Overview
In complex IT environments, observability tools let you see and understand what’s happening. Those insights are useful, but what if you can do more?
With artificial intelligence for IT operations (AIOps) automation, you can apply AI to turn insights into action. This approach supports the outcomes you want: working more efficiently and delivering reliable and scalable IT systems.
When deployed together as part of a unified strategy, observability, AIOps, and automation can amplify each others’ strengths. However, many organizations make significant investments in these areas but struggle to bring it all together. Observability tools may produce a large number of alerts, leaving teams facing alert fatigue and high stress—even after applying AI to prioritize or add value to the alerts. Without context and effective integrations with your automation platform, you might experience an overwhelming amount of data but have no way to act on it consistently and at scale using automation you trust.
This article explains how observability with intelligence can support operational benefits, with an emphasis on Red Hat® Ansible® Automation Platform and its included Event-Driven Ansible. We’ll build on the core concepts of observability, AIOps, events, and automation, and show how they tie together to help you execute AI-informed decisions quickly through governed automation.
Our journey starts with an important resource: data.
Observability tools and their limitations
Data is the raw material that makes AI-enhanced automation possible. The first step in gaining value from your data is observability. As IT environments grow more complex, it’s not enough to monitor your error logs and react to them. You need a more complete picture.
Observability takes monitoring a step further. It aims to produce insights that help you proactively troubleshoot and optimize your IT systems and applications. Observability tools may combine traditional data—logs, metrics, and traces—with additional sources such as metadata, user behavior, network topology, and code-level details.
You have many choices of observability tools. Red Hat platforms integrate with popular observability platforms including Splunk, Dynatrace, IBM Instana, and LogicMonitor as well as industry technologies such as event buses, Kafka, and webhooks. It’s common to use multiple observability tools at the same time to better observe different systems and behaviors.
Bringing all this information together helps you see more. With observability, you’ll know not just that an issue has occurred, but also the root cause and what to do about it.
So far so good. Observability tools are excellent at surfacing information about what’s wrong and what needs to be done to support operations management. But your operations teams may find themselves overwhelmed by high numbers of alerts. Then what?
One approach is to code a series of predefined rules for how to respond to each alert. Unfortunately, this time-consuming process creates technical debt that comes due whenever there’s a change to how your systems work.
Observability isn’t much good by itself. You need to apply your data and insights intelligently. Here’s where AIOps enters our journey.
What is observability?
Operational intelligence at scale with AIOps
Observability alerts are flooding in fast. How do you determine what to do? AIOps offers the answer.
Think of AIOps as a concept rather than a product category or platform by itself. AIOps is an approach that applies machine learning and artificial intelligence to help manage the complexity of IT automation. Ideally, AIOps supplies the necessary intelligence to launch automated actions that support the outcomes you want. AIOps concepts work in harmony with the goals of platform engineering and site reliability engineering teams.
To implement AIOps, gather data from your observability sources to form a unified view of your IT environment. Then you can use machine learning to spot anomalies, identify patterns, and produce useful recommendations in real time. What’s more, AI-driven systems can improve over time. Rather than simply reacting the same way to each event, they can observe and adjust to better achieve the desired outcomes.
There’s no single way to incorporate AI into your operations. Many observability tools now have AI capabilities built in. You can also choose to bring your own AI models to your automation workflows.
By now you can see the value that comes from combining observability data with real-time AIOps intelligence. However, you’re still missing a way to transform this intelligence into useful actions. This brings us to events.
Events and why they matter to AIOps
An event is anything detectable and meaningful that happens in an IT system. It could be a change of state in any of your applications, hardware, software, cloud instances, or other technologies: Something starts up or shuts down. A network connection opens or closes. An activity exceeds a certain level. Those are all events.
Some events might require drastically different responses depending on the circumstances. A high load on a system could trigger a notification in normal operations, but if it’s running sensitive workloads it might require an immediate shutdown to prevent a security risk. Observability tools can detect events, while AIOps can help you contextualize them so you can trigger the appropriate automated response.
With events augmented by intelligence, you can specify the best course of action for a wide range of situations and adapt to new event types as they emerge. Now you've built the foundation you need to get the most value out of event-driven automation—which the next chapter in our story.
AI-powered event-driven automation
Event-driven automation is a way to launch automated IT operations workflows based on observability data. Event-driven automation helps humans stay aware of complex systems, including hybrid cloud, AI, and edge environments. It reduces routine and repetitive tasks, freeing IT operations teams to focus on more important work.
As mentioned above, you can apply AI to your observability data to make better automated decisions. You’ll be able to resolve issues efficiently and see more value from your event-driven automation workflows.
For users of Red Hat Ansible Automation Platform, the included Event-Driven Ansible set of features provide event-handling capabilities for automating tasks across IT domains.
Event-Driven Ansible relies on 3 components as building blocks:
- Sources provide event data about conditions in your IT environment. These events are sent to Event-Driven Ansible via plug-ins or webhooks.
- Rulebooks contain sets of rules and conditions that trigger an action. Rules define the appropriate responses to events.
- Actions are the result of the automation. They’re taken to address or remediate the event.
Ansible Rulebooks, like Ansible Playbooks, are written in human-readable YAML format. Unlike playbooks, rulebooks use conditional rules to define when an event should trigger an action. Event-Driven Ansible monitors for events, recognizes when events happen, and automatically executes the appropriate action.
You can integrate Ansible Automation Platform with open source tools such as Prometheus Alertmanager or Apache Kafka. You can choose from certified and validated ecosystem collections to more quickly deploy these automation integrations.
Combine event-driven automation with your existing tools and you'll start to see a series of benefits, which brings us to our next section.
Benefits of observability-supported automated AIOps
An AIOps approach to event-driven automation lets you apply observability data, AI insights, and rule-based logic to automate what would otherwise be overwhelming amounts of manual work. You can prioritize proactive measures over reactive, manual processes.
Some benefits of this approach include:
- Proactive detection. AI-powered anomaly detection helps prevent problems before they impact your users.
- Intelligent analysis. Automated root-cause identification and recommendations help you save time and get accurate answers at the moments you need them.
- Faster response. Your teams can move quickly with AI-informed decisions made with governed automation.
- Continuous learning. Rather than operating from a fixed set of rules, AI-driven systems can improve their recommendations over time.
The result is more reliable infrastructure, reduced costs, and faster issue resolution. Next we'll take a look at some of the specific use cases where these benefits make a difference.
AIOps automation use cases
Observability, automation, and AIOps can help address a variety of real-world business use cases.
Infrastructure reliability
You can use an AIOps approach to automatically address common alerts. Through this strategy, your observability platform triggers automated actions based on AI-augmented analysis and recommendations.
If a particular system begins to fail, automation kicks in to restart services, clear logs, reallocate resources, or scale infrastructure. This approach can help you remediate issues before they escalate, shorten mean time to resolution (MTTR), and boost system reliability.
Enhanced service tickets
Infrastructure teams can better respond to IT service management (ITSM) tickets when they have a clear picture of the situation. Using analytics tools that supplement events with AI analysis, you can add helpful information to your ITSM ticketing and tracking processes. You can provide preliminary analysis and priority scoring before tickets enter the queue, shorten MTTR, and reduce manual effort.
With this added context, your teams can better understand events so they can resolve issues quickly and limit downtime.
3 automated steps to faster ServiceNow ITSM ticket resolution with Ansible Automation Platform. Video duration: 10:54.
Optimized AI infrastructure
Meeting the demands of AI workloads is a challenge for IT infrastructure teams. Applying observability and automation keeps these complex systems working reliably with less manual toil. You can automate repetitive AI tuning tasks, including resizing infrastructure and reducing resource sprawl. You can also make systems more reliable by automating optimization patterns and configurations. Together these approaches prevent performance issues before they impact users.
As a result, your teams can accelerate their AI development cycles and move AI models from development to production along tested and reliable paths. Your organization can innovate faster and stay competitive.
Automated configuration-drift detection and correction
Configuration drift—when IT systems deviate from their desired state—is a common source of security vulnerabilities and instability. You can try to manage configuration drift with traditional monitoring, but an AIOps approach can do more by providing context about risks and impacts and prioritizing what to fix 1st.
When your monitoring or observability tools identify a configuration drift, you can use AI-augmented automation to prioritize corrections based on risk and business impact. You can also predict any cascading effects before you apply the corrections, and apply fixes when they’ll be least disruptive. You’ll reduce the security and stability issues that come from configuration drift without introducing additional disruptions.
Policy enforcement and compliance
Your IT teams rely on established policies to make sure systems comply with regulations and organizational standards. You can align your event-driven automation systems with these policies to stay compliant.
As part of your AIOps approach, you can incorporate these policies into automated decision making. When an AI system makes an inference and initiates automation, your systems can validate that action to make sure it complies with your policies.
With these uses cases in mind, the next step on our journey is a look at specific ways you can make these integrations work.
How to integrate observability and automation systems
To benefit from data-driven intelligent automation, you need to integrate your observability tools with your automation platform. Red Hat Ansible Automation Platform users have several choices:
- Event-Driven Ansible. Event-Driven Ansible is well suited for high-volume event processing. It’s the recommended choice for handling large bursts of observability alerts or streams of asynchronous events.
- Model Context Protocol (MCP). Designed for AI agents, MCP is an open source standard for communication between AI applications and external services. It’s ideal for agentic workflows and AI-assisted operations. If you’re integrating an AI model with Ansible Automation Platform, MCP is the right choice.
- Webhooks. A webhook is a way to send lightweight event-driven communication between applications over HTTP. Webhooks are limited in what they can do and are suitable for simple, push-based actions, such as triggering an ITSM ticket.
- REST application programming interface (API). Ansible Automation Platform can interact with other applications using a REST API, following an established standard for sharing information among applications. This helps support continuous integration and continuous delivery (CI/CD) pipelines and existing systems built for REST API standards. For new installations, one of the above methods will likely provide some advantages compared to the older REST API standard.
With these integration approaches in mind, we'll now look at how Red Hat solutions can help you put observability, AIOps, and automation to work for your teams.
Why choose Red Hat for AIOps?
To support your AIOps strategy, Red Hat’s unified solutions let you automate across environments and deploy validated and optimized AI models.
Red Hat Ansible Automation Platform
Red Hat Ansible Automation Platform is a comprehensive IT enterprise automation solution that helps you encourage productivity and break down barriers between teams. Through integrations with existing AI and observability tools, Ansible Automation Platform helps transform intelligence into repeatable, governed automation across your IT environments.
Included with your Ansible Automation Platform subscription, Event-Driven Ansible is a scalable, responsive automation solution that can process events containing discrete, useful intelligence. It allows your IT teams to determine the appropriate response to an event and execute automated actions to address or remediate the event.
Red Hat AI
Red Hat AI is a platform of products and services that can support your enterprise at any stage of the AI journey. It helps deliver generative and predictive AI models, including those deployed in support of AIOps.
With Red Hat AI, you can access Red Hat AI Inference Server to optimize model inference for faster, cost-effective deployments. Red Hat AI Inference Server includes the Red Hat AI repository, a collection of third-party validated and optimized models that allows model flexibility and prioritizes cross-team consistency.
Together these solutions can help you turn AI-driven intelligence into automated actions, improving how your teams make decisions at scale and at speed.
Unlock the full potential of AIOps with automation
If AI is to be operationalized successfully, IT automation must be integrated from the start. Download to read more.
