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harness

Architecting MCP for AI Agents: Lessons from Our Redesign | Harness Blog

Mar 19, 2026 By Sunil Gattupalle In Harness
-- Key Takeaways: The Harness MCP server is an MCP-compatible interface that lets AI agents discover, query, and act on Harness resources across CI/CD, GitOps, Feature Flags, Cloud Cost Management, Security Testing, Resilience Testing, Internal Developer Portal, and more. -- The first wave of MCP servers followed a natural pattern: take every API endpoint, wrap it in a tool definition, and expose it to the LLM.
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lightrun

Claude Code + Lightrun MCP: Your AI Agent Now Has Live Runtime Vision

Mar 19, 2026 By Lightrun Team In Lightrun
Claude Code, Anthropic’s coding agent, now integrates with Lightrun through MCP. AI code assistants have been flying blind. Google Dora’ 2025 report found it is causing, an almost 10% increase in code instability. Even with up to 1M tokens of context available in Claude, this powerful agenti cannot see how the code it writes actually behaves inside a live system under real traffic, real dependencies, and under a load of 10,000 requests per second.
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Komodor

Komodor Introduces Extensible, Autonomous Multi-Agent Architecture for AI-Driven Site Reliability Engineering

Mar 18, 2026 By Komodor In Komodor
Out-of-the-box and bring-your-own AI agents that encode operational knowledge boost troubleshooting speed and accuracy across cloud native infrastructure TEL AVIV and SAN FRANCISCO, March 18, 2026 — Komodor, the autonomous AI SRE company for cloud-native infrastructure, today announced a new extensibility framework that transforms its Klaudia AI technology into a universal multi-agent platform for troubleshooting and optimizing performance of complex cloud native infrastructures and applications.
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cloudzero

How A Finance Director Found $30K/Month In AI Savings In 10 Minutes

Mar 18, 2026 By Emily Allen In CloudZero
A real workflow showing how Claude + CloudZero MCP turns plain-English questions into actionable cost intelligence — no dashboards, no tickets, no waiting As Director of Finance and Accounting at a software company, my job can be described simply: Understand what we’re spending, who’s responsible, and whether we can get more efficient. But as anyone who’s had to wrangle AI costs knows, doing so for AI is anything but simple.
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Engineers Want AI in Observability - With One Catch: 4th Annual Observability Survey by Grafana Labs

Mar 18, 2026 By Grafana In Grafana
Actually useful AI is welcome in observability. AI for the sake of AI is not. In this overview of Grafana Labs’ 4th annual Observability Survey, Marc Chipouras shares what 1,300+ respondents from 76 countries told us about the current state of observability — and what comes next. This year’s survey explores four major themes: The results show strong interest in AI for forecasting, root cause analysis, onboarding, and generating dashboards, alerts, and queries. But when it comes to autonomous action, practitioners are more cautious — and 95% say AI needs to show its work to earn trust.
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Flow State in an AI Workplace - Digital Friction 1:1 with Mike Lovewell

Mar 18, 2026 By Nexthink In Nexthink
Tom welcomes Mike Lovewell to explore how digital friction continues to shape the modern workplace. From early days of low awareness to today’s complex, AI-influenced environments, Mike shares how friction has evolved in scale rather than cause. They discuss the growing importance of flow state, the measurable business impact of small disruptions, and why adoption—not just technology—is the key to success. AI emerges as both a solution and a new source of friction, depending on trust and usability.
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bigpanda

How agentic AI for ITOps overcomes observability tool gaps

Mar 18, 2026 By Conor Castronovo In BigPanda
As enterprise ITOps teams monitor increasingly complex, cloud-based, containerized systems, traditional observability practices are struggling to keep up. As IT infrastructure complexity increases, the typical response is to layer on more monitoring, logging, and instrumentation.
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statuscake

Buy vs Build in the Age of AI (Part 3)

Mar 18, 2026 By James Barnes In StatusCake
In Part 1, we looked at how AI has reduced the cost of building monitoring tools. Then in Part 2, we explored the operational and economic burden of owning them. Now we need to talk about something deeper. Because the real shift isn’t just economic; it’s structural. AI isn’t just helping engineers write code faster. It’s accelerating the entire software ecosystem; including how monitoring tools are built, maintained, and trusted.
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