Skip to main content

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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...