How AIOps is transforming MSPs with proactive IT

Why ‘Observability’ Matters Even When the Product Is Mature A common misconception is that observability is something you only need during the early growth phase of a system. But as a product evolves and becomes more stable, observability becomes even more important. Why? Because maturity brings complexity — more services, more dependencies, more edge cases, more users. And complexity is where issues hide. When your product reaches scale, your biggest risks are no longer simple bugs — they are unknown behaviors happening across distributed systems. What Mature Observability Enables 1) Faster Incident Detection Issues are caught before customers feel the impact. 2) Faster Root-Cause Analysis No more reading logs line-by-line at midnight. You trace the request path and find the problem. 3) Safer, Confident Deployments You know exactly what changed, how it behaves, and where it fails. 4) Data-Driven Technical Decisions Instead of guessing which service is slow, you see it. Key Components of Effective Observability Logging — to understand what happened Examples: Loki, ELK Stack, OpenSearch Metrics — to see performance and system health Examples: Prometheus, VictoriaMetrics, CloudWatch Tracing — to follow requests across multiple services Examples: Jaeger, Zipkin, OpenTelemetry ------ In a microservice environment, a single request may touch 10–30 services. Without tracing, debugging becomes guesswork. With tracing, you see the entire path and exactly where latency or failure occurs. ------- Distributed tracing deserves special mention. In modern microservice environments, a single request may travel across 20+ services. Without tracing, debugging becomes guesswork. With Jaeger or Zipkin, you see the entire journey — who called whom, where latency appeared, and where things broke. Engineering Practices to Mature Observability • Use structured logs, not random text messages • Create dashboards that reflect user experience, not raw internals • Define and track SLOs (Service Level Objectives) • Configure alerts for real impact, not noise • Establish an incident review process that improves—not blames Observability is not just tools. It is a habit of making systems easier to understand. 💬 At what stage did your team realize observability wasn’t optional anymore? Was it proactive — or was it after a painful outage? Or is this too "expensive" a thing to implement?

There’s a widespread misconception I keep seeing quite often: → Observability = APM Surprisingly, it’s not just developers , even EMs and CTOs get this wrong. And does it matter? Absolutely. Because the same misunderstanding is why many CXOs get sold “Observability” tools that are nothing more than glorified APM dashboards with prettier charts and vendor lock-in. The truth is: 1. You don’t need another fancy dashboard telling you what you already know. 2. You need insight - the ability to ask new questions without shipping new code. That’s what real Observability is about. And that’s why understanding the difference matters. I break this down in simple language in my latest article: 👉 To the layman: Observability is NOT APM 🔗 https://lnkd.in/gnFnkda8 If you’re into infra, SRE, or monitoring, this one might be worth your time. Would love your thoughts!

𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 - 𝘁𝗵𝗲𝘆’𝗿𝗲 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝘄𝗮𝘆 𝗔𝗜 𝗰𝗮𝗻 𝘀𝗰𝗮𝗹𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝘁𝗮𝘀𝗸𝘀. ⬇️ We’ve spent the last two years optimizing single-model performance. But in retrieval-heavy, orchestration, and autonomous workflows - the real leap forward comes from multi-agent architectures. Galileo just released a 165-page guide on building multi-agent systems — packed with some good real-world frameworks, potential trade-offs, and production insights. 𝗧𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲𝗮𝘀:⬇️ 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟭: 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 → Specialization & Validation – Divide tasks across focused agents and verify outputs through peer review. → Scalability & Resilience – Run workloads in parallel and maintain performance even when components fail. 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟮: 𝗪𝗵𝘆 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗙𝗮𝗶𝗹 → Coordination Overhead – Communication, context sharing, and write conflicts quickly erode performance gains. → Architecture Fragility – Fragmented memory and inter-agent dependencies make debugging and scaling expensive. 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟯: 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 → Core Patterns – Centralized, decentralized, hierarchical, and hybrid setups define how agents coordinate and share state. → Framework Selection – Practical guidance on when to use LangGraph, CrewAI, Mastra, or AWS Strands. 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟰: 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Foundation of Reliability – Context management determines how well agents reason and collaborate. → Common Failure Modes – Identifies context poisoning, distraction, confusion, and clash as key reliability risks. 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟱: 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 → Performance Measurement – Use metrics like Action Completion and Tool Quality to track agent effectiveness. → Observability & Feedback – Build custom monitoring and feedback loops to keep multi-agent systems improving over time. Multi-agent systems are powerful - but only when the coordination cost is lower than the value of specialization. Full guide below and here: https://lnkd.in/dh9xU29H 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 + 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲, 𝗮𝗻𝗱 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟱𝗸+ 𝗽𝗲𝗼𝗽𝗹𝗲: https://lnkd.in/dbf74Y9E

After OpenTelemetry, Datadog, and Dynatrace, we’re adding IBM Instana Observability to Causely’s supported telemetry sources. This is another step toward autonomous service reliability: our Causal Reasoning Engine turns the data you already collect into action: pinpointing causes, highlighting risks, and guiding remediation so responders move faster with confidence. https://lnkd.in/eFx_P-db

Migrating to Datadog isn’t just swapping tools, it’s rethinking how your teams do observability. Done right, it can unlock faster insight, tighter reliability, and better collaboration across IT and engineering. Our team at EverOps recently shared what to watch for before making the move, from managing massive log data to setting your teams up for success post-migration. If you’re planning (or even considering) a Datadog transition, give this a read. I’d love to hear how your org is approaching observability this year.

Why not just use S3 for ML artifacts? Because S3 is storage, not packaging. Here's what S3 gives you, Buckets. Keys. Files. Upload and download. That's it. You still need to manually track: - Which files belong together - Which versions match - What the dependencies are - Whether anything changed - How to promote across environments You end up building your own conventions: s3://models/fraud-detector/v2/model.pkl, s3://models/fraud-detector/v2/preprocessor.py, s3://models/fraud-detector/v2/config.json. S3 forces you to reinvent artifact management. You write scripts to track manifests. You maintain naming conventions. You hope nobody makes a mistake. You have no cryptographic guarantees that what you're deploying matches what you tested. OCI registries solve packaging, not just storage. Content-addressable storage means one digest cryptographically guarantees the complete artifact. Change anything, the digest changes. No silent drift. Immutable layers mean you can't accidentally overwrite v2 with different contents. Tags can move, digests can't. Security scanning, signing, and attestation are built into the ecosystem. Your existing container tooling already works. This is why KitOps uses OCI for ModelKits. It solves artifact packaging properly. The same infrastructure that handles your containers handles your ML artifacts. Same registries. Same promotion workflows. Same security tooling. One digest. One immutable package. One source of truth. S3 is great for storage. But if you're building artifact management on top of it, you're solving a problem OCI already solved. https://kitops.org

How does ORDR IQ function? Our VP of Product Management, Srinivas Loke, explains the architecture—from real-time device discovery to AI-driven policy creation, ensuring complete governance and auditability. Discover more from Srinivas Loke 👇 https://gag.gl/C38RDV

What Does It Take for AI Agents To Deploy Infrastructure? Discover how environment orchestration with blueprints and guardrails is making AI-assisted deployment a reality.

Epic News in AI Development Palantir and Lumen Technologies Join Forces to Accelerate AI-Driven Telecom Transformation Palantir’s Foundry and AIP support Lumen’s transformation by simplifying operations, accelerating modernization, and ensuring Lumen offers align with customer needs DENVER--(BUSINESS WIRE)-- Palantir Technologies Inc. (NASDAQ: PLTR), a leading provider of enterprise operating systems, and Lumen Technologies (NYSE: LUMN), the trusted network for AI, today announced a collaboration that brings Palantir’s Foundry and Artificial Intelligence Platform (AIP) to Lumen as it transforms its business. Lumen is transforming into a next-generation technology infrastructure company leveraging its physical fiber network, digitally powered platform, and connected ecosystem. To meet customers’ evolving multi-cloud, AI-ready needs, Lumen is collaborating with Palantir across its operations, finance, and technology functions to unlock new value. Palantir’s Foundry and AIP aim to streamline workflows, accelerate decision-making, and simplify complex legacy operations — from customer service and compliance reporting to the decommissioning of legacy telecom infrastructure and migration of products into modernized ecosystems. By surfacing actionable insights and enabling AI-assisted decision-making, the software is driving faster execution and improved operational efficiency, empowering Lumen to transform its network and services with speed. “As Lumen powers the backbone of the AI economy, we’re determined to make our own operations intelligent and efficient, just like the networks we deliver to our customers,” said Dave Ward, chief technology and product officer of Lumen Technologies. “Working with Palantir allows us to harness AI to accelerate our modernization efforts and deliver the network and services our customers need in the AI era.” “Lumen is redefining what’s possible in telecom by fusing AI into the very fabric of its operations,” said Ted Mabrey, Head of Palantir Commercial. “With Foundry and AIP, Lumen is accelerating its transformation into a technology infrastructure company. Their expansive network, digital platform, and connected ecosystem make them an ideal partner to showcase how AI can transform an industry at scale.” About Palantir Foundational software of tomorrow. Delivered today. Additional information is available at https://www.palantir.com. About Lumen Technologies Lumen is unleashing the world's digital potential. We ignite business growth by connecting data, and applications quickly, securely, and effortlessly. As the trusted network for AI, Lumen uses the scale of our network to help companies realize AI's full potential. From metro connectivity to long-haul data transport to our edge cloud, security, managed service, and digital platform we meet our customers’ needs today and as they build for tomorrow. Questions on how Lumen can help propel your AI strategy contact me. Mike Ehringer, mike.ehringer@lumen.com

How do you measure the technical maturity of your MLOps platform? Via a 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐌𝐚𝐭𝐮𝐫𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 across 4 pillars: •  𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐞𝐯𝐞𝐥 (manual → full CI/CD) •  𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 (single-model → multi-tenant) •  𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 (basic logs → predictive monitoring) •  𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 (ad-hoc → policy-driven compliance) This 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 allows organizations to assess their current standing and identify areas for improvement in their MLOps journey.