Devoteam | AWS Partner’s Post

The AWS Prescriptive Guidance team has published a comprehensive resource on building agentic AI systems in cloud-native environments. The document, authored by Aaron Sempf and Joshua Samuel (Amazon Web Services, July 2025), explores key components for designing and deploying AI agents, including: Frameworks – Comparative analysis of Strands Agents, LangChain/LangGraph, CrewAI, Amazon Bedrock Agents, and AutoGen. Protocols – Guidance on adopting open standards such as the Model Context Protocol (MCP) and Agent2Agent (A2A) for interoperability and long-term flexibility. Tools – Strategies for integrating protocol-based tools, framework-native options, and meta-tools to extend agent capabilities securely. The publication highlights how organizations can leverage autonomous AI agents to automate workflows, enhance decision-making, and improve system responsiveness across distributed cloud environments. Full document: How to Build AI Agents in Cloud-Native Systems (AWS Prescriptive Guidance, 2025) What are your thoughts on the future adoption of open protocols like MCP in enterprise AI ecosystems? #ArtificialIntelligence #CloudComputing #AIagents #AWS #smenode #smenodelabs #smenodeacademy

🚀 AWS is shaping the future of AI with production-ready agents at scale! 🤖☁️ Swami Sivasubramanian, VP for Agentic AI at AWS, shared how AI agents are transforming industries by combining agility, security, reliability, and scale. 💡🔒⚙️📈 ✨ Key AWS Innovations: 🧠 Amazon Bedrock AgentCore → Secure, serverless runtime for deploying AI agents at scale 🔧 Amazon Nova Customization → Fine-tune models for industry-specific use cases 📦 Amazon S3 Vectors → First cloud object store with native vector support, cutting vector costs by 90% 💸 🛍️ AI Agents in AWS Marketplace → Enterprise-ready solutions for faster adoption 🤝 Kiro & AWS Transform → Helping businesses modernize, innovate, and scale faster 💼 What this means for companies: ⚡ Faster time-to-production for AI solutions 🔍 Freedom to choose models + integrate proprietary data 🚀 Transformative workflows powered by cloud-native AI #AWS #AI #AgenticAI #Bedrock #GenAI #CloudComputing #Innovation #Scalability #DigitalTransformation #FutureOfAI #EnterpriseAI #MachineLearning #AWSMarketplace

I've seen it countless times: a client walks in, buzzing with excitement, thinking we can flip a switch and have revolutionary Generative AI running instantly. The reality? In production, "move fast and break things" is just moving fast to create a massive, expensive mess. Migrating your AI workloads to the cloud, specifically AWS, isn't a single magical leap. It's a strategic transformation - moving your AI capabilities from a hopeful possibility to a concrete reality, one well-planned step at a time. Yes, we charge premium rates, and that's because we bring a non-negotiable level of rigor. We don't have a magic wand, but we do see the pitfalls and complexities that you might be overlooking. Our value is in delivering predictable, scalable, and secure results. To help set realistic expectations (and avoid the "instant AI" myth), we approach Gen AI migrations by classifying the complexity: 𝟏. 𝐒𝐢𝐦𝐩𝐥𝐞 𝐀𝐏𝐈 𝐄𝐧𝐝𝐩𝐨𝐢𝐧𝐭 𝐒𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 - Timeline: 1 to 4 Weeks - What it is: A basic transition from externally hosted AI endpoints to Amazon Bedrock. - Positioning: "Quick Wins with Minimal Disruption." This is perfect for validating AWS benefits and achieving immediate, low-effort results. You can even switch back if needed. 𝟐. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐖𝐨𝐫𝐤𝐥𝐨𝐚𝐝 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 - Timeline: 1 to 3 Months - What it is: For customers who need customization like fine-tuning models on their proprietary data. - Positioning: "Enhanced Capabilities While Maintaining Business Continuity." 𝟑. 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 - Timeline: 4 to 6 Months - What it is: The full journey: complex implementations including data dependencies, RAG (Retrieval Augmented Generation) components, and autonomous agents. - Positioning: "Comprehensive Transformation for Long-Term AI Success." My team specializes in guiding you through this structure, ensuring we always prioritize stability, security, and true business value over rushed implementation. If you're ready to move past the hype and build a realistic, high-impact Gen AI strategy on AWS, drop a comment or send me a DM. Let's talk about where your vision fits on this timeline. #GenAI #AWS #CloudMigration #AmazonBedrock #AIStrategy #DigitalTransformation

Generative AI is delivering measurable results for enterprises. The real value comes when productivity gains can be quantified time saved, workflows streamlined, and faster decisions at scale. Our latest post breaks down how AWS Generative AI deployments drive ROI in clear business terms, giving leaders confidence to invest and expand. 🔗 Read the full blog here: https://lnkd.in/gZ2TAR-a #GenerativeAI #AWS #Productivity #ROI #CloudTransformation #Easycoder

What happens when AI models get confused about where they're running? 🤔 Amazon Bedrock just introduced global cross-region inference with Claude Sonnet 4.5, and it's changing how we think about AI infrastructure. Instead of being locked to specific regions, your inference requests can now route globally across AWS's commercial regions—automatically finding available capacity worldwide. 📊 Key benefits emerging: • Enhanced throughput during peak demand • 10% cost savings on token pricing • On-demand quota flexibility across regions This matters because traditional approaches require complex client-side load balancing and leave you vulnerable to regional capacity limits. AWS Bedrock now handles traffic spikes automatically while maintaining centralized monitoring through CloudWatch and CloudTrail in your source region. The intelligent routing considers model availability, capacity, and latency to optimize each request without manual configuration. For organizations running business-critical AI applications, this removes a major infrastructure headache while actually reducing costs. 🎯 Ready to level up? Dive into this comprehensive guide (https://lnkd.in/gNVrEGMy) What's your biggest challenge with AI infrastructure scaling—regional capacity limits or cost optimization? #AWS #ArtificialIntelligence #CloudInfrastructure #MachineLearning #AnthropicClaude

Implementing AI Agents in your org? From automating operations to powering real-time decision-making, AI agents are becoming the new enterprise backbone. And AWS is leading the way. With tools like Bedrock, SageMaker, and Lambda, AWS is enabling teams to build, deploy, and scale agents with unmatched control and reliability. In our new e-guide “AI Agents at Scale”, we dive deep into AWS’s world of agentic AI. In the guide, we break down how organizations are: → Designing purpose-built AI agents using AWS tools → Embedding security, trust, and guardrails from day one → Turning prototypes into scalable, production-grade systems If you’re leading AI transformation in your organization, this is a must-read. Download the full guide: https://lnkd.in/gRWTBf2n Want to get hands-on? Explore our AWS GenAI Essentials training: https://lnkd.in/gs3QGY6x #AWS #AgenticAI #GenerativeAI #AIAgents #CloudComputing #AWSBedrock #SageMaker #NetComLearning

Just published: Smart Scaling – How Small Businesses Can Leverage AI Cost-Effectively. Practical strategies, innovative use cases, and tools to make AI accessible for SMBs. #AIforBusiness #SmallBusinessTech #CloudDailywire #MicrosoftAI #AWS #NoCodeAI #DigitalTransformation

🚀 Amazon Web Services (AWS) S3 now supports vectors, enabling petabyte-scale AI data storage with up to 90% cost savings. Threat to vector #databases like Pinecone or Milvus? Not quite. #S3 Vectors excels for cold storage and simple searches, freeing specialized DBs for real-time, low-latency tasks and advanced features. This could democratize #AI and push innovation. Threat or opportunity? Share your thoughts! 👇 #AI #VectorDatabases #AWS #MachineLearning #backend

Google Cloud recently unveiled practical blueprints showcasing how GenAI is transforming industries like customer support, software development, personalization, knowledge management, and healthcare. What’s exciting? These examples go beyond theory, offering reference architectures and technical workflows that make adopting GenAI actionable and accessible. 📖 Dive in: [Real-World Gen AI Use Cases with Technical Blueprints] https://lnkd.in/gctQ3k6E

Amazon Web Services (AWS) Quick Suite is a newly launched enterprise AI platform targeting business automation, research, and security needs. Consolidating AI tools like QuickSight and Amazon Q Business, Quick Suite offers an agentic AI workspace with research, process automation, and deep reporting features that connect to 1,000+ apps and internal databases. It stands out for robust enterprise-grade security and data privacy, ensuring business queries aren’t used for model training. Early adopters report up to 80% reduction in customer service ticket handling and 91% cost savings for analytics tasks. Priced competitively at $20/user/month, Quick Suite launches across four AWS regions with a free 30-day trial for up to 25 users. Read Full Article: https://lnkd.in/gy3aE7iR #AWS #AI #EnterpriseAI #DeccanFounders #Amazon #Microsoft #ChatGPT #Google #Gemini #Copilot