Operations | Monitoring | ITSM | DevOps | Cloud

When every employee is building AI agents, seeing what they did afterward isn't enough. AgentPatrol's in-flow governance runs inside the agent's execution flow—pausing for human approval, enforcing policies by value, masking PII, and stopping runaway agents before costs spiral.

Your LLM application emits telemetry unlike anything else in your stack. Model calls, tool invocations, retrieval steps, and token usage arrive as spans whose attributes carry entire prompts and completions. That data is bulky, it's full of user content you may not be allowed to export, and depending on which instrumentation each service uses, the same fact can arrive under different attribute names.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how PagerDuty’s Runbook Automation / Rundeck MCP Server recently announced in GA builds towards this vision.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how PagerDuty’s AI-Powered Incident Communications builds towards this vision.

Today we're launching Foresight AI, Gremlin’s agentic resilience product that analyzes and tests your systems for potential failures, fixes them, and verifies reliability at the speed of AI. I've spent most of my career on call. At Amazon and Netflix, I served as a Call Leader, the person running the bridge when something big broke. Those years taught me the same lesson we founded Gremlin on: the best incident is the one that never happens.

Application development and operations teams are adopting AI coding tools at an exponentially increasing rate, from a rounding error of 6% of code output by AI in 2023, to as much as 51%-75% for a majority of enterprises. Agents are making pull requests (PRs) faster than human developers could have ever dreamed. As features are pushed to market faster, there’s a sharp increase in production incidents, with 80% of development shops specifically tracing production outages to AI.

Dedicated vector databases have become such a standard part of the RAG conversation that teams often add one before they have proved they need it. According to studies, over 70% of companies using LLMs are using vector databases and RAG to customize their models. That shows how quickly the pattern has become normal. However, it does not mean every RAG application needs a separate retrieval system. If your application already runs on PostgreSQL, pgvector may be enough.