Cloud-Based Autonomous Solutions

Cloud Native technologies have long been at the heart of scalable applications. But now, with AI and Agentic Systems, the game is changing!   Unlike traditional AI automation, Agentic AI can make decisions, execute workflows, and adapt dynamically to system changes—without constant human oversight. This means self-healing, self-optimizing, and autonomous cloud-native infrastructure!  Here’s how Agentic AI can transform each layer of Cloud Native skills:  1. Linux & AI-Optimized OS   - AI-powered package managers automatically resolve compatibility issues.   - Agentic AI monitors system logs, predicts failures, and patches vulnerabilities autonomously.  2. Networking & AI-Driven Observability   - AI-driven network forensics using self-learning algorithms to detect anomalies.   - Agent-based routing optimizations, ensuring seamless traffic flow even in congestion.  3. Cloud Services & AI-Augmented Workflows   - Agentic AI predicts cloud workload demand and pre-allocates resources in AWS, Azure, and GCP.   - Autonomous cost optimization adjusts instance types, storage, and compute in real time.  4. Security & AI Cyberdefense Agents   - Self-learning AI security agents actively detect and mitigate cyber threats before they happen.   - Generative AI-powered penetration testing agents simulate evolving attack patterns.  5. Containers & Agentic AI Orchestration   - Autonomous Kubernetes controllers scale clusters before demand spikes.   - Agentic AI continuously optimizes pod scheduling, reducing cold starts and resource waste.  6. Infrastructure as Code + AI Copilots   - AI-driven infrastructure agents automatically refactor Terraform, Ansible, and Puppet scripts.   - Self-adaptive IaC, where AI updates configurations based on usage patterns and compliance policies.  7. Observability & AI-Driven Incident Response   - AI-powered anomaly detection in Grafana & Prometheus—flagging issues before failures.   - Agentic AI handles incident response, running diagnostics and executing pre-approved fixes.  8. CI/CD & Autonomous Pipelines   - Agentic AI writes, tests, and deploys code autonomously, reducing developer toil.   - Self-optimizing pipelines that rerun failed tests, debug, and retry deployment automatically.  The Future: Fully Autonomous Cloud Native Systems!  𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 → 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The result? Zero-touch, self-managing environments where AI agents handle failures, optimize costs, and secure systems in real time.  𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘆𝗼𝘂’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆?

🌐 The future of cloud management is here – AI agents are revolutionizing how we operate at Belcorp! 🚀 We’re leveraging autonomous cloud agents from Sedai, and the results speak for themselves: 💰 27% reduction in cloud costs for our AWS account – impactful savings at scale! ⚡ 26% latency reduction in our Lambda functions, saving us over 6 years of processing time – that’s real speed! One of the most fascinating things about these AI agents is their adaptability. Just like our work with generative AI in beauty tech, we’re fine-tuning how they manage our cloud infrastructure: 🤖 Autonomous Mode: Now handling 43% of our resources completely independently 🤝 Collaborative Mode: Partnering with our engineers, executing with approval 🔍 Insight Mode: Offering AI-driven recommendations for our team to evaluate Here’s where AI has identified additional opportunities: ✦ 43% savings potential on EC2 VMs ✦ 24% savings potential on ECS containers ✦ 30% savings potential on EBS storage A big shoutout to our innovative team – Miguel Tenorio Leyva, Jose Alcibiades Salinas Cari, and Edgardo Cornejo  – for driving these new technologies forward! Their work is essential as we get ready for the holiday season. What excites me most? How AI enhances our team’s expertise, allowing us to focus on strategic initiatives and exploring even more AI-driven opportunities across our business. How are you incorporating AI and autonomous agents into your infrastructure? What’s been your experience? Let’s discuss! #AIinTech #AutonomousAgents #CloudOptimization #DigitalTransformation #TechForGood

 Cloud Management: LLMs Pave the Way for Self-Managing Systems ... The study, titled "The Vision of Autonomic Computing: Can LLMs Make It a Reality?", explores the potential of Large Language Models (LLMs) to realize the long-standing vision of self-managing computing systems. 👉 The Promise of Autonomic Computing: Past vs. Present For over two decades, the tech industry has pursued the dream of autonomic computing - systems that can manage themselves with minimal human intervention. - Traditional Approach: Rule-based systems and predefined policies - LLM-based Approach: Adaptive, context-aware decision-making leveraging vast knowledge 👉 Key Innovations: Breaking New Ground The researchers have developed a novel framework that leverages LLMs in a hierarchical multi-agent system: 1. Traditional Kubernetes: Relies on separate tools for monitoring, scaling, and troubleshooting 2. LLM-enhanced Framework:    - High-level group manager for overarching coordination   - Low-level autonomic agents for granular control of individual microservices 👉 Measuring Progress: A New Taxonomy To evaluate effectiveness, the team introduced a five-level taxonomy for autonomous service maintenance: 1. Simple Step Following 2. Deterministic Task Automation 3. Proactive Issue Detection 4. Automatic Root Cause Analysis 5. Full Self-Maintenance Unlike previous benchmarks focused on specific tasks, this taxonomy provides a clear roadmap for advancing autonomous systems across multiple dimensions. 👉 Real-World Testing: The Sock Shop Benchmark - Traditional Testing: Often relies on simulated environments or limited test cases - This Study: Created a live evaluation benchmark using the Sock Shop microservice demo The results? Their system achieved Level 3 autonomy, demonstrating effective proactive issue detection and resolution in a realistic, dynamic environment. 👉 Implications for Cloud Computing This research has far-reaching implications for the future of cloud services: - Current Practice: Human operators manage most aspects of cloud infrastructure - LLM-enhanced Future:  - Enhanced reliability through faster issue detection and resolution  - Improved scalability to handle increasingly complex distributed systems  - Reduced operational costs by minimizing the need for human intervention  - More efficient resource utilization through intelligent, autonomous management  👉The Road Ahead: Challenges and Opportunities While achieving Level 3 autonomy is a significant milestone, there's still work to be done: - Current Limitations: Root cause analysis and complex issue mitigation need improvement - Future Potential: This study lays a solid foundation for advancing towards fully autonomous cloud management 👉 Why This Matters This research demonstrates: - Traditional Approach: Siloed tools and human-intensive management - LLM-driven Future: Integrated, adaptive, and increasingly autonomous cloud infrastructure management

We’re moving from operating software to operating alongside systems that can reason and act. Today we’re making that real with the general availability of AWS DevOps Agent and AWS Security Agent — part of a new class of systems we call frontier agents. Unlike traditional tools, frontier agents don’t just respond to prompts — they work autonomously across multiple steps to achieve outcomes, operating continuously until the job is done. One helps you run cloud operations — investigating incidents, reducing time to resolution, and preventing issues before they happen. Customers like United Airlines, Western Governors University, and T-Mobile are already using DevOps Agent to accelerate incident response and simplify operations at scale. At WGU, resolution time dropped from hours to minutes, and in preview customers report up to 75% lower MTTR and 3–5x faster resolution. The other helps you secure them — bringing continuous, context-aware penetration testing into the development lifecycle. Customers including LG CNS, HENNGE, and Wayspring are seeing strong results. At LG CNS, teams estimate over 50% faster testing and ~30% lower costs, along with significantly fewer false positives. Both are designed to work across Amazon Web Services (AWS), multicloud, and on-prem environments. The goal is simple: give teams an always-available teammate that can handle the heavy lifting, so builders can focus on what matters most. We’re still early, but this is a big step toward more autonomous, resilient systems. Learn more: https://lnkd.in/em-eeJwc

AI Agents Are Becoming Cloud-Native — And It’s Changing Everything AI agents are no longer simple chatbots. They’re autonomous systems that can reason, plan, collaborate, and take real actions across distributed cloud environments. ☑️ AWS’s latest guidance highlights three pillars shaping this new era: ✨ Frameworks — Strands Agents, LangGraph, CrewAI, AutoGen, Bedrock Agents ✨ Open Protocols — especially MCP, enabling agent-to-agent and agent-to-tool interoperability ✨ Tooling Ecosystems — from native tools to MCP-based remote tools, all secured with OAuth, guardrails, and observability ☑️What stood out most: 🔹 MCP is emerging as the interoperability standard for multi-agent systems 🔹 Strands Agents + Bedrock provide deep AWS-native integration for enterprise-scale automation 🔹 LangGraph & CrewAI shine for complex workflows and multi-agent collaboration 🔹 The future is composable agents—independent, asynchronous, and secure ☑️AI agents will reshape cloud architectures the same way microservices reshaped software. ☑️The organizations that invest now in protocols, tooling, and observability will be the ones that win the next decade. #AIAgents #CloudNative #AWS #MCP #AgenticAI #GenerativeAI #AIArchitecture #LangChain #Bedrock #StrandsAgents #AutoGen #CrewAI

📊 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Real Enterprise Stories of Autonomous Cloud Optimization 🎯 While fragmented FinOps tools leave significant savings on the table and create incident risks, Sedai's AI-powered autonomous optimization is delivering transformative results. Here's how leading enterprises are taking advantage of a new way to manage cloud resources: 💫 𝗣𝗮𝗹𝗼 𝗔𝗹𝘁𝗼 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 on GCP → Kubernetes optimization across Dev/Test & Production → 46% cloud cost reduction → $3.5M projected value → Full compliance with automated safety controls → Complete optimization audit trail 🚀 𝗞𝗻𝗼𝘄𝗕𝗲𝟰 on AWS → Mixed workload optimization: 3,000 ECS services & 2,500 serverless functions → 27% cloud cost reduction → 5-month ROI achievement → Autopilot running on 98% of services → Advanced Infrastructure-as-Code integration ⚡ 𝗧𝗼𝗽 𝟭𝟬 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 → On-prem Kubernetes on VMware → 35% compute cost reduction → Eliminated overprovisioning → Automated safety controls 💊 𝗧𝗼𝗽 𝟭𝟬 𝗣𝗵𝗮𝗿𝗺𝗮 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 on Azure → VM-focused optimization where containerization wasn't suitable → 28% cloud cost reduction → 90% reduction in optimization effort → Vertical rightsizing focus 🎯 𝗧𝗵𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗶𝘀 𝗖𝗹𝗲𝗮𝗿: → Significant cost savings (27-46%) across all platforms → Applicability across cloud-native and traditional workloads → Autonomous operations with strong safety controls → Enterprise-grade integrations, compliance and governance → Flexible deployment models (Autopilot/Copilot) to match readiness → Enables platform teams to focus on strategic initiatives

What enterprises need isn’t more diffuse agentic workloads. They need coordinated, governed agentic workloads, where this architectural pattern makes business sense, that coordinate the incumbent systems that run their businesses. They also need agentic workloads whose north star goals are the rapid, iterative generation of clear business outcomes. These truisms guide what we announced this week with Google Cloud. We’re moving from siloed AI to autonomous operations — where agents collaborate across 5G networks, retail environments, as representative examples, and the systems that power these operations to detect, diagnose, and resolve issues before they ever become incidents. An end-to-end chain from signal capture & processing, to reasoning, to governed & secure resolution. Some incremental guiding principles that are driving our co-innovation roadmap: 1️⃣ Interoperability. Agentic workloads that communicate and act across incumbent systems in real time, powered by open & increasingly foundational protocols like MCP, A2A, and A2UI. 2️⃣ Data reachability maximization. Insight is powered by data, and our partnership with Google Cloud embraces this principle by maximizing data reachability, across multiple form factors, from ETL/ELT, zero copy access, streaming, change data capture, and AI-assisted semantic integration. These resulting data-powered insights are optimally valuable when they trigger governed, secure action, derived based on organization-specific context. 3️⃣ Governance at the core. As agentic systems scale, you need a control plane that knows what every workload is doing, what data it’s touching, and whether it’s operating within the guardrails of prevalent internal policy & external regulations. This is how one moves fast while staying in control. Put it all together and you get agentically powered business value delivered: ✅ Networks that heal themselves before customers notice ✅ Stores that fix equipment before they fail ✅ IT systems that resolve issues before tickets are created This is the shift from automation to autonomy. It only happens when platforms work in lockstep. Proud of what our teams at ServiceNow and Google Cloud are building together — and even more excited about where our co-innovation takes us next. Innovation = Invention + Execution. Radhika Rengaswamy Josyula, Neeraj Jain, Alexander Vukovic, Alix Douglas, Hasan Kidwai, Kevin Ichhpurani, Francis deSouza, Thomas Kurian, Seth Siciliano, Rodrigo Rocha Learn more: https://lnkd.in/g2CWjRwG