Operations | Monitoring | ITSM | DevOps | Cloud

In recent months, my conversations with fellow technology leaders have consistently revolved around two key themes: how we leverage AI to drive innovation and efficiency, and how we mitigate the inherent risks associated with AI. However, I’ve noticed a concerning gap – while enterprises are busy strategizing the adoption of AI to enhance productivity, reduce costs, and outpace competitors, very few are addressing how AI is being actively used today by their own teams.

Generative AI has moved beyond early-stage experiments into an emerging driver of enterprise value. By automating complex tasks, personalizing customer interactions at scale, and accelerating innovation cycles, organizations adopting Generative AI (GenAI) see measurable performance improvements. For businesses, the challenge now lies not merely in adoption but in precise alignment of AI capabilities to strategic business goals, driving revenue, optimizing costs, and mitigating risks effectively.

Today, we’re announcing the general availability of Lumigo Copilot, the most intelligent AI-powered observability assistant on the market, built for the complexities of modern microservices. Copilot emerged from a simple realization: Distributed systems produce too much fragmented data across too many layers, making troubleshooting slow, reactive, and deeply manual. Copilot changes that.

We are exploring how we can help on-call engineers troubleshoot incidents more effectively by providing the OpenAI Codex agent with access to real-time observability data in terminals. We've developed an integration and new tool visualizations that connect OpenAI's Codex CLI to the new Datadog MCP server. In this post, we'll share what we've been experimenting with: enabling an AI agent to retrieve production metrics, logs, and incidents from Datadog in real time and act on that context.

There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” But these aren’t checkpoints on a single journey to automation. DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.