Enterprise AI is rapidly evolving from standalone models to agentic AI systems, where multiple AI agents collaborate to gather information, reason across data sources, and generate complex outputs. These systems unlock powerful new capabilities, but they also introduce significant operational challenges. Organizations must be able to observe, govern, and optimize AI agents, models, and infrastructure in real time.

Dynatrace helps support this need by providing broad visibility across key layers of the AI stack—from agent orchestration and model inference to GPU infrastructure and enterprise applications. With Dynatrace, teams can monitor AI workflows, understand model behavior, optimize costs, and help improve reliability as agentic systems scale.

Why agentic AI needs full-stack observability

As organizations build GPU-accelerated platforms for AI training and inference, understanding system behavior becomes increasingly complex, with bottlenecks potentially occurring anywhere – from GPU utilization, model latency, token consumption, and downstream service dependencies.

Dynatrace connects these layers through full-stack AI observability, designed to help teams monitor model performance, trace multi-agent workflows, track GPU and infrastructure utilization, detect bottlenecks across AI pipelines, and potentially accelerate troubleshooting with AI-powered root cause analysis.

This unified visibility helps organizations run AI workloads with the same reliability, efficiency, and operational confidence expected from modern enterprise systems.

This unified visibility helps organizations operate AI workloads with improved visibility and operational confidence. By integrating with NVIDIA AI–Q Blueprint and the NVIDIA Agent Toolkit, Dynatrace enriches agent reasoning with high-quality operational telemetry while at the same time helping teams govern and identify opportunities to optimize costs.

How Dynatrace addresses Agentic AI

Dynatrace is designed to assist your team with monitoring infrastructure usage and model behavior and detecting pipeline bottlenecks and token consumption while improving reliability by accelerating troubleshooting and root cause analysis. It also provides a unified view of AI workflows from agent to model down to the infrastructure, allowing organizations to support responsible AI operations, manage cost, improve performance and support agentic workflows at scale.

Every agentic deployment is customized with different agents, tools, models, and data pipelines; therefore, observability is an important capability for understanding how these systems behave in production. The complexity arises as agents interact with multiple enterprise data sources, including:

• internal datasets
• external web and knowledge repositories
• proprietary research systems
• models served through NVIDIA NIM and Nemotron

Dynatrace can serve as operational data source for AI agents that may help improve the quality of generated insights and enable more informed decision-making. With flexible integration across customized AI-Q implementations, this architecture also lays out the groundwork for automated analysis, research, and decision making.

How Dynatrace integrates NVIDIA AI-Q

By combining NVIDIA’s AI-Q Blueprint with Dynatrace AI observability, organizations gain the transparency and operational intelligence needed to govern, optimize, and scale complex AI systems.

Dynatrace integrates into AI-Q environments in two ways.

1. Observability and cost intelligence for Agentic AI workflows

The NVIDIA Agent Toolkit generates lightweight OpenTelemetry traces that Dynatrace ingests to visualize agent workflows and model interactions.

Dynatrace automatically maps the underlying infrastructure supporting AIQ deployments including NVIDIA NIM and Nemotron microservices and enriches telemetry with AI-specific signals such as:

• token usage
• inference latency
• model metadata
• GPU utilization

This provides comprehensive visibility across key components including:

• AI models and inference workloads
• agent orchestration pipelines
• GPU and infrastructure resources
• enterprise data interactions

With these insights, teams can quickly detect performance bottlenecks across agent pipelines, monitor GPU utilization and overall infrastructure health, and identify inefficient model usage. This visibility can help organizations identify cost optimization opportunities associated with AI workloads. Together, these capabilities position observability as important components for building reliable and scalable AI systems.

2. Dynatrace as a high-quality data source for AI agents

Dynatrace can also serve as an operational intelligence source for AI agents.

Through Model Context Protocol (MCP) integrations, Dynatrace exposes telemetry that agents can use in their reasoning workflows, including:

• infrastructure performance metrics
• operational incidents and problems
• deployment and reliability trends
• system behavior and resource consumption

This allows AI agents to incorporate real-time operational insights into their decision-making. Instead of relying solely on external data, agents gain contextual awareness of enterprise systems, which may support more informed outputs Dynatrace ingests NVIDIA Agent Toolkit OpenTelemetry traces, model telemetry, and infra metrics exposing operational context via MCP.

Together, these technologies create a powerful foundation for deploying deep research in the enterprise as reflected in the picture below.

AI-Q use cases

The following are illustrative examples of what becomes possible when AI-Q-based research agents incorporate Dynatrace operational data and insights into their reasoning workflows. While NVIDIA AI-Q is a reference framework rather than a formal certified Dynatrace integration, these scenarios show how agentic research systems could use Dynatrace AI observability to generate richer analysis, identify patterns, and support more informed decisions.

Infrastructure migration analysis

AI agents combine Dynatrace operational telemetry such as performance trends, incidents, and deployment velocity with infrastructure and cloud cost data to evaluate platform migration scenarios (for example, OpenShift to AKS). The system produces data-driven recommendations with quantified tradeoffs to support strategic decisions.

Large-scale incident analysis

By analyzing thousands of historical problems, AI agents can identify recurring patterns, understand infrastructure behavior, and correlate technical issues with business KPIs. This enables deep operational insights and long-form analysis that would be difficult and time-consuming for humans to produce.

AI cost governance and optimization

Enterprises can use observability data from Dynatrace to analyze token consumption, model usage, and inefficient data interactions across AI workloads. Agents can identify patterns and suggest potential optimizations such as more efficient models or improved workflows.

Software delivery and reliability insights

DevOps and SRE teams can use agentic analysis to correlate deployments with incidents, assess build quality trends, forecast reliability risks, and identify engineering priorities—using Dynatrace as the trusted operational data source.

Get started today

• Try the Dynatrace Playground — Explore AI observability with sample data
• Explore NVIDIA integrations in the Dynatrace Hub
• Stream NAT telemetry to Dynatrace — NVIDIA Agent Toolkit OpenTeletemetry guide
• Use Dynatrace as an operational data source — Remote MCP Docs

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The NVIDIA Enterprise AI Factory addresses the rapidly evolving needs for AI infrastructure to support the rise of agentic AI. Since its launch, customers have leveraged this validated design to build agents by following structured methodology and recommended frameworks, which simplifies deployment and configuration while facilitating the implementation of AI factories in both on-premises and hybrid cloud environments.

Dynatrace has been an integral part of this initiative. Dynatrace full-stack AI and LLM observability helps organizations move forward with confidence in building their AI and agentic AI initiatives.

Observable AI: Turn a black box into a glass box to build confidence

With the publication of comprehensive guidelines, it’s simpler than ever for Dynatrace customers to set up and start monitoring their full-stack NVIDIA enterprise AI infrastructure, including its key tiers and components. Covering the infrastructure layer from GPUs to Kubernetes, NVIDIA NIM microservices, NVIDIA NeMo, and other technologies up to the application layer, Dynatrace observability enables customers to confidently run and operate complex AI workflows on NVIDIA infrastructure.

In parallel, Dynatrace has worked to significantly advance our AI and LLM observability offering by introducing the following:

• A dedicated app experience for AI observability with out‑of‑the‑box analytics, auto‑instrumentation, targeted metrics, a debugging flow, and ready‑made dashboards.
• Data governance and audit trails for AI services, turning governance into a competitive advantage.
• Model versioning and A/B testing to compare model performance across versions.
• Multi-modal agent explainability and tracing throughout RAG pipelines and agentic frameworks.

These improvements address challenges such as missing observability insights, scale, sovereignty, and trust. This empowers organizations to operationalize AI by building trust and monitoring guardrails; providing analytics capabilities to detect user-facing issues; helping SREs and AI-native engineers maintain performance, reliability, and security; and reducing cost across the agentic, AI, and LLM stack.

Privacy and security lead the way to scaling AI with confidence

AI is delivering significant productivity improvements, with 66% of senior executives reporting positive trends in productivity, according to PwC’s AI Agent Survey. This momentum is driving the demand to manage AI expenditures, enhance the decision-making quality of agents, and optimize development through visibility into AI components’ behavior in production environments — from pilot projects to full-scale operations.

However, sensitive data considerations and strict compliance requirements often impede progress, preventing organizations from fully realizing the benefits of AI adoption. As enterprises prioritize data privacy, regulatory compliance, and data sovereignty, there is an increasing need for high-performance NVIDIA AI infrastructure alongside frameworks designed to preserve control, trust, and autonomy in AI development.

In a recent blog on sovereign AI, NVIDIA shares strategies for nations and enterprises to develop AI factories that uphold local governance, security, and cultural values. Combining such factories with the Dynatrace advanced observability solution enables organizations to operationalize AI at scale — building secure and scalable agents, deployed on premises or in hybrid environments.

From privacy needs to public-sector requirements: NVIDIA AI Factory for Government

At NVIDIA GTC Washington, D.C. today, NVIDIA AI Factory for Government was announced, in support of the needs for regulated environments to drive AI initiatives. The U.S. Office of Management and Budget’s decision to establish scorecards for agencies’ AI maturity and management is in line with a 2024 Gartner Research forecast that more than 60% of government organizations will be prioritizing their investments in business automation by 2026 — up from 35% in 2022. The NVIDIA AI Factory for Government is a full-stack, end-to-end reference design that brings the power of reasoning AI to federal organizations. It helps organizations unlock productivity gains just like it does for enterprises, from service delivery to threat detection and day-to-day operations.

Built on the experience of deploying internal AI factories, the reference design offers guidance for deploying agentic AI, physical AI, and high-performance computing workloads on premises and in hybrid cloud environments, while meeting the compliance needs of federal and other secure organizations. The NVIDIA AI Factory for Government reference design includes NVIDIA Blackwell accelerated computing and NVIDIA networking, NVIDIA-Certified Systems, NVIDIA AI Enterprise software, NVIDIA Nemotron open models, and third-party software from AI leaders, all validated by NVIDIA.

Dynatrace delivers trusted observability and automation for regulated environments

Dynatrace has always been committed to supporting the public sector and other industries with regulatory requirements by providing customers with capabilities to control data flow through its lifecycle and manage sensitive data from ingestion to deletion, as well as global deployment options to meet data residency requirements, configurable retention times for different data types and use cases, unique encryption keys for customer’s stored data, and more.

Our dedication is reflected in customers’ success stories from regulated industries, as well as a growing list of global and local certifications, such as ISO 27001, SOC 2 Type II, CSA STAR 2, ENS, Tisax, and others. Find out more about our certifications and supported compliance frameworks in our Trust Center. For organizations also navigating evolving sovereignty requirements, our approach to digital sovereignty demonstrates how Dynatrace combines technical innovation with policy alignment to deliver trusted solutions globally.

Benefit from full-stack observability for end-to-end validated design

Dynatrace observability with the NVIDIA AI Factory for Government reference design enables organizations to accelerate the deployments of their AI agents and applications for federal and enterprise environments, and benefit from real-time, AI-powered insights.

These benefits range from improved scalability and performance to reduced complexity and total cost of ownership by simplifying processes, mitigating deployment risks to improved data security and compliance.

Visit the Dynatrace Playground to experience the possibilities of AI and LLM observability, and discover how Dynatrace is accelerating enterprise AI at scale.

Dynatrace and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

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NVIDIA Blackwell systems provide high-performance infrastructure for enterprise AI, and now, thanks to the Dynatrace integration with the NVIDIA Enterprise AI Factory reference design, enterprises can add Dynatrace Full-Stack Observability to NVIDIA Blackwell infrastructure. This magnifies the value of the NVIDIA Blackwell platform by providing real-time performance insights, anomaly detection, and dependency mapping.

Keep high performance and security top of mind with unified observability and security

Dynatrace aligns with high data security and privacy standards typical of on-premises NVIDIA Blackwell deployments, particularly in regulated industries such as finance and healthcare. Its unified data model, Smartscape® topology mapping, and Davis® AI engine provide deep visibility into the full stack—from GPU metrics and containerized workloads to distributed applications and user experiences, enabling tailored observability for workloads running on  NVIDIA Blackwell. Integrating NVIDIA Data Center GPU Manager or other telemetry sources is straightforward, allowing teams to monitor GPU health, utilization, thermal thresholds, and memory bandwidth alongside traditional infrastructure metrics.

Dynatrace technology allows for automated discovery and instrumentation of services running on NVIDIA Blackwell-accelerated systems. Whether monitoring high-throughput GPU compute tasks, Kubernetes clusters, or microservices, Dynatrace ensures low-overhead performance monitoring with minimal manual configuration.

AI-powered, real-time insights improve performance and explainability

With Dynatrace Full-Stack AI Observability, you can monitor real-time performance, trace prompts end-to-end, and ensure compliance, optimizing cost and throughput for your AI and LLM workflows and agents, offering various use cases such as

• Monitor service health and performance, tracking real-time metrics and offering clear visibility into service incidents.
• Validate service quality by measuring response speed or identifying performance hotspots.
• End-to-end tracing and debugging pinpoint the root cause of errors and failures in the LLM chain, troubleshoot issues in complex pipelines, and trace dependencies across the entire system spanning multiple LLMs, RAG pipelines, and agentic frameworks.

Unified AI-powered observability

Dynatrace delivers full stack observability for your LLMs and Generative AI applications running on NVIDIA Blackwell systems. Its ability to provide visibility into complex, high-performance environments allows enterprises to fully leverage Blackwell’s capabilities while maintaining operational excellence and system reliability, improving the performance, explainability, and compliance of your AI workloads and agents.

Visit the Dynatrace Playground to learn more and gain hands-on experience with prepopulated data, so you can experience the possibilities of AI and LLM observability with Dynatrace. If you’re interested in using Dynatrace for your own AI workloads, visit our documentation and start benefiting from full stack observability for AI and LLM.

Read more

• Part one of the Rise of Agentic AI blog series covers the fundamentals of AI agents, models, and emerging communication standards such as Agent2Agent (A2A) and MCP.
• Part two explores AI agent observability and monitoring, A2A and MCP communications, and how to scale and monitor Amazon Bedrock Agents.
• Part three explains how to monitor Amazon Bedrock Agents and how observability optimizes AI agents at scale.
• Part five demonstrates how to build a simple agentic application using the OpenAI Agents SDK and instrument the data with Dynatrace.
• Part six explores AI Model Versioning and A/B testing for smarter LLM services.
• Part seven introduces data governance and audit trails for AI services.

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