Operations | Monitoring | ITSM | DevOps | Cloud

Ticket Deflection Starts With Your Knowledge Base, Not Your Chatbot

Your service desk closed 400 tickets last month. Somewhere between a third and half of them were password resets, VPN questions, licence requests, and "how do I get access to the shared drive." Every one of those had a documented answer. Most of those answers were sitting in a knowledge base that the person raising the ticket either could not find or did not trust.

Hyperping MCP: Run Incidents, Status Pages and Maintenance

Summarize with ChatGPT Claude The Hyperping MCP server now has 49 tools: 28 that read and 21 that write. An agent connected from Claude Code, Cursor, Codex or another MCP client could already manage monitors, publish a status page incident and schedule maintenance. It can now do most of the rest: declare an incident and page on-call, acknowledge and escalate it, correct what was posted on the status page, create and configure status pages, and reschedule, end or cancel maintenance.

From Meeting Rooms to the Contact Center Floor: How Krisp Assists Agents on Every Live Call

Contact centers are where companies do their talking at scale. Thousands of agents, dozens of live calls each per shift, and on the other end a stranger with a problem and a finite amount of patience. A ten-second lookup, repeated across a few million calls a year, is a budget line.

Autonomous IT operations: Scaling business without scaling IT complexity

Autonomous IT operations use AI, operational data, observability, and automation to enable IT environments to detect issues, understand their context, determine the appropriate response, and act with minimal human intervention. As businesses grow, IT environments rarely stay simple. More employees, endpoints, applications, and cloud services generate even more alerts, incidents, and operational work. The traditional model scales linearly: more environment means more manual effort.

Database governance in the AI era: Framework, risks and best practices

AI database governance can get overlooked when teams rush to connect AI tools to production data. IBM’s 2025 research found that 97% of organizations that reported a breach involving an AI model or application lacked proper AI access controls. The risk is easy to see. Give an AI agent too much access and it can expose or alter data in seconds. Feed it poor-quality data and it may produce a confident but incorrect answer.

GitKraken Insights | AI feels faster. Make sure it is.

AI feels faster. Make sure it is. GitKraken Insights shows engineering leaders the real cost, output, and ROI of their AI investment, per tool, per team, per developer. Then it gives every developer their own data and coaching to get more from it. 84% of developers say they feel more productive with AI. Only about one in five can actually measure the impact.* With GitKraken Insights, you can: We run on it ourselves: GitKraken's own engineering org reached 2.53x output in six months.

Cribl On Your Coffee Break Episode 20 - Keep learning, keep growing, keep Cribl-ing!

As we wrap up our month-long series, we look at the resources that will help you keep learning and growing - from Cribl University to Sandboxes to the Cribl Community and beyond. By the time the month is over, you will have a pretty good idea of what Cribl can do, and how to do it. You’ll also have consumed more caffeinated beverages than is strictly appropriate...