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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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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...