Operations | Monitoring | ITSM | DevOps | Cloud

Introducing AI Drive: Closing the AI Value Gap

The enterprise is standing at the edge of a seismic shift: an AI revolution. In the next five years, the way work gets done will be fundamentally reshaped as workflows once handled by humans are increasingly replaced or enhanced by artificial intelligence. But here’s the reality: success won’t come from simply handing out Copilot or GPT licenses and hoping employees figure it out.

LangChain Observability: Monitoring Guide for Production Apps

LangChain applications fail differently than traditional web apps. A single user request can trigger 15+ LLM calls, cost $5 in tokens, and fail silently without throwing errors. One team discovered a $12,000 OpenAI bill caused by a recursive chain with no monitoring. This guide shows how to implement observability for LangChain applications, giving you complete visibility into performance, costs, and errors before they impact your users or budget.

A Leader's Guide to Upskilling Teams for the AI Era

Every week, we hear about new AI breakthroughs. AI models write code, create videos, or analyze data in ways we couldn’t imagine just months ago. But there’s a gap: While most companies have adopted AI tools, the majority of employees still don’t use AI in their everyday work. As a manager, you see AI’s potential to change how your team works. Yet your employees struggle to figure out how AI fits into their daily tasks.

Agentic AI in Customer Support: Is It Ready to Resolve 80% of Issues Autonomously?

Daniel O’Sullivan, Senior Director Analyst at Gartner Customer Service & Support Practice, recently said in an article “Agentic AI has emerged as a game-changer for customer service, paving the way for autonomous and low-effort customer experiences.

Debugging issues with Sentry's MCP

Turns out, this MCP thing is pretty solid. We've built the MCP server to tap into all the different areas of context within Sentry and make it easy to bring these into your editor client to help debug your application. Want to know the most fixable issues in your environment? Easy. Want to see your query performance for your backend? Just ask it.

AI Reliability Insights: How to Build a Gremlin MCP Server

Gremlin’s Reliability Intelligence helps teams uncover the cause behind failure modes so they can move faster and improve reliability without sacrificing velocity. The new Gremlin MCP Server, part of Reliability Intelligence, gives you new ways to explore your data, giving you access to insights and recommendations to improve reliability and better run your systems using Gremlin. In this webinar, Gremlin CTO Sam Rossoff shows you how to integrate your favorite LLM and use plain language to query data, uncover insights, create dynamic dashboards, and more.

Introducing Honeycomb Intelligence Canvas

Canvas is an AI-guided workspace inside Honeycomb that combines an AI assistant with an interactive notebook for visualizing query results and traces. You can ask a natural language question about your data and Canvas will immediately start exploring your traces, through multiple queries and other tools, to find the right next steps. Instead of having to write each query yourself, Canvas automatically proposes relational queries, comparisons, and visualizations that explain why an SLO fired or what changed after a deploy.