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New Relic AI Available as Plugin for Amazon Q Business

New Relic announced it is connecting New Relic AI, its in-platform generative AI assistant, into Amazon Q Business, AWS’ enterprise generative AI assistant for streamlining complex workflows.

By connecting New Relic’s Intelligent Observability Platform with Amazon Q Business, New Relic goes beyond a simple API data pull to provide deep, intelligent insights and recommendations. The collaboration integrates Amazon Q Business’ built-in retrieval augmented generation (RAG) capabilities with New Relic’s real-time monitoring and intelligence to help enterprises quickly detect and resolve operational issues—such as application performance slowdowns, server malfunctions, and network bottlenecks. Now, anyone across an enterprise organization can detect, understand, and address issues, and receive intelligent recommendations to inform escalation decisions—all within Amazon Q Business. This enables more intelligent, faster, and cost effective business operations for enterprises.

“Enterprises are focused on unlocking the value of their data, leveraging the potential of AI, and driving business growth,” said New Relic CEO Ashan Willy. “New Relic is built to support agentic AI systems. We are the only intelligent observability platform to enable AI agent to AI agent integrations, or agentic orchestration. This allows us to go beyond simply bringing your observability data into other AI applications—we unlock the full power of our AI-strengthened platform with intelligent recommendations so any user can automate observability workflows to drive faster operations.”

Business interruptions can severely impact revenue and customer experience, especially during high-traffic periods. With data scattered across tools and knowledge sources, switching tools can lead to missed service level agreements (SLAs), confusion, and slow incident mitigation, which can prolong incidents and increase operational costs. By analyzing complex data and centralizing critical observability insights and actions into one interface, the two powerful AI systems break down team silos, streamline workflows, accelerate time to resolution, and automate incident response.

“Switching tools and context is one of the most painful problems enterprises face in modern incident response. Data and knowledge can end up in silos, making it hard to understand what your tools are telling you and when to escalate problems,” said New Relic Chief Product Officer Manav Khurana. “Together, New Relic and AWS are helping enterprises improve their business workflows and outcomes with AI. Bringing observability directly into the business application workflow is a game changer for gaining fast insights and intelligent recommendations on complex data so you can troubleshoot in real-time.”

New Relic AI and Amazon Q Business now work together to enhance enterprise productivity. New Relic brings real-time production data like errors, logs, traces, security vulnerabilities, and alerts directly into Amazon Q Business experiences and workflows. All information and insights are presented directly within the Amazon Q Business interface, eliminating switching tools between New Relic and Amazon Q Business. By providing a unified interface for querying and resolving issues, the solution empowers the entire team to maintain and improve digital services, regardless of their technical expertise.

Key features include:

- Natural language unlocks access for any user: Leverage natural language to access information, generate summaries, and complete tasks securely based on information in enterprise systems—lowering the barrier to entry for resolving issues and speeding up resolution.

- Real-time service analysis and intelligent recommendations: Query specific services, hosts, and system components for performance insights drawn from current performance data and compare it against historical trends and best practices—providing detailed insights and actionable recommendations based on the latest production environment information.

- Alert intelligence reporting: Package sophisticated insights and intelligent recommendations based on deep analysis into the health of an application–preventing or reducing negative business impacts from incidents.

- View information and insights directly within Amazon Q Business: Eliminates the need to switch between the New Relic and Amazon Q Business interfaces, enabling faster problem resolution.

- Agentic orchestration: Amazon Q Business and New Relic AI coordinate to automate research and incident response tasks. This helps eliminate human toil, prevent human error, and enforce best practices.

This announcement builds upon New Relic’s deep relationship with AWS and adds to its more than 105 existing AWS integrations and integration between New Relic AI monitoring and Amazon Bedrock.

New Relic AI is available as a plugin for Amazon Q Business.

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New Relic AI Available as Plugin for Amazon Q Business

New Relic announced it is connecting New Relic AI, its in-platform generative AI assistant, into Amazon Q Business, AWS’ enterprise generative AI assistant for streamlining complex workflows.

By connecting New Relic’s Intelligent Observability Platform with Amazon Q Business, New Relic goes beyond a simple API data pull to provide deep, intelligent insights and recommendations. The collaboration integrates Amazon Q Business’ built-in retrieval augmented generation (RAG) capabilities with New Relic’s real-time monitoring and intelligence to help enterprises quickly detect and resolve operational issues—such as application performance slowdowns, server malfunctions, and network bottlenecks. Now, anyone across an enterprise organization can detect, understand, and address issues, and receive intelligent recommendations to inform escalation decisions—all within Amazon Q Business. This enables more intelligent, faster, and cost effective business operations for enterprises.

“Enterprises are focused on unlocking the value of their data, leveraging the potential of AI, and driving business growth,” said New Relic CEO Ashan Willy. “New Relic is built to support agentic AI systems. We are the only intelligent observability platform to enable AI agent to AI agent integrations, or agentic orchestration. This allows us to go beyond simply bringing your observability data into other AI applications—we unlock the full power of our AI-strengthened platform with intelligent recommendations so any user can automate observability workflows to drive faster operations.”

Business interruptions can severely impact revenue and customer experience, especially during high-traffic periods. With data scattered across tools and knowledge sources, switching tools can lead to missed service level agreements (SLAs), confusion, and slow incident mitigation, which can prolong incidents and increase operational costs. By analyzing complex data and centralizing critical observability insights and actions into one interface, the two powerful AI systems break down team silos, streamline workflows, accelerate time to resolution, and automate incident response.

“Switching tools and context is one of the most painful problems enterprises face in modern incident response. Data and knowledge can end up in silos, making it hard to understand what your tools are telling you and when to escalate problems,” said New Relic Chief Product Officer Manav Khurana. “Together, New Relic and AWS are helping enterprises improve their business workflows and outcomes with AI. Bringing observability directly into the business application workflow is a game changer for gaining fast insights and intelligent recommendations on complex data so you can troubleshoot in real-time.”

New Relic AI and Amazon Q Business now work together to enhance enterprise productivity. New Relic brings real-time production data like errors, logs, traces, security vulnerabilities, and alerts directly into Amazon Q Business experiences and workflows. All information and insights are presented directly within the Amazon Q Business interface, eliminating switching tools between New Relic and Amazon Q Business. By providing a unified interface for querying and resolving issues, the solution empowers the entire team to maintain and improve digital services, regardless of their technical expertise.

Key features include:

- Natural language unlocks access for any user: Leverage natural language to access information, generate summaries, and complete tasks securely based on information in enterprise systems—lowering the barrier to entry for resolving issues and speeding up resolution.

- Real-time service analysis and intelligent recommendations: Query specific services, hosts, and system components for performance insights drawn from current performance data and compare it against historical trends and best practices—providing detailed insights and actionable recommendations based on the latest production environment information.

- Alert intelligence reporting: Package sophisticated insights and intelligent recommendations based on deep analysis into the health of an application–preventing or reducing negative business impacts from incidents.

- View information and insights directly within Amazon Q Business: Eliminates the need to switch between the New Relic and Amazon Q Business interfaces, enabling faster problem resolution.

- Agentic orchestration: Amazon Q Business and New Relic AI coordinate to automate research and incident response tasks. This helps eliminate human toil, prevent human error, and enforce best practices.

This announcement builds upon New Relic’s deep relationship with AWS and adds to its more than 105 existing AWS integrations and integration between New Relic AI monitoring and Amazon Bedrock.

New Relic AI is available as a plugin for Amazon Q Business.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...