Amazon Bedrock for AI Professionals and Developers

We've been diving deep into Amazon Bedrock over the past couple of months, exploring the fascinating capabilities it unlocks for our customers. 💭 Some of you may remember my skepticism when the preview was announced in April of this year… Well, I’m happy to report that I was wrong. After getting hands-on-keyboard with the service over the last two months, I now firmly believe this service propels AWS ahead of the curve and paves the way for the democratization of GenAI. Bedrock gives builders unfettered access to LLMs from multiple providers through a consistent API that’s deeply integrated with AWS. 🚀     🌟 My favorite features?   ✅ Speed. We’ve been getting about 20 tokens per second on Claude V2, not accounting for network latency. On Claude Instant, we’ve seen 100s of tokens per second. ✅ Scale. Despite taxing the service pretty aggressively, we have yet to hit any rate limits. ✅ Growing set of model options. So far at Caylent, we’ve been working with Antrhopic’s Claude V2, Stability’s Stable Diffusion XL v2, and AI21’s Jurassic-2. Last Wednesday, AWS announced the addition of Cohere’s Command model, which I can’t wait to try. ✅ Privacy. Your data is never used to retrain the models for other customers. No inference request’s input or output is used to train any model. Model deployments are inside an AWS account owned and operated by the Bedrock service team. Model vendors have no access to customer data. ✅ Security. You can customize the FMs privately and retain control over how your data is used and encrypted. Your data, prompts, and responses are all encrypted in transit (TLS 1.2) and at rest with AES-256 KMS keys. You can use PrivateLink to connect Bedrock to your VPCs. Your data never leaves the region you’re using Bedrock in. IAM integration enables RBAC, ABAC, and resource-based policies that allow your organization to customize access based on your organizational policies. ✅AWS Integration. For existing AWS customers, the deep integration of Bedrock into tooling like CloudWatch, CloudTrail, and IAM means Bedrock is production ready as soon as it’s generally available.   💼 We’ve given 100+ demos of Bedrock over the last 60 days, and it’s thrilling to see customers start to move beyond experimentation and into production. All of these demos and customer conversations led to the creation of our Generative AI Knowledge Base Catalyst that connects Amazon Bedrock with Amazon Kendra to deliver bespoke enterprise scale retrieval augmented generation capabilities to any AWS Customer. This is already powering our internal knowledge base at Caylent and even providing weekly summaries of updates. 🔜 What's next on the horizon? I'm eagerly awaiting access to Bedrock's game-changing feature, Agents.   💡With all the above, it's no wonder we're thrilled to help customers #MoveToBedrock and #BuildOnBedrock.   #GenAI #AWS #AWSBedrock

🚀 Breaking: Amazon just dropped a game-changing suite of generative AI models, and on analyzing Amazon's Nova Technical Report, I'm particularly excited about their breakthroughs including #agentic workflow benchmarks and results. The Nova family introduces 6 powerhouse models: • Nova Micro - Cost-efficient text specialist • Nova Lite - Budget-friendly multimodal • Nova Pro - Advanced multimodal powerhouse • Nova Premier - Top-tier performance • Nova Canvas - Image generation • Nova Reel - Video synthesis 🔥 Key Performance Highlights: - Nova Pro crushes it on text-visual tasks, hitting 89.2% on ChartQA - Nova Lite delivers impressive 157 tokens/sec throughput - All models support major languages with strong translation capabilities - Nova Canvas & Reel introduce competitive image/video generation 🛡️ What really stands out is their comprehensive approach to Responsible AI. They have built everything on 8 core principles, from fairness to transparency - with rigorous testing at each step. 🎯 Key Agentic Benchmarks: • Nova Pro achieved 68.4% overall accuracy on Berkeley Function Calling Leaderboard (BFCL) • 90.1% AST score for function calling accuracy • 89.8% execution accuracy • Impressive 95.1% relevance score 🌟 Multimodal Agent Performance: - 79.7% on VisualWebBench - 63.7% step accuracy on MM-Mind2Web - 81.4% accuracy on GroundUI-1K What's fascinating is how Nova models can: • Break down complex multi-step tasks • Choose and execute appropriate tools • Process both text and visual inputs • Make decisions based on conversation history • Integrate seamlessly with Bedrock Knowledge Bases 🤓 Tech Detail That Impressed Me: The models were trained on Amazon's custom Trainium1 chips and scaled up to H100s, with some clever optimizations like their "Super-Selective Activation Checkpointing" reducing memory usage by ~50% with only 2% recomputation overhead. That's seriously efficient engineering. 💡 For practitioners: The models are available through AWS Bedrock, making them easily accessible for production deployment. The multimodal capabilities especially look promising for enterprise applications. This release feels like a major leap forward in making enterprise-grade AI both powerful and responsible. Excited to see how the community puts these models to work! #ArtificialIntelligence #AWS #MachineLearning #GenerativeAI #Innovation #AITechnology

1 week ago at the AWS New York Summit, Matt Wood launched a suite of new Generative AI features in Amazon Web Services (AWS) Bedrock. Rather than do an announcement post last week, I figured I'd use a few of them and give some more feedback in a week. Here's my take on the most impactful features: 1️⃣ Prompt Management - worth adopting immediately - replaces the janky prompt management approach you've probably hacked together with a well thought out managed service. Provides a standard, API-based (with a good UI) approach to manage prompts for your generative AI applications. I can see many tools standardizing / integrating with this over time. 2️⃣ Memory for Bedrock Agents - start POCs now - preview feature that's great for use cases where people interact with your AI app periodically over a long period of time. Provides an integrated approach for maintaining context over long interactions with an AI agent. 3️⃣ Bedrock Guardrails API - worth evaluating - lets you centralize and re-use evaluations of GenAI model inputs and outputs during development and in production. There are lots of good tools / libraries in this space today that handle the runtime, but this feature enables serverless execution. 4️⃣ Q Developer Customization - worth evaluating - lets Q understand your overall code base in addition to its native functions, so it can recommend calls into your proprietary libraries, and also utilize your organization's coding standards. Need to see it doing better at suggesting the right library function calls, but its definitely an improvement for large code bases 5️⃣ Code Interpreter for Bedrock Agents - limited POCs now - preview feature great for small tasks where deterministic results are important or to help with "last mile" data analysis. Lets your agent dynamically generate and run code (with no network access - so no API calls), but the limitations make it a 'niche' feature for now. A few that are in 'wait and see' mode: Prompt Flows - missing some key features, but could be very interesting combined with Agents once it's built out. Q Apps - need to understand why I wouldn't just use Bedrock Studio LLM Fine Tuning - cost performance improvements go to AWS & Anthropic. Only in one region and one model.

𝐅𝐫𝐨𝐦 𝐎𝐧𝐛𝐨𝐚𝐫𝐝𝐢𝐧𝐠 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐒𝐜𝐚𝐥𝐞: 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐢𝐧 𝐧𝟖𝐧 𝐚𝐧𝐝 𝐀𝐦𝐚𝐳𝐨𝐧 𝐁𝐞𝐝𝐫𝐨𝐜𝐤. I built this automation workflow last week that reminded me of a workshop I recently led on creating Amazon Bedrock Agents and my first AI application with Amazon Q for Business. That comparison reminds me how powerful it is when context, automation, and data come together to build systems that actually think and act. I built this project for a client onboarding system. The goal was to simplify the entire process from the initial call to contract signing and post-onboarding communication and to see how far automation could go with minimal human intervention. Using n8n, I created an AI Agent that connects Google Sheets, Fathom AI, PandaDoc, Gmail, and Calendly into one seamless onboarding flow. Every time a deal closes, it:  • Extracts sales call transcripts using AI  • Generates contract fields automatically  • Sends PandaDoc for signing  • Updates CRM and triggers a welcome email Here’s what stood out when comparing this build to Amazon Bedrock Agents: 𝐒𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 𝐯𝐬 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 n8n makes automation visual and fast. You can build and test agent logic in minutes. Amazon Bedrock requires setup across Lambda, IAM, and orchestration services but delivers scalable reliability and enterprise-grade fault tolerance. 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐯𝐬 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 n8n is perfect for quick prototyping and cross-app integrations. Amazon Bedrock focuses on managed security, model access control, and compliance frameworks that support enterprise and regulated workloads. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐩𝐞𝐞𝐝 𝐯𝐬 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐏𝐨𝐰𝐞𝐫 n8n connects instantly with Gmail, Docs, and CRMs using built-in nodes. Amazon Bedrock integrates deeply with S3, DynamoDB, SageMaker, and Amazon Q to create agent ecosystems that scale across organizations. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐧𝟖𝐧 If you build in n8n, treat your workflow like production code. ❤️🔥  • Store API keys and secrets in environment variables, not inside workflow nodes.  • Use n8n’s credential encryption for sensitive data.  • Limit user permissions and secure all webhooks with authentication.  • Rotate credentials regularly and review audit logs often. Both platforms aim to turn workflows into intelligent systems. n8n gets you there quickly. Amazon Bedrock keeps you there securely. If you want both speed and scale, build your prototype in n8n and operationalize it with Amazon Bedrock Agents.

Someone asked me about my fundamental choices for a solid AI at Scale solution. Here is my response and why. Top 6 coming at you… 1. Amazon Bedrock – Foundation Models as a Service • Why: Lets me tap Anthropic, Meta, Amazon, Cohere, and others without building model infra. • Mass impact: One endpoint for multiple models = democratized access for devs and enterprises. • Governance: Bedrock Guardrails + Knowledge Bases give me control over safety and retrieval. 2. LangChain / LangGraph – Agent & Workflow Framework • Why: I need composability—memory, retrieval, multi-step orchestration, agent routing. • Mass impact: It lowers the barrier for thousands of devs who don’t want to re-invent orchestration logic. • Future-proof: Works across models, integrates with Bedrock, OpenAI, or open-source. 3. Vector Database (Pinecone / Weaviate / OpenSearch Serverless) • Why: RAG is the only way to make AI useful at scale with enterprise data. • Mass impact: Makes private knowledge searchable and usable by anyone, not just data scientists. • Enterprise fit: I’d lean OpenSearch Serverless inside AWS for tight compliance and ops. 4. Step Functions / Temporal – Deterministic Orchestration • Why: n8n/Zapier are great at the edge, but at scale I need durable, replayable, high-SLA orchestration. • Mass impact: Keeps long-running AI workflows reliable (days-to-weeks sagas, retries, state). • Choice: Step Functions if staying fully AWS, Temporal if I want portability. 5. Streamlit / Gradio (or equivalent low-code front end) • Why: To “bring AI to the masses,” the user interface must be simple, visual, and quick to iterate. • Mass impact: Enables non-technical users to experiment and deploy lightweight apps without waiting on IT. 6. OpenTelemetry + Grafana – Observability & Trust Layer • Why: If I don’t monitor prompts, outputs, latency, cost per call, and guardrail triggers, the system becomes a black box. • Mass impact: Building trust at scale requires transparency and feedback loops. • Bonus: Can plug into CloudWatch/Datadog; gives business KPIs tied to AI performance.  How I’d Deploy Them Together • Bedrock is my model backbone. • LangChain/LangGraph orchestrates agentic logic. • Vector DB powers RAG + personalization. • Step Functions/Temporal handle reliable, large-scale workflows. • Streamlit/Gradio put AI in human hands fast. • OpenTelemetry/Grafana ensure I can prove it’s working, safe, and ROI-positive.

An insightful whitepaper from AWS explores the '6 Key Guidelines for Building Secure and Reliable Generative AI Applications on Amazon Web Services (AWS) Bedrock.' 🛡️🤖 Building generative AI applications requires thoughtful planning and careful execution to achieve optimal performance, strong security, and alignment with responsible AI principles. Key takeaways from the whitepaper: 1️⃣ Choose the right model for your specific use case to ensure effectiveness. 2️⃣ Customize models with your data and import your own models for tailored solutions. 3️⃣ Enhance accuracy by grounding foundation models with retrieval systems. 4️⃣ Integrate external systems and data sources to create powerful AI agents. 5️⃣ Ensure responsible AI practices by safeguarding foundation model responses. 6️⃣ Strengthen security and protect privacy in applications powered by foundation models. This whitepaper is a must-read for anyone building the future of AI applications. 💡 Add your thoughts in the comments—how are you incorporating security and reliability into your AI projects? ---------------------- Sarveshwaran Rajagopal #GenerativeAI #AmazonBedrock #AIApplications #ResponsibleAI

"Building #AgenticAI with #AmazonBedrock AgentCore and #DataStreaming Using Apache Kafka and Flink" At #AWS Summit New York 2025, Amazon launched Bedrock #AgentCore—a secure, scalable platform to build and operate enterprise-grade Agentic AI systems. But here’s the key insight: LLMs and orchestration tools are only half the story. To truly observe, reason, and act in real time, agents need an event-driven architecture. That’s where #ApacheKafka and #ApacheFlink become essential. Agentic AI is not about synchronous API calls. It’s about autonomous, always-on software that continuously listens to business events and triggers the right action at the right time—across domains like fraud detection, personalization, supply chain, and IT ops. Kafka provides the real-time event backbone. Flink adds continuous intelligence and stateful processing. With support for open protocols like #MCP (Model Context Protocol) and #A2A (Agent-to-Agent), this architecture enables scalable, collaborative agents that can span tools, teams, and clouds. If you’re building #autonomous agents that actually run in production, you can’t afford to ignore the streaming layer. How is your organization preparing its architecture to support long-running, autonomous AI agents at scale? Learn more in my latest blog post: https://lnkd.in/etRJGNsV

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗔𝗚-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 I recently worked on a 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) based Proof of Concept (POC) to streamline financial research and portfolio generation using 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗮𝗻𝗱 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝘀. Here's a quick breakdown of the implementation: 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗲𝗽𝘀: 1️⃣ 𝗨𝗽𝗹𝗼𝗮𝗱 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗗𝗮𝘁𝗮(𝗥𝗲𝗽𝗼𝗿𝘁𝘀) 𝘁𝗼 𝗦𝟯 Set up an S3 bucket and upload company reports for data retrieval. 2️⃣ 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝗶𝗻 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 Enable models (𝗖𝗹𝗮𝘂𝗱𝗲 𝟯 𝗛𝗮𝗶𝗸𝘂, 𝗧𝗶𝘁𝗮𝗻 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗩𝟮) and create a knowledge base linked to the S3 bucket. 3️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗗𝗮𝘁𝗮 The Lambda function serves as a backend API for the AI agent that will be created to access and retrieve company-related data. 4️⃣ 𝗦𝗲𝘁 𝗨𝗽 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁 & 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 Create an agent in Amazon Bedrock with defined action groups (e.g., /companyResearch, /createPortfolio). Customize prompts for precise orchestration and output. 5️⃣ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝘄𝗶𝘁𝗵 𝗔𝗴𝗲𝗻𝘁 Link the knowledge base to the agent and configure handling instructions for seamless interaction. 6️⃣ 𝗦𝘆𝗻𝗰 𝘁𝗵𝗲 𝗞𝗕 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 Sync Knowledge base and prepare the agent for real-time enhancements. 7️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 𝗔𝗽𝗽 𝗼𝗻 𝗘𝗖𝟮 Host an interactive AI-driven app by running a Streamlit application on EC2, enabling users to explore insights via an external URL. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 This AI Agent simplifies analysis by automating research, generating tailored portfolios, and summarizing documents—all while adapting to user feedback for better accuracy. 💬 Have you built an AI agent yet? Let’s connect and share ideas! #AIInnovation #GenerativeAI #RAG #AmazonBedrock #MachineLearning

Amazon Bedrock Knowledge Bases now make it easier than ever to build Retrieval Augmented Generation (RAG) workflows by enabling direct, real-time ingestion of streaming data from Apache Kafka using custom connectors. This new capability allows developers to add, update, or delete information in their knowledge bases instantly via API calls, eliminating the need for intermediary storage or time-consuming full syncs. Whether you’re working with clickstream analytics, IoT sensor data, or financial market trends, this approach delivers faster access to up-to-date information, reduces latency, and streamlines the process of building generative AI applications. The blog post demonstrates how to set up a generative AI stock price analyzer that leverages Amazon Managed Streaming for Apache Kafka and Bedrock Knowledge Bases, highlighting how organizations can now power AI-driven insights with fresh, contextual data at scale. #Bedrock #GenAI #ApacheKafka #AmazonMSK https://lnkd.in/gjGAr8Rc

Claude 4 just changed the game for business AI. Built by Anthropic and now available through Amazon Bedrock, this model isn’t just smart. It’s built for real work. For strategy. For execution. For security. And most of all, for results. Here’s what makes Claude 4 different and why I’m recommending it to clients right now: 1. Deep reasoning with long memory Claude 4 can understand and respond to full-length business documents like strategy decks, legal agreements, and product plans without losing context. It sees the big picture and the fine print at the same time. 2. Enterprise-level coding and beyond Yes, Claude is excellent at code. But it’s not just for developers. Use it to draft internal playbooks, summarize complex reports, design onboarding flows, or build training guides. It works across departments. 3. Directly inside AWS If your business already lives in AWS, you don’t need to copy or move sensitive data into public tools. Claude is inside Amazon Bedrock. Your AI stays compliant, secure, and scalable within your existing environment. 4. Ethically designed and trustworthy Trained using Anthropic’s Constitutional AI approach, Claude is built with safety and transparency at the core. That matters more than ever as companies apply AI in regulated or high-stakes environments. Claude 4 is not a flashy toy. It is a serious business tool for teams that want to move faster, reduce friction, and think at scale. But like any tool, it only works if you use it right. Learn how to prompt well. Train your team. Build processes that integrate Claude into your operations. That is where the competitive edge starts. If you want help bringing Claude into your business the right way, let’s talk. It’s time to lead with the right tools used the right way. Do you have an AI Strategy???