New Relic this week unfurled a revamped observability platform based on an artificial intelligence engine that enables anyone from application developers to business analysts to employ natural language to surface insights.

In addition, the company revealed that the New Relic Intelligent Observability Platform is now integrated with the GitHub Copilot tool that uses generative AI to write code.

At the same time, New Relic is making available Pathpoint Plus, which connects IT insights surfaced by AI models with business key performance indicators (KPIs). Organizations can also take advantage of low-code tools to map end-user journeys and, if necessary, playback specific sessions.

New Relic CEO Ashan Wily said the New Relic Intelligent Observability Platform surfacing a set of opinionated insights that move organizations one step closer to being able to democratize observability. The goal is to make it simpler to identify issues without having to wait for someone with enough expertise to be able to frame a specific query.

New Relic has been steadily embedding machine learning algorithms and generative AI capabilities into its core observability platform for several years. The New Relic Intelligent Observability Platform takes those efforts to the next level by combining compound AI and agentic AI to enable multiple models, agents and tools to be combined in a way that automates a broader range of tasks.

For example, two AI agents from New Relic and GitHub can be collaboratively assigned a task to detect issues arising from code changes that could then be addressed directly from within an integrated development environment (IDE).

While New Relic is providing orchestration capabilities that can be applied to multiple agents, it’s not clear to what degree organizations might be able to standardize on a single management framework for AI agents. Instead, most organizations will have access to an ecosystem of AI agents that will be managed in a federated manner depending on the use case and orchestration framework employed, noted Wily.

Hopefully, advances in AI will reduce the time IT teams spend troubleshooting IT environments. A New Relic survey finds on average teams are grappling with 280 hours of median annual downtime a year, which at 12 hours of a 40-hour work week equates to about 30% of their time.

On the plus side, organizations are investing more in observability. A TechStrong survey finds 63% working for organizations that will be making additional investments in observability over the next two years, with 21% describing those investments as significant. The survey finds that 48% of respondents work for organizations that already practice observability regularly.

Application environments are only going to become more distributed, creating dependencies that are becoming too complex for IT teams to manage without the aid of some type of observability platform infused with AI capabilities. While there is no silver AI bullet for achieving and maintaining observability there will soon come a day when software engineers are not going to want to work for organizations that don’t provide them access to AI tools. After all, the odds of being successful, coupled with the likely level of stress and toil to be encountered, is simply too high.