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speedscale

What AI Has Never Seen: The Context Gap in Code Generation

Feb 4, 2026 By Ken Ahrens In Speedscale
Your AI coding assistant has read the entire internet. It knows every programming language, every framework, every best practice documented in Stack Overflow answers and GitHub repositories. It can generate a REST API handler in seconds that looks perfect with clean code, proper error handling, following all the patterns. But here’s what it’s never seen: your production traffic. Data from a real API request. Someone filling out a form with messed up or incomplete data.
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grafana

Observing agentic AI workflows with Grafana Cloud, OpenTelemetry, and the OpenAI Agents SDK

Feb 4, 2026 By Adam Quan In Grafana
As agentic AI applications are used more broadly in production, they introduce new operational models, combining multi-step reasoning, tool execution, and autonomous decision-making into a single workflow. SRE teams need visibility into how these agents behave, where they fail, and how they perform over time.
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The Dangerous Power of Local AI Agents. #speedscale #proxymock #aiagents #openclaw #localai

Feb 4, 2026 By Speedscale In Speedscale
I’ve been testing OpenClaw, a fully autonomous agent that lets you remote control your entire system via Signal. It’s incredibly powerful to text your computer from a coffee shop and have it execute tasks, but you’re essentially handing the keys to your digital kingdom to an LLM. The Golden Rule: Trust, but verify. I’m using Proxymock to sniff every single API call going in and out of the agent. If there’s a data leak or a "hallucination" that tries to wipe my drive, I see it first.
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harness

Qwiet AI Is Now Harness SAST and SCA | Harness Blog

Feb 4, 2026 By Renny Shen In Harness
Modern application security is struggling to keep up with AI-driven development and cloud-native scale, especially when security feels bolted onto CI/CD instead of built in. Harness SAST and SCA bring AI-powered application security testing natively into the Harness platform, reducing noise and alert fatigue. By identifying only vulnerabilities that are actually reachable in production code, teams get findings they can trust and act on faster.
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mezmo

The Grok-to-AI Evolution: Why Modern SREs Are Moving Beyond Manual Parsing

Feb 4, 2026 By Mezmo In Mezmo
Grok structures logs. Context engineering connects systems. AI explains behavior. For years, Grok patterns have been the workhorse of the SRE world. Built on regular expressions, Grok helps teams extract structure from unstructured logs. As we explored in "Do You Grok It?", Grok is the key to turning messy log lines into usable fields. It's why our Grok Pattern Reference remains one of our most-visited resources — SREs are hungry for structure.
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upsun

Scalable AI governance: why your policy needs a platform, not just a PDF

Feb 4, 2026 By Upsun In Upsun
Most IT teams don’t lack AI policies. They lack policies that survive a Git push. In many organizations, AI governance is a paper tiger. There are comprehensive documents outlining data usage, approved models, and risk management. On an auditor's desk, these policies look complete. But inside the workflow, the reality is different. AI tools are being embedded directly into IDEs, CI pipelines, and internal automation scripts.
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upsun

What mid-market IT teams wish they knew before deploying AI agents

Feb 4, 2026 By Upsun In Upsun
AI agents are quickly shifting from experimentation into day-to-day operations. That shift is showing up in the data. McKinsey’s latest State of AI research highlights both broader AI use and the growing focus on “agentic AI,” even as many organizations still struggle to scale safely. For mid-market IT teams, agents can feel like the unlock: automate repetitive workflows, reduce backlog pressure, and deliver more output without expanding headcount.
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logicmonitor

AI Agent Governance: How to Keep Agentic ITOps Workflows Safe

Feb 4, 2026 By Margo Poda In LogicMonitor
The future of ITOps automation is better control over what AI agents can see, share, and do. AI automation in ITOps is expected to resolve incidents, reduce operational load, and operate with limited human involvement. Those outcomes depend on systems that can take action, not just surface insight. Agentic AI enables that shift. AI agents can correlate signals across tools, update tickets, trigger remediation, and coordinate workflows without waiting for instruction.
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Komodor

Building Trust in the Machine: A Guide to Architecting Agentic AI for SRE

Feb 4, 2026 By Itiel Shwartz In Komodor
The promise of Artificial Intelligence in Site Reliability Engineering (SRE) is seductive: an autonomous system that never sleeps, instantly detects anomalies, and fixes broken infrastructure while humans focus on high-value work. However, the gap between a demo-ready chatbot and a production-grade Autonomous AI SRE is vast. In complex, noisy environments like Kubernetes, a “naive” implementation of Large Language Models (LLMs) is not just ineffective, it can be dangerous.
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