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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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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

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Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...