Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants like Claude Code are becoming core parts of modern development workflows. But as with any powerful tool, the question quickly arises: how do we measure and monitor its usage? Without proper visibility, it’s hard to understand adoption, performance, and the real value Claude brings to engineering teams. For leaders and platform engineers, that lack of observability can mean flying blind when it comes to understanding ROI, productivity gains, or system reliability.

Since launching our AI investigations and insights in a research preview, one thing has become clear: automated root cause analysis delivers a significant return on investment. Teams have confirmed that instant insights don’t just save a few minutes; they fundamentally shorten incident response cycles, free up valuable engineering hours, and reduce the business impact of downtime.

Vector databases allow you to store, manage, and efficiently query high-dimensional vector data, which are numerical representations of data like text, images, or audio. Pinecone is a fully managed vector database optimized for fast, scalable similarity search—to power a Retrieval-Augmented Generation (RAG) system. This allows you to enhance language model responses by grounding them in relevant context retrieved from your own documents.

The rise of hybrid cloud, CI/CD, agile methodologies, and microservices has dramatically accelerated innovation, but it has also brought corresponding increases in complexity, fragmentation, and chaos. Enterprise IT departments are struggling to keep up. To stay ahead of these complex environments, enterprises have dramatically increased their spending on observability and IT Service Management (ITSM) tools. However, despite a 20% year-over-year increase in spending, incident detection remains poor.

The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed. In the early days, public cloud services were the obvious choice.

Agentic AI has sparked a ton of excitement in IT. These intelligent agents can analyze signals, interpret requests, and recommend actions with surprising accuracy. But left on their own, they struggle to translate those insights into reliable execution. The end result is a fragmented picture of great thinking... but limited doing. This is why orchestration matters.

You don’t invest in automation just to shave seconds off a workflow. You do it to reclaim time, reduce overhead, and eliminate waste that eats into margins. For a while, scripted chatbots and tier-1 deflection provided some degree of automation. They could close low-priority tickets or route FAQs, but rarely solved what mattered: zero-touch resolution and outcomes that don’t need follow-ups. Most enterprise automation is surface-level.