credibility. The ranked results then serve as grounding context for Amazon Nova Lite to generate content suggestions in the user’s authentic voice. Engagement data feeds back into query generation, making the News Engine progressively smarter. Generative AI and machine learning stack: Amazon Bedrock – Core generative AI engine for content generation, personalization, post analysis (batch mode), and content coaching. Amazon Nova (Nova Lite) – Primary large language model (LLM) powering content generation, goal creation, and trend analysis. Web Search on Amazon Bedrock AgentCore – Real-time web retrieval powering the News Engine, content grounding, and trend discovery through an MCP-compliant managed connector. Stability AI – Image generation. Claude (through Amazon Bedrock) – Additional LLM capabilities for complex reasoning tasks. Infrastructure: React, TypeScript, and Tailwind CSS on the frontend. Node.js, Amazon Elastic Container Service (Amazon ECS), AWS Lambda, Amazon DynamoDB (multi-table with composite key patterns), Amazon API Gateway, Amazon Simple Storage Service (Amazon S3), Amazon Simple Queue Service (Amazon SQS), Amazon EventBridge, Amazon CloudFront, Amazon CloudWatch, and AWS Secrets Manager on the backend. Infrastructure managed through Terraform with blue-green deployment for zero-downtime releases. Processing pipeline: The platform runs four pipeline stages: Full Scan – Pull up to 1,000 historical posts, store in Amazon S3, run Amazon Bedrock batch analysis for reputation impact. Daily Scan – Amazon EventBridge-triggered incremental analysis. Weekly Goal Generation – Lambda generates goals considering past engagement data plus real-time trend data from AgentCore Web Search. Content Coaching – Synchronous Amazon Bedrock calls through the Analyze, Refine, Post flow, each step grounded with AgentCore Web Search results. Engineering with an AI-native development mindset We embedded generative AI from day one, as both the product and the development methodology. The human-AI responsibility split was deliberate: human-owned architecture decisions, security reviews, business logic, and personalization algorithms. AI handled code generation, documentation, test scaffolding, refactoring, and UI component creation. With Claude, we produced 10 or more design variants per concept, compressed design-to-prototype time from weeks to hours and generated 70 percent of UI code with AI. We tested five pricing models in the time previously needed for one. The gamification system went through eight iterations before launching. The measurable impact: 3x faster feature implementation, boilerplate generation accelerated by 90 percent, documentation by 80 percent, test creation by 70 percent, and UI component generation by 65 percent. The trade-off: AI output review consumed 20–30 percent of our time. AI helped with personalization algorithms, the DynamoDB multi-table architecture (which needed experience-based judgment), security implementation, and Amazon Bedrock batch processing optimization. The key insight: AI accelerated implementation, but humans designed how the AI Learning System should adapt to user goals. Product generative AI (Amazon Bedrock, Amazon Nova, AgentCore Web Search) and development generative AI served different purposes, and we kept them separate. Testing, security, and quality assurance We achiev