Resolve's Zero Ticket Minute - Ep. 6 #itautomation #aiautomation #agenticai
More AI agents aren’t the answer. Orchestration is. When agents work together, IT problems get resolved end-to-end. Less noise. More execution.
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Azure Cost Control for CSPs: Find Spend Leakage, Eliminate Waste & Scale FinOps
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VirtualMetric DataStream + Google SecOps Integration: Pre-Ingest UDM Normalization at Scale
Google SecOps (formerly Chronicle) is widely used for large-scale security analytics, long-term telemetry retention, and detection across diverse environments. Its Unified Data Model (UDM) enables correlation across sources and supports analytics that operate over long time horizons. To take full advantage of these capabilities, security data must arrive in a consistent and well-structured UDM format. In practice, this is rarely the case.
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Upsun's AI story: the 5% path from pilots to production value at scale
Here’s the uncomfortable truth: most companies do not have an AI problem. They have a delivery problem wearing an AI costume. MIT’s Project NANDA research has been widely cited for a brutal headline statistic: roughly 95% of corporate generative AI pilots fail to produce measurable business impact or returns, while only about 5% break through to meaningful outcomes. (Yahoo Finance) The models are impressive. The demos are dazzling. The budgets are real.
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Blue-Green Deployments for GCP Managed Instance Groups with Harness
Learn how to set up and execute zero-downtime blue-green deployments for Google Cloud Managed Instance Groups (MIG) using Harness Continuous Delivery.
(Tech Talk) Shipping with Context Knowledge Graphs as the Backbone of AI-First Software Delivery
Knowledge graphs are essential to solving the context bottleneck in AI-First software delivery, which occurs because workflows, policies, and dependencies are siloed and invisible to AI agents. In this Tech Talk, Prateek Mittal ((Product Director of AI Core and Data Platform at Harness)) discusses the key concepts: Knowledge Graphs vs. Observability: Observability tells you "what is happening," while knowledge graphs tell you "what does that mean" by modeling structured relationships. They work together to link live signals to affected services or SLAs.