TelcoGPT: AI for network operations, diagnostics, and more

Agent Operations (Agent Ops), the continuous measurement, debugging, and governance of AI agents in production. The next evolution of DevOps + Product Ops: >Observability through telemetry and traces >Evaluation with LM-as-Judge frameworks >Live A/B testing for prompts, context windows, or actions >Feedback ingestion from human escalation signals You can implement this as part of your AI product lifecycle governance, something most companies haven’t even defined yet.

As AI coding tools flood development pipelines, the real bottleneck has shifted: delivery, security, and reliability. That’s where Harness steps in. At {unscripted} CEO Jyoti Bansal unveiled new agentic DevSecOps capabilities built to accelerate everything after code — from pipeline creation and testing to rollback and remediation. These AI agents are powered by Harness’s knowledge graph, enabling smarter, context-aware automation across DevOps, SRE, AppSec, and FinOps. They’re already running in production for 100+ enterprise customers. 👉 Learn more about how Harness is bringing trustworthy autonomy to software delivery: https://lnkd.in/gxYh82is

As AI coding tools flood development pipelines, the real bottleneck has shifted: delivery, security, and reliability. That’s where Harness steps in. At {unscripted} CEO Jyoti Bansal unveiled new agentic DevSecOps capabilities built to accelerate everything after code — from pipeline creation and testing to rollback and remediation. These AI agents are powered by Harness’s knowledge graph, enabling smarter, context-aware automation across DevOps, SRE, AppSec, and FinOps. They’re already running in production for 100+ enterprise customers. 👉 Learn more about how Harness is bringing trustworthy autonomy to software delivery: https://lnkd.in/dgrWH6m5

As AI coding tools flood development pipelines, the real bottleneck has shifted: delivery, security, and reliability. That’s where Harness steps in. At {unscripted} CEO Jyoti Bansal unveiled new agentic DevSecOps capabilities built to accelerate everything after code — from pipeline creation and testing to rollback and remediation. These AI agents are powered by Harness’s knowledge graph, enabling smarter, context-aware automation across DevOps, SRE, AppSec, and FinOps. They’re already running in production for 100+ enterprise customers. 👉 Learn more about how Harness is bringing trustworthy autonomy to software delivery: https://lnkd.in/eNr3SRqZ

𝗧𝗮𝗰𝗸𝗹𝗶𝗻𝗴 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗖𝗿𝗮𝘀𝗵𝗟𝗼𝗼𝗽𝗕𝗮𝗰𝗸𝗢𝗳𝗳 𝘄𝗶𝘁𝗵 𝗶𝗴𝗻𝗶𝗼 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗳𝗼𝗿 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 CrashLoopBackOff is one of the most common issues in Kubernetes-based applications. It occurs when containers repeatedly fail to start due to resource limits, application errors, or misconfigured volume mounts. Traditionally, resolving this involves manual log checks, event descriptions, and resource tuning - all of which increase Response Time and impact SRE performance KPIs. Here’s how ignio AI Agent changes the game:  • 𝗔𝗜/𝗠𝗟-𝗱𝗿𝗶𝘃𝗲𝗻 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 : ignio continuously monitors Kubernetes clusters, detecting anomalies like CrashLoopBackOff.  • 𝗥𝗼𝗼𝘁 𝗖𝗮𝘂𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝘁 𝗦𝗽𝗲𝗲𝗱 : Instead of spending hours digging through logs, ignio pinpoints the exact cause - whether it’s resource bottlenecks, misconfigurations, or application-level issues.  • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗺𝗲𝗱𝗶𝗮𝘁𝗶𝗼𝗻 : From scaling resources to correcting configurations, ignio contextually triggers self-healing actions instantly, reducing latency and improving MTTR (Mean Time to Resolution). 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗦𝗥𝗘 𝗞𝗣𝗜𝘀:  • Faster Response Time → Immediate detection and automated fixes.  • Improved Reliability → Proactive prevention of recurring incidents.  • Operational Efficiency → Freeing SRE teams from repetitive firefighting. With ignio, your Kubernetes environment becomes smarter, self-healing, and aligned with modern SRE goals. Join the demo waitlist : https://lnkd.in/gjR8XvFj #Kubernetes #SRE #AIOps #IncidentManagement #ignio #CrashLoopBackOff #Automation #DevOps

Automation is powerful, but only if you can see what it’s doing. As more organizations turn to AI-driven operations and smaller DevOps teams, one question becomes critical: who’s watching the automation? Real-time visibility and intelligent log management are no longer optional. They help teams detect anomalies early, reduce noise, and maintain control when headcount drops but complexity grows. LogZilla’s AI-powered platform turns raw data into actionable insight, helping teams stay ahead of issues before they become outages. 🔗 Revolutionizing Log Management: Harnessing the Power of AI with LogZilla: https://lnkd.in/g-4KKVuA #AIOps #LogManagement #Automation #Observability #DevOps #ITOperations #CloudSecurity #LogZilla

One Vague Prompt, 15 Minutes, Production Fixed My n8n automation server died. I had zero memory of how I'd set it up. I opened Claude Code and typed: "Can you check my 'axis' host? I normally run n8n there. But now it doesn't work. I already forgot how I set it up. It could be docker or k8s. I don't know." Claude SSH'd in, figured out it was K3s, found that pods couldn't start because of disk pressure, traced it to 4GB of journal logs, cleaned them up, restarted K3s, and verified everything was running. 15 minutes. I clicked "approve" once on the plan. Пощекотал нервы себе слегка. Watching AI run production commands is weird I sent the logs to a friend. His response: "I kept waiting for it to run rm -rf" Same. The whole time I was half-expecting something catastrophic. But also thinking: I would've fired half my admin team if I still had one. The actually interesting part It wasn't that Claude fixed the issue. It's that I gave it one informal sentence with zero context, and it just... figured everything out. No documentation. No runbook. I literally forgot whether I was using Docker or Kubernetes. My friend called it "vibe-based system administration" and said he's skeptical this scales beyond hobby projects. Fair. Watching those logs was stressful even knowing it was just my personal server. But here's what I think works: let AI do read-only investigation autonomously. Let it dig through logs, check configs, diagnose issues. Then require approval for anything that changes state. Basically an incredibly thorough engineer who investigates everything but asks before touching anything. I don't know if this is the future of DevOps. But it's definitely the future I want - where I can forget how I set something up and still fix it with one sentence. #AI #DevOps #Infrastructure #Automation #AIAgents

This whitepaper provides a comprehensive technical guide to the operational life cycle of AI agents, focusing on deployment, scaling, and productionizing. Building on Day 4's coverage of evaluation and observability, this guide emphasizes how to build the necessary trust to move agents into production through robust CI/CD pipelines and scalable infrastructure. It explores the challenges of transitioning agent-based systems from prototypes to enterprise- grade solutions, with special attention to Agent2Agent (A2A) interoperability. This guide offers practical insights for AI/ML engineers, DevOps professionals, and system architects.

You can't orchestrate what you can't see at the application layer. — Infrastructure teams have perfected container orchestration and built multi-cloud platforms. But there's a gap between what infrastructure 𝘤𝘢𝘯 𝘥𝘰 and what applications 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘯𝘦𝘦𝘥. It’s not as simple as adding more features or tools. Applications need intelligence at the delivery layer – where requests turn into responses, where APIs meet consumers, where traffic decisions happen. Without this visibility, infrastructure investments stay disconnected from every application. There are 5 main capabilities that define this layer: 1. 𝗔𝗣𝗜 & 𝗔𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Handle everything from API gateway functions to AI model routing, semantic caching, and safety guardrails. 2. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Identity-aware routing through protection protocols. 3. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Unified policy management across VMs and containers with auto-discovery, supporting gradual modernization. 4. 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻 𝗖𝗹𝗼𝘂𝗱 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 Complete operational independence with zero external dependencies, offline policy bundles, and audit-grade logging. 5. 𝗘𝗱𝗴𝗲-𝘁𝗼-𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆 Consistent behavior across distributed deployments with local enforcement during connectivity failures and central policy control. This layer connects infrastructure orchestration to application delivery. It's what makes infrastructure portable for ops teams AND applications. — At Traefik Labs, we empower demanding DevOps and Platform Engineering teams with a cloud-native, GitOps-driven API runtime infrastructure. DM me for more details!

Demystifying the critical shift from Traditional Monitoring to Modern Observability! On the left, see how monitoring tells you if something is broken (focusing on pre-defined metrics and reactive alerts). On the right, explore Observability, which empowers you to understand why it's broken, enabling dynamic probing, proactive insights, and deep system understanding. It's the evolution from asking 'what?' to discovering 'why?'. #Observability #Monitoring #DevOps #SRE #SystemUnderstanding #TechInsight #ShiftFromWhatToWhy