Operations | Monitoring | ITSM | DevOps | Cloud

AWS re:Invent 2025 AI-First Incident Management in Slack

Jan 6, 2026 By PagerDuty Inc. In PagerDuty
Jacky Leybman from PagerDuty and Kaninie Knight from Slack share how their integration streamlines incident response and real-time collaboration. This session highlights practical workflows and measurable gains – such as faster triage and lower MTTR – achieved by connecting on-call operations directly in Slack.
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Ep 24: Governing AI in the age of agentic systems and Model Context Protocol

Jan 6, 2026 By Sumo Logic, Inc. In Sumo Logic
On this episode of Masters of Data, we unpack David's new white paper on AI governance for agentic systems. He explains model context protocol (MCP) as "APIs for agents", how AI systems talk and execute tasks. The catch? Autonomous agents are insider threats that move fast and cause serious damage. David introduces the Model Control Plane (MoCop), a twelve-pillar framework designed to prevent your AI from going rogue. We cover his roadmap for security leaders to build real controls and telemetry. His advice: treat agents like interns with root access. Get ahead of this before your agents do.
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Automating BGP Troubleshooting with Kentik AI Advisor

Jan 6, 2026 By Kentik In Kentik
In this demo, we use Kentik AI Advisor to troubleshoot a real-world BGP misconfiguration that brings down a peering session with a transit provider. You’ll see how AI Advisor works both as a dedicated page and as an in-portal overlay, using natural language to identify the affected interface, correlate SNMP and syslog data, and pinpoint a maximum-prefix issue as the root cause. Then we accelerate and standardize the workflow with custom network context and AI-powered runbooks, so every engineer can troubleshoot BGP alerts like an expert.
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bigpanda

How agentic IT operations transform IT Service Management (ITSM)

Jan 6, 2026 By Sam Osborn In BigPanda
Enterprise ITOps leaders are realizing that legacy incident management processes are collapsing under the weight of today’s sprawling, hybrid-cloud enterprise environments. The fastest path from reactive firefighting to proactive, automated control is an agentic AI-powered incident assistant that can understand context, coordinate people, and take intelligent action at machine speed. Enterprise IT doesn’t look anything like it did even five years ago.
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Leveraging AI Crypto Trading Platforms for Smarter Investment Strategies

Leveraging AI Crypto Trading Platforms for Smarter Investment Strategies

Jan 6, 2026 By OpsMatters In OpsMatters
The world of cryptocurrency has experienced explosive growth over the past decade, transforming from a niche digital asset market into a global financial phenomenon. With this rapid expansion comes a new set of challenges for investors, including high market volatility, an overwhelming number of trading options, and the constant demand for real-time data analysis. Traditional trading strategies often struggle to keep up, leading to missed opportunities and heightened risks. To address these challenges, investors are increasingly turning to technology-driven solutions, most notably, AI crypto trading platforms.
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ivanti

How Agentic AI for ITOps Unlocks Value at Scale

Jan 5, 2026 By Sterling Parker In Ivanti
Here’s a paradox for the AI era: organizations are obsessed with the promise of AI as the key to unlocking productivity and enterprise transformation, and IT teams are all-in on the advantages AI and automation offer — yet those same organizations are the ones holding that transformation back. While the majority of IT workers advocate for AI adoption, operational, cultural and budgetary barriers stand in the way of enterprises implementing AI at scale.
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gitkraken

The Context Engineering Framework: 3 Shifts for AI-Powered Dev Teams

Jan 5, 2026 By Jade Nangah In GitKraken
You’ve probably used AI earlier today. Maybe you asked it to debug a function, generate a test case, or explain a legacy codebase you just inherited. But here’s the thing: you didn’t just type a question and get an answer. You explained your problem, shared background context, pasted code snippets, clarified what you meant, then refined the output until it was actually useful. In other words, you were context engineering.
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