At Perform, its flagship annual user conference, Dynatrace showcased how customers are using Dynatrace AI Observability to scale AI applications safely, reliably, and cost-effectively.
Gartner® predicts that “40% of enterprise apps will be integrated with task-specific agents, up from less than 5% now” – making the shift from experimentation to production a business priority. Organizations need observability solutions that can not only monitor AI, but actively optimize, govern, and secure it at scale.
With the Dynatrace platform as a control plane for AI in production, enterprises gain the visibility, automation, and governance required to adopt agentic AI with confidence. This evolution is helping customers manage complexity and compliance while optimizing performance across emerging technologies.
Customers in Action
Canadian technology giant, TELUS, has been using Dynatrace AI Observability to transform incident response and drive measurable operational ROI. By consolidating multiple monitoring tools into a single observability platform, TELUS has lowered tooling costs and achieved a 30% reduction in onboarding time for new teams. Automation and monitoring as code capabilities reduced the effort to deploy end-to-end observability from 600 minutes to just 20 minutes, delivering substantial time savings for the business.
New Advancements
Despite AI enthusiasm, according to recent findings, the majority (95%) of AI initiatives deliver zero return on investment due to failures before reaching production. Dynatrace has introduced major advancements designed to close this gap, helping enterprises scale AI initiatives with confidence while mitigating security and compliance risks such as data leakage, prompt injection, and policy violations.
Recent innovations include:
- Unified observability across the agentic AI stack. Support for a broad ecosystem of agentic frameworks and services, including Amazon Bedrock AgentCore, Amazon Bedrock Strands, Google Agent Development Kit, OpenAI Agent, Anthropic Model Context Protocol (MCP), LangChain Agents and Azure AI Factory agents. This gives organizations a single, unified view across internal and external models, services, and orchestration layers.
- Model versioning & A/B testing. Built-in comparison across models such as GPT-5, Claude, Vertex AI, Azure AI Foundry using metrics including response time, token consumption, cost, and relevancy – enabling data-driven selection and continuous optimization.
- Intelligent alerting and forecasting. AI-driven cost and performance forecasting helps teams anticipate risk early and maintain predictable, efficient AI operations.
“By combining our Agentic AI initiatives with Dynatrace’s AI Observability capabilities, we’ve successfully optimized our development and operations workflows. This collaboration has enabled us to streamline incident resolution to minutes, from detection to pull requests. Through this integration of AI technologies, we’re driving innovation and delivering measurable business impact while reducing downtime.” states Kulvir Gahunia at TELUS. This partnership has delivered clear, measurable ROI for TELUS by accelerating innovation, reducing operational effort, and enabling us to proactively ensure the reliability and performance of our most important digital services.”
“Across industries, our customers are leading the shift from AI experimentation to AI at enterprise scale,” said Steve Tack, Chief Product Officer at Dynatrace. “Their work demonstrates how deep observability of modern AI workloads – using LLMs, agentic AI workflows, and generative AI applications – enables organizations to move faster and more confidently. By combining visibility with automation and intelligent analytics, our customers are turning AI into measurable business outcomes – faster innovation, improved reliability, higher customer satisfaction, and stronger operational efficiency.”
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 ...