From manual to AI-driven monitoring: The evolution of observability

🚨 $1M/hour. That’s the cost of downtime, according to IDC. And yet, most orgs still rely on manual incident response in sprawling hybrid environments. That has to change. At LogicMonitor, we’re thrilled to partner with IBM Watsonx and Red Hat Ansible to launch a new era of self-healing infrastructure. 🔁 Together, we’re building an agentic AIOps loop: - LogicMonitor (Edwin AI): Real-time context & root cause detection - IBM watsonx: Translates insights into custom remediation code - Red Hat Ansible: Executes fixes—autonomously, securely, and at scale The result? ➡️ Faster resolution ➡️ Fewer outages ➡️ Freed-up engineering time to focus on innovation, not fire drills We’re inviting early adopters to join our autonomous operations pilot. Let’s move beyond alerts—and into action. ⚙️💡 #AIOps #Automation #SelfHealingSystems #LogicMonitor #IBMwatsonx #RedHat #Observability #ITOps #DevOps #HybridCloud #AI

Add awesome parallel orchestration to your claude coding sessions! 🚀 I've built Orchestr8 to bridge the gap between AI potential and enterprise reality - a comprehensive system that transforms Claude Code into an autonomous software engineering organization. 72 specialized agents. 19 autonomous workflows. 8 reusable skills. Full enterprise compliance including FedRAMP, ISO 27001, and SOC2. The breakthrough is the meta-orchestration capability. The system creates its own agents, workflows, and skills, moving beyond static AI tools toward truly adaptive enterprise systems. For cloud infrastructure teams, this represents a fundamental shift: from managing individual AI interactions to orchestrating autonomous engineering processes that maintain compliance standards from requirements to production. The security and compliance implications are game-changing. When AI systems can self-extend while maintaining enterprise-grade controls, we're looking at a new paradigm for scalable, compliant automation. Ready to transform your development workflow? Check out the full system: https://lnkd.in/g9EXQPsN How do you see autonomous AI orchestration fitting into your enterprise architecture? 🤔 #CloudInfrastructure #EnterpriseAI #SecurityCompliance #AIOrchestration #AutonomousSystems #DevOps #CloudSecurity #EnterpriseAutomation

🌐 𝐓𝐫𝐮𝐞 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐬 𝐭𝐡𝐫𝐞𝐞 𝐩𝐢𝐥𝐥𝐚𝐫𝐬 𝐦𝐞𝐭𝐫𝐢𝐜𝐬, 𝐥𝐨𝐠𝐬, 𝐚𝐧𝐝 𝐭𝐫𝐚𝐜𝐞𝐬 Metrics show what’s 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 📊, 𝐥𝐨𝐠𝐬 𝐫𝐞𝐯𝐞𝐚𝐥 𝐰𝐡𝐲 🧩, and 𝐭𝐫𝐚𝐜𝐞𝐬 show where issues 𝐬𝐭𝐚𝐫𝐭 🧭. Tools like 𝐏𝐫𝐨𝐦𝐞𝐭𝐡𝐞𝐮𝐬, 𝐆𝐫𝐚𝐟𝐚𝐧𝐚, and 𝐎𝐩𝐞𝐧 𝐓𝐞𝐥𝐞𝐦𝐞𝐭𝐫𝐲 combine these signals to create a comprehensive view of 𝐬𝐲𝐬𝐭𝐞𝐦 𝐡𝐞𝐚𝐥𝐭𝐡. The result is not just visibility, but also predictability, and that defines modern engineering excellence. #Observability #Prometheus #Grafana #OpenTelemetry #DevOps #EuropeTech #USTech

Thinking about adopting 𝐌𝐂𝐏? Most teams, when they learn about 𝐌𝐂𝐏, rush complexity instead of mastering flow first. They jump straight to agents, automation, orchestration, and miss the real value: building something that actually works. The smarter way to start is simple; 𝐨𝐧𝐞 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐟𝐥𝐨𝐰. Connect a model to a single API or dataset. Test it. Learn how 𝐌𝐂𝐏 connects tools, data, and workflows. Then layer up: 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 → 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 → 𝐒𝐞𝐜𝐮𝐫𝐞 → 𝐆𝐨 𝐋𝐢𝐯𝐞 Start with low-code tools like n8n or Zapier to understand the flow. Once it works, connect to your real systems, add OAuth, logging, and compliance, and then deploy with DevSecOps confidence. MCP isn't about going big on day one. It's about starting small, learning fast, and scaling with confidence. 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 𝐟𝐚𝐬𝐭. 𝐒𝐞𝐜𝐮𝐫𝐞 𝐥𝐚𝐭𝐞𝐫. 𝐆𝐨 𝐋𝐢𝐯𝐞 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. #AIEngineering #MCP #ScalableAI #EnterpriseAI #AIIntegration #SoftwareLeadership

Active telemetry is revolutionizing SRE in Kubernetes, unleashing the potential of AI-driven autonomy 🚀 Say goodbye to traditional, passive data hoarding. Active telemetry sweeps in, transforming raw information into “decision-ready” signals right at the point of data entry. This breakthrough is not just about automation, it's about empowering Site Reliability Engineers [SRE] with a supercharged pipeline that acts as both a watchdog and a problem-solver. 🟠 **Real-time Processing and Routing:** No more delays! Data is instantly processed and routed, ensuring that every signal is sharp, immediate, and devoid of noise. This means rapid and precise responses, every time. 🟠 **Context Engineering:** As telemetry data is contextualized in real-time, the full picture of system status is always available. Agents can finally understand the 'why' behind failures, not just the 'what'. 🟠 **Noise Reduction to the Max:** Slash irrelevant data while preserving critical performance insights—up to 50% of data can be pruned without losing context. Efficiency, meet clarity. Active telemetry isn't just a tech buzzword; it's reshaping the landscape from passive observability to dynamic actionability. It's the foundation on which SREs can proactively initiate solutions, instead of reacting to crises. Are your systems primed for this intelligent shift, or are you still wading through data swamp? #Kubernetes #SRE #Telemetry #AIAutomation 🎯 🔗https://lnkd.in/dtG2S8Br 👉 Post of the day: https://lnkd.in/dACBEQnZ 👈

🧩 [𝙋𝙖𝙧𝙩 𝟯/𝟱] 𝗙𝗿𝗼𝗺 𝗧𝗮𝘀𝗸 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 🚀 The impact of Deep Agents isn’t subtle — it’s architectural. 🔹 Shallow Agents → Task Automation 🔹 Deep Agents → Process Autonomy Examples: 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: from summarizing schema drift → to self-updating data contracts📝 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: from FAQ bot → to agent that closes tickets end-to-end ✅ 𝗠𝗟𝗢𝗽𝘀: from monitoring accuracy → to retraining and redeploying automatically🔄 They handle the 𝘸𝘩𝘢𝘵, the 𝘩𝘰𝘸, and the 𝘸𝘩𝘦𝘯. Next up — how this shift changes the architecture itself. That’s not a copilot. That’s an AI system that runs on intent. #AIinEnterprise #Automation #DataEngineering #MLOps #AgenticWorkflows

Why n8n can’t go to production (and that’s okay) I’ve seen this question pop up many times lately: “Why not just use n8n (or similar tools) to orchestrate our AI agents or workflows?” Short answer: because production systems have very different constraints than demos. n8n is an amazing prototyping tool; fast to iterate, great for visualizing flows, perfect for internal automation. But once you move toward multi-tenant, event-driven, CI/CD-deployed AI systems, the assumptions break down: • No version control or migration path: you can’t GitOps it, review it, or safely roll back. • Limited observability: good luck debugging when a graph runs across hundreds of executions. • Opaque state: workflows hold transient data without deterministic re-runs. • No schema governance: inputs and outputs drift silently over time. • Single-tenant mindset: scaling across environments becomes painful fast. These are not bugs, they’re design trade-offs. n8n was never meant to be a platform backbone; it’s a tooling layer for humans, not an execution layer for production agents. And that’s totally fine. The value of tools like n8n is in thinking visually, iterating on logic before encoding it into your architecture. But when you want repeatability, traceability, and reliability, you need a graph, not a flow, something declarative, versioned, deployable, observable. That’s where orchestration frameworks like LangGraph or your own event-driven runtime come in. In short: • Use n8n to discover the workflow. • Use orchestration to deliver it. #AIEngineering #LangGraph #AIOperations #AgentOrchestration #WorkflowAutomation #LLMInfrastructure #MLOps #AIArchitecture #DevOps #SoftwareEngineering #n8n

🚀 Supercharging SRE with Human-Like Multi-Agentic AI Last week at Acceler8IT in Gdańsk, Krzysztof Stec and I shared our vision for the future of Site Reliability Engineering. We started with a challenge: how can lean SRE teams keep pace with the flood of alerts, performance data, and incidents? We explored what the ideal SRE setup looks like KPI-driven dashboards, smart AIOps, automated remediation, and predictive self healing. But reality often means too few engineers and too many manual steps. Our answer? A human like multi agent AI assistant that sits at the heart of your stack. It orchestrates across your documentation, ALM, ITSM, databases, logs, email, and a reasoning LLM all under responsible AI guardrails. We demoed a working prototype built on Ignis, our enterprise ready agentic platform based on LangFlow. It features: 🔍 Full observability & logging 🤝 Collaboration & flow sharing ✅ Governance & audit controls 🔐 Security (RBAC, SSO, JWT, container isolation) ⚙️ Pre-built flows & extensibility ☁️ Scalable, modular architecture The big idea? Tomorrow’s SRE teams will be a blend of humans and AI agents working together to reduce toil, increase reliability, and automate incident lifecycles end-to-end. If you missed the session or want to explore how multi-agent orchestration could transform your ops, let’s connect! #Acceler8IT #SRE #AIOps #MultiAgentSystems #AIOrchestration #LangFlow #Ignis #DevOps #Observability #Automation

You know this meeting. The whiteboard is a disaster, the coffee is cold, and you've spent days debating infrastructure without writing a single feature. This is the silent tax on innovation. We've somehow normalized that development teams spend **80%** of their time on this foundational maze: authentication, database schemas, CI/CD pipelines, API boilerplate. It’s weeks, sometimes months, of undifferentiated heavy lifting before the *real* work even begins. What we're looking at here isn't a failure of talent. It's a failure of process. The real bottleneck has always been the blank repository and the mountain of setup that comes with it. We hire brilliant innovators and inadvertently turn them into infrastructure janitors, burning their best energy on problems that have been solved a thousand times before. This is the exact friction that AI automation is eliminating. With platforms like TheSSS.AI, the entire "6 months to production" model is collapsing into **5 days**. It works by generating the complete, production-ready foundation, flipping the script so your team spends 85% of its time on unique business logic, not boilerplate. It's time we started measuring velocity not by how complex our setup diagrams are, but by how quickly we get from an idea to real user feedback. What’s the one foundational task you wish you could automate away forever? #SoftwareDevelopment #EngineeringLeadership #DeveloperProductivity #AI #DevOps #CTO #Innovation

📊 Workflow Automation: The Development Trend You Can't Ignore Workflow automation has been THE conversation across engineering teams. Here's why: 🔹 AI-Powered DevOps AI agents are embedded in CI/CD—automating test generation, intelligent rollbacks, and predictive deployment monitoring. 🔹 Low-Code Workflow Orchestration Tools like n8n, Temporal, and Airplane.dev are booming. Engineers are building production-grade automation without drowning in boilerplate. 🔹 Security-as-Code Automated vulnerability scans, compliance audits, and threat modeling are now standard in modern pipelines—not optional. Automation isn't just a DevOps buzzword anymore. It's infrastructure. What tools are you using to automate your workflows? Drop your stack below 👇 #DevOps #WorkflowAutomation #CICD #AIinTech #InfrastructureAsCode #CloudEngineering