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How Engineers Can Use AIOps to Innovate Their Infrastructure

Paul Constantinides
Salesforce

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI.

The need for a new approach to IT operations is critical, one that moves beyond manual monitoring and static thresholds to intelligent, automated, and proactive systems. At Salesforce, we've embraced this challenge head-on by pioneering AI for IT operations (AIOps). We're already seeing 2,800 engineering hours now saved weekly on Warden AIOps, an AIOps agentic platform to help our site reliability engineers (SREs) and service owners proactively detect, diagnose, and remediate issues faster with minimal manual effort.

This isn't just about managing scale; it's about building an intelligent, proactive, and fully autonomous system that frees our engineers to focus on keeping services up and running smoothly, not constant firefighting.

The Challenge: From Manual Monitoring to Intelligent Automation

Managing vast and intricate systems involves a significant amount of manual effort. Our SREs and service owners often found themselves "glass watching" — staring at dashboards across disparate systems to identify issues. This reactive approach, while necessary, was inherently limited by human capacity and the sheer volume of data.

This challenge led to the creation of Warden AIOps, our system that leverages AI to assist with operational tasks. Our vision for Warden AIOps is to transform day-two operations, the ongoing management, maintenance and monitoring of a system after its deployment, by moving from manual, reactive interventions to automated, proactive, and safe operations. In doing so, we've built a system that can take actions like automatically adjusting resources, restarting pods, or running custom scripts to safely prevent outages before they happen.

A New Era of Proactive Operations

Here's how Warden AIOps is helping our engineers with quick and automated resolution, improving overall service availability:

  • Intelligent Anomaly Detection with Merlion: One of our foundational breakthroughs was the development of Merlion, an open-source library that we developed specifically for the purpose of anomaly detection. Merlion combines traditional models like isolation forests and statistical models with sequential neural network models. This allows us to identify subtle deviations and predict potential issues before they escalate into incidents. We also developed Moirai, an open-source foundation model for time series forecasting, which predicts potential spikes or dips in our systems.
  • Unified Observability for Comprehensive Context: To achieve truly intelligent operations, we needed a unified view of our vast and complex data. We aggregate three petabytes of data daily from various sources, including metrics from service level objectives (SLO) metrics, custom metrics, events, logs, and profiling and diagnostics. This eliminates the manual effort of sifting through different dashboards, allowing our systems to correlate information and give engineers a full contextual understanding.
  • From Correlation to Causation (and Remediation): Our PyRCA open-source library developed by the Salesforce Research team, helps us analyze hundreds of telemetry, dependency graph, and tracing data points to pinpoint root causes, significantly reducing the time for humans to identify key signals. We also use generative AI to auto-generate Root Cause Analysis (RCA) and Problem Review Board (PRB) reports and an orchestration engine to take immediate, rule-based actions to mitigate incidents, such as restarting app servers, even while the true causation is being investigated.
  • The Agentic Leap: Reasoning Like Humans, at Scale: Agentic AI adds a "reasoning layer" on top of our anomaly detection. Our system can now describe anomalies in natural language, correlate metrics, and reason like a human, using context to determine if a signal is truly anomalous. This capability automates log anomaly detection and allows engineers to dynamically explore problem patterns.

The Road Ahead: Towards a More Autonomous Agentic Enterprise Future

Our journey with AIOps is continuously evolving. The integration of tools like Cursor with Warden AIOps, via Model Context Protocol (MCP), is paving the way for a more autonomous state, a "flow state" where developers and service owners can easily transition from a signal to identifying the problematic code with repercussive context (even business impact), and taking necessary actions.

We are building an agentic enterprise future where our infrastructure is not just managed, but intelligently self-optimizing and self-healing. 

Warden AIOps is an internal Salesforce AIOps platform, and Merlion, Moirai, and PyRCA are open-source tools. These technologies are not available for sale.

Paul Constantinides is EVP of Engineering at Salesforce

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How Engineers Can Use AIOps to Innovate Their Infrastructure

Paul Constantinides
Salesforce

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI.

The need for a new approach to IT operations is critical, one that moves beyond manual monitoring and static thresholds to intelligent, automated, and proactive systems. At Salesforce, we've embraced this challenge head-on by pioneering AI for IT operations (AIOps). We're already seeing 2,800 engineering hours now saved weekly on Warden AIOps, an AIOps agentic platform to help our site reliability engineers (SREs) and service owners proactively detect, diagnose, and remediate issues faster with minimal manual effort.

This isn't just about managing scale; it's about building an intelligent, proactive, and fully autonomous system that frees our engineers to focus on keeping services up and running smoothly, not constant firefighting.

The Challenge: From Manual Monitoring to Intelligent Automation

Managing vast and intricate systems involves a significant amount of manual effort. Our SREs and service owners often found themselves "glass watching" — staring at dashboards across disparate systems to identify issues. This reactive approach, while necessary, was inherently limited by human capacity and the sheer volume of data.

This challenge led to the creation of Warden AIOps, our system that leverages AI to assist with operational tasks. Our vision for Warden AIOps is to transform day-two operations, the ongoing management, maintenance and monitoring of a system after its deployment, by moving from manual, reactive interventions to automated, proactive, and safe operations. In doing so, we've built a system that can take actions like automatically adjusting resources, restarting pods, or running custom scripts to safely prevent outages before they happen.

A New Era of Proactive Operations

Here's how Warden AIOps is helping our engineers with quick and automated resolution, improving overall service availability:

  • Intelligent Anomaly Detection with Merlion: One of our foundational breakthroughs was the development of Merlion, an open-source library that we developed specifically for the purpose of anomaly detection. Merlion combines traditional models like isolation forests and statistical models with sequential neural network models. This allows us to identify subtle deviations and predict potential issues before they escalate into incidents. We also developed Moirai, an open-source foundation model for time series forecasting, which predicts potential spikes or dips in our systems.
  • Unified Observability for Comprehensive Context: To achieve truly intelligent operations, we needed a unified view of our vast and complex data. We aggregate three petabytes of data daily from various sources, including metrics from service level objectives (SLO) metrics, custom metrics, events, logs, and profiling and diagnostics. This eliminates the manual effort of sifting through different dashboards, allowing our systems to correlate information and give engineers a full contextual understanding.
  • From Correlation to Causation (and Remediation): Our PyRCA open-source library developed by the Salesforce Research team, helps us analyze hundreds of telemetry, dependency graph, and tracing data points to pinpoint root causes, significantly reducing the time for humans to identify key signals. We also use generative AI to auto-generate Root Cause Analysis (RCA) and Problem Review Board (PRB) reports and an orchestration engine to take immediate, rule-based actions to mitigate incidents, such as restarting app servers, even while the true causation is being investigated.
  • The Agentic Leap: Reasoning Like Humans, at Scale: Agentic AI adds a "reasoning layer" on top of our anomaly detection. Our system can now describe anomalies in natural language, correlate metrics, and reason like a human, using context to determine if a signal is truly anomalous. This capability automates log anomaly detection and allows engineers to dynamically explore problem patterns.

The Road Ahead: Towards a More Autonomous Agentic Enterprise Future

Our journey with AIOps is continuously evolving. The integration of tools like Cursor with Warden AIOps, via Model Context Protocol (MCP), is paving the way for a more autonomous state, a "flow state" where developers and service owners can easily transition from a signal to identifying the problematic code with repercussive context (even business impact), and taking necessary actions.

We are building an agentic enterprise future where our infrastructure is not just managed, but intelligently self-optimizing and self-healing. 

Warden AIOps is an internal Salesforce AIOps platform, and Merlion, Moirai, and PyRCA are open-source tools. These technologies are not available for sale.

Paul Constantinides is EVP of Engineering at Salesforce

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...