Gen Digital Leverages AWS Bedrock AgentCore for Norton Revamp’s Real-Time AI News Engine

In an era where professional reputations are forged and maintained online, Gen Digital has developed Norton Revamp, a generative AI-powered platform designed to help professionals manage their digital presence. At the core of this platform is an AI-curated News Engine, a sophisticated system built using Amazon Bedrock AgentCore’s Web Search capability to ensure content suggestions and professional insights are grounded in the most current, cited web knowledge.

The Chenab Times has learned that this innovative approach addresses the critical need for real-time professional awareness. For individuals aiming to establish themselves as thought leaders in fields like generative AI, reacting to today’s breakthroughs rather than last week’s news is paramount. In a digital landscape saturated with AI-generated content, authenticity and timeliness are increasingly valued by audiences, making a dynamic and accurate information stream essential.

The Challenge of Real-Time Professional Content Grounding

Traditional chatbots often rely on static knowledge bases, but Norton Revamp requires a dynamic understanding of current trends within a user’s specific professional domain. A cybersecurity expert, for instance, needs up-to-the-minute threat intelligence, while a marketing leader must stay abreast of weekly industry shifts. Existing solutions, such as custom web crawlers, third-party news APIs, or static databases, each present significant drawbacks. These can include substantial infrastructure overhead, data that quickly becomes stale, complexity in managing multiple vendor contracts, or risks associated with data egress from secure environments.

Gen Digital evaluated several alternatives before selecting AgentCore Web Search. Custom crawlers demand continuous maintenance and invariably lag behind real-time events. Third-party APIs introduce the burdens of vendor management, API key administration, and the potential for sensitive data to leave the AWS ecosystem. Static knowledge bases are inherently limited by their unchanging nature. AgentCore Web Search, conversely, provides a fully managed service with zero infrastructure overhead, operates entirely within AWS thereby eliminating data egress concerns, and crucially, returns cited sources complete with URLs and publication dates. This enables the delivery of personalized, real-time news feeds at scale.

How AgentCore Web Search Powers Norton Revamp’s Features

Web Search on Amazon Bedrock AgentCore functions as a fully managed web search capability, adhering to the Model Context Protocol (MCP). It is accessible as a managed connector via the AgentCore Gateway, a component of Amazon Bedrock AgentCore. The system operates by sending a natural-language query, which Web Search then processes to return relevant snippets, source URLs, titles, and publication dates. This retrieved information serves as the grounding context for the AI model to generate accurate and relevant responses.

This capability underpins three primary features within Norton Revamp:

• AI-curated news feed: Each user receives a personalized content stream featuring the latest articles and trending topics relevant to their professional goals. For instance, a user aspiring to be a generative AI thought leader will see their feed prioritized with AI breakthroughs, industry analyses, and emerging use cases, all sourced in real time via AgentCore Web Search. This feed acts as a goal-aware discovery engine, distinct from static RSS aggregators, refreshing dynamically with each user session.
• Smart content suggestions: Users are provided with AI-generated content ideas tailored to current trends in their field, presented in their authentic voice, with optional AI-generated visuals. AgentCore Web Search ensures that every suggestion is grounded in contemporary information. A guided three-step process—Analyze, Refine, Post—assists users from initial concept to published content on platforms like LinkedIn and X, incorporating a feedback loop that refines the AI’s performance with each iteration.
• Trend-aware goal setting: Users define their professional objectives, and the AI constructs a personalized roadmap outlining weekly themes and milestones. AgentCore Web Search injects real-time trend data into this goal generation process, ensuring that weekly themes accurately reflect current industry movements. The system learns from the preceding four weeks of user activity, adapting its recommendations as the user progresses.

Architecture and Technology Stack

The integration of these components follows an event-driven architecture. When a user establishes or modifies their professional goal, Amazon Nova, operating through Amazon Bedrock, generates targeted search queries based on the user’s goal, recent activity, and engagement patterns. These queries are then dispatched to AgentCore Web Search, which returns cited results entirely within the AWS environment. An AI Learning System subsequently ranks these results based on relevance, recency, and source credibility. These ranked results then provide the grounding context for Amazon Nova Lite to generate content suggestions that align with the user’s authentic voice. User engagement data is fed back into the query generation process, enhancing the News Engine’s intelligence over time.

The generative AI and machine learning stack comprises:

• Amazon Bedrock: The core generative AI engine responsible for content generation, personalization, batch post analysis, and content coaching.
• Amazon Nova (Nova Lite): The primary large language model (LLM) powering content generation, goal creation, and trend analysis.
• Web Search on Amazon Bedrock AgentCore: Facilitates real-time web retrieval for the News Engine, content grounding, and trend discovery through a managed, MCP-compliant connector.
• Stability AI: Utilized for image generation capabilities.
• Claude (via Amazon Bedrock): Provides additional LLM capabilities for complex reasoning tasks.

The infrastructure supporting Norton Revamp includes React, TypeScript, and Tailwind CSS on the frontend. The backend utilizes Node.js, Amazon Elastic Container Service (Amazon ECS), AWS Lambda, Amazon DynamoDB, 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. Infrastructure management is handled through Terraform, employing blue-green deployments for seamless, zero-downtime releases.

The platform operates through a four-stage processing pipeline:

• Full Scan: Retrieves up to 1,000 historical posts, stores them in Amazon S3, and performs batch analysis via Amazon Bedrock to assess reputation impact.
• Daily Scan: Conducts incremental analysis triggered by Amazon EventBridge.
• Weekly Goal Generation: AWS Lambda generates goals by considering past engagement data alongside real-time trend data from AgentCore Web Search.
• Content Coaching: Employs synchronous Amazon Bedrock calls through the Analyze, Refine, Post workflow, with each step grounded by AgentCore Web Search results.

An AI-Native Development Mindset

Gen Digital adopted an AI-native development methodology from the outset, integrating generative AI into both the product and the development process. A deliberate human-AI responsibility split was established: humans retained ownership of architecture decisions, security reviews, business logic, and personalization algorithms. AI, in turn, handled tasks such as code generation, documentation, test scaffolding, refactoring, and UI component creation.

Using Claude, the team was able to produce multiple design variants per concept, significantly compressing the design-to-prototype timeline from weeks to mere hours. Approximately 70 percent of the UI code was generated by AI. This agility allowed for the testing of five pricing models within the timeframe previously allocated for one. The gamification system underwent eight iterations before its public launch. The measurable impact of this approach included a threefold increase in feature implementation speed, a 90 percent acceleration in boilerplate code generation, an 80 percent improvement in documentation creation, a 70 percent boost in test generation, and a 65 percent increase in UI component generation. The primary trade-off was that AI output review consumed between 20 to 30 percent of the team’s time.

AI also assisted in developing personalization algorithms, architecting the DynamoDB multi-table structure, implementing security measures, and optimizing Amazon Bedrock batch processing. The key takeaway was that while AI accelerated implementation, human expertise was crucial in designing how the AI Learning System should adapt to user goals. Product generative AI, such as Amazon Bedrock, Amazon Nova, and AgentCore Web Search, and development generative AI served distinct purposes and were managed separately.

Rigorous Testing, Security, and Quality Assurance

The development process achieved an 85 percent code coverage through automated test generation. Critical testing areas encompassed the quality of content generation, the validation of AgentCore Web Search results for relevance and freshness, the accuracy of engagement tracking, the logic of the gamification system, and the overall relevance of the news feed to user objectives.

For a product bearing the Norton brand, compliance is a non-negotiable aspect. Security checks were comprehensive, including static code analysis, dependency vulnerability scanning, authentication flow verification, user data encryption, access control logic, generative AI model output validation, and content filtering. The security team meticulously reviewed all AI-generated recommendations, with AI expediting the identification of potential issues, but humans making the final decisions.

The outcome of these rigorous processes is a platform that has maintained 99.9 percent uptime since its launch, supported by real-time monitoring of generative AI content quality and user satisfaction levels.

Best Practices for AgentCore Web Search Integration

Gen Digital offers several best practices for organizations considering AgentCore Web Search for real-time content grounding:

• Invest in query generation: Well-structured queries lead to superior results compared to open-ended ones. Emphasis was placed on prompt engineering to optimize query formulation.
• Strategic caching: While real-time freshness is essential, not all queries necessitate re-execution in every session. Implementing time-to-live (TTL) values that align with freshness requirements allows for strategic caching of results.
• Build feedback loops: Tracking user engagement with web search results and feeding this signal back into query generation significantly enhances the system’s relevance. The News Engine’s performance improved notably within the first 90 days due to such feedback mechanisms.
• Validate source quality: AgentCore Web Search provides source URLs and publication dates, enabling the development of a lightweight credibility scoring layer to prioritize authoritative sources.

Conclusion

The development of Norton Revamp demonstrated that a compact, generative AI-augmented team can deliver enterprise-grade software within months. It also validated the utility of Web Search on Amazon Bedrock AgentCore as a real-time intelligence backbone for products requiring up-to-date information. The platform, launched in mere months, offers a generative AI-native professional reputation management solution featuring personalized goal setting, AI-driven content suggestions, real-time curated news feeds grounded by AgentCore Web Search, adaptive learning, gamification, and robust Norton-grade security, all achieved with 99.9 percent uptime by a small engineering team.

Generative AI amplifies, rather than replaces, engineers. Pairing this amplification with fully managed services like AgentCore Web Search eliminates significant categories of infrastructure complexity. While humans conceptualized the vision, generative AI technologies accelerated its realization, grounded in real-time, cited web knowledge.

“Web Search on Amazon Bedrock AgentCore eliminates the complexity of building and maintaining our own web retrieval infrastructure. It lets our Norton Revamp News Engine deliver real-time, personalized content grounded in what’s happening in each user’s professional domain, with zero data egress and Norton-grade privacy. We went from concept to production in months, and AgentCore Web Search was a key enabler.”

For further details on Web Search on Amazon Bedrock AgentCore, consult the AgentCore documentation. Information on Norton Revamp is also available.

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