Your cloud environment has become increasingly complex to operate. There are multiple applications running in your cloud (public, private, hybrid) on different platforms. You’re collecting tons of data on these applications and services that you need to understand and keep operating.
From performance metrics, to application logs, security findings and billing data. CloudOps teams are tasked with understanding all of this information and ensuring that the applications and services continue to meet SLAs and stay online.
Automation and rule-based alerts have helped with reducing the amount of manual work required. However there are only so many rules you can build out and when there are multiple applications and services interacting with one another rules will fail.
By introducing artificial intelligence (AI) into your cloud you can allow for these problems to be identified and automatically remediated. From finding outliers in data sets and correlating findings to providing recommendations on what actions should be taken. AI can provide all of these capabilities with the proper guardrails in place. Allowing your CloudOps team to become more autonomous in their day-to-day activities.
That’s when we start moving from automation to autonomous cloud.
Gartner’s research, Transform CloudOps With Agentic AI, discusses how agentic AI can address fragmented tools and rigid workflows in cloud operations.
By implementing AI in your cloud operations you can improve your uptime, decrease the amount of manual tasks, reduce cloud costs and allow your team to focus on what’s important to the business. Here’s how.
What is AI for Your Cloud?
Using artificial intelligence and machine learning to understand your cloud infrastructure and the workloads that are deployed to it. You can think of this as taking what you already know about cloud operations and applying AI to that knowledge.
AI can allow you to monitor your environment and provide insight on any anomalies or trends that it may find. It can also take those actions for you once you have provided the proper permission and context to it. Google Cloud provides an example of proactive cloud operations with Gemini Cloud Assist and agentic operational workflows.
There are many tasks that you can use AI to help with in your cloud operations. Here are a few examples:
- Find anomalies in your cloud environment and applications
- Correlate alerts to find a common thread
- Forecast potential outages
- Recommend resources to reduce cost and improve performance
- Assist in debugging and resolving incidents
- Auto remediate certain tasks
- Create tickets based on events in your environment
- Manage policies and configurations
Many of these capabilities fall under the term AIOps or AI for IT Operations. Using AI to assist IT teams with the complexities of operating infrastructure.
Taking this a step further you can allow AI to take actions for you and be considered autonomous. By providing your AI the proper permission and context it can perform certain tasks that you would normally perform manually.
Of course you want to be careful about what you allow AI to do autonomously. Having AI provide a recommendation to fix an issue and having it perform that action for you are two different things. You will want to decide what you want to automate and what you want humans to take care of.
Why Evolve Cloud Operations?
Cloud environments allow us to do many great things. However with that we’ve seen our environments become more complex.
Cloud Environments Have Become More Complex
With the proliferation of clouds and platforms we now have multiple locations to monitor and keep operational. Not only do we have more places to look but each location can have different types of monitoring and alerts.
Making it harder to see the big picture of what may be causing your incident.
Too Many Alerts
With more environments we’ve seen an increase in alerts. Whether those alerts are good or bad it becomes hard to know what to look at first.
You may spend more time understanding each alert than actually finding what’s causing them.
Reducing Costs
As you add more applications and services to your environment your cloud costs will grow. You want to ensure that you’re only paying for what you need. And not leaving extra compute, storage or network sitting around unused.
There are ways that you can optimize your costs but they may not be sustainable if your environment continues to grow.
Applications Need to Stay Online
As businesses move more towards digital customers want those services to be online. Any downtime can affect transactions and employee productivity.
We want our cloud operations teams to be able to predict outages and fix them before they happen or have processes in place that can keep applications up and running.
This is why we need to introduce AI into our cloud operations. We want to be able to understand what’s happening in our environment and take action on those findings.
Evolving Cloud Operations With AI
We can think about evolving your cloud operations with AI in four steps. Your company may choose to implement each of these in different orders or have some workloads that are more hands-off then others.
1. Monitor and detect
Collect metrics, logs, traces, and events to identify unusual behavior and service degradation.
2. Analyze and predict
Use AI to correlate alerts, detect patterns, estimate risks, and recommend actions.
3. Automate approved actions
Execute predefined workflows, such as restarting a service, scaling resources, or opening an incident ticket.
4. Operate autonomously within guardrails
AI agents can investigate, plan, and execute authorized actions while recording decisions and escalating uncertain or high-risk situations.
Once you reach level 4 you’re no longer requiring someone to manage these tasks for you. Allowing your team to spend time on other areas of your cloud.
Use Cases For AI in your Cloud
Monitoring the Environment
Most likely you have alerts set up for your applications and services. Alerts can tell you when your CPU is above a certain percentage or memory has run out. Read Mastering Cloud Operations: Key Understandings and Smart Approaches for related guidance on operating and managing cloud environments.
While these are great alerts they can only go so far. You may have a spike in latency for your application but CPU and memory may not be affected. If we can look at the data and see a trend of this we can alert ourselves before it becomes an outage.
Again this is all dependent on what telemetry you have and what you can pull from your environment. But by using AI to understand this data we can reduce the number of false positives and give your team time to debug.
Predicting Outages
If we can take our monitoring data and understand past incidents we can predict future outages. Whether that’s an application spiking in demand or infrastructure reaching its limits.
You could also use AI to correlate multiple alerts and find what may be causing them.
We don’t want to rely on these predictions but it can help give us time to resolve issues before they happen.
Auto Remediation
Allowing AI to automatically remediate issues in your environment is one of the first steps to becoming an autonomous cloud.
Many of these tasks can be performed by humans and follow a set path to resolve. Whether that’s restarting a service or spinning up additional instances. If we can train our AI on these tasks it can perform them for us.
You could have an application that goes down due to some type of blip. Your AI could understand what happened and restart the service for you. Then check to see if it’s back online and log that it did that.
We want to start small and grow our self healing infrastructure. Making sure that if something doesn’t work we can escalate to a human.
Reducing Costs With AI
Similar to monitoring we can use AI to find outliers in our billing data. Identify if we have extra resources that aren’t being used or can be downsized. We can also predict how much we’ll need in the future. Read Cloud FinOps: Maximizing Cloud Investment Returns for more on cost visibility, forecasting, and resource optimization.
Maybe you have an environment that is always running but is only used during the day. Your AI can apply a schedule to turn that off and let the team know that it’s happening.
Another way to reduce your cloud costs is to know what you’re paying for. By understanding what resources are driving your bill you can optimize them.
Capacity and Performance
Applications and services will grow and shrink over time. Maybe there’s a holiday that your customer base increases or you release a new feature that drives more demand.
By using AI we can forecast how much capacity we’ll need and scale our environment to match. Not only will this help keep our application up but it can reduce our costs as well.
Make sure you understand all of your applications’ needs. You don’t want to overscale or underscale your environment.
Debugging Incidents
When an incident occurs your team will need to start looking at all of your monitoring dashboards and logs. They may also need to look at past tickets and articles to figure out what may be causing it.
Using Generative AI you can help your team find this information faster. Whether that’s explaining an error message or pulling information from past incidents.
You could ask your AI to summarize the alerts that have occurred and what may be causing them. It can then help your team start debugging.
Make sure your AI understands what it knows and what it’s assuming. And that it has the proper access to the information you’re looking for.
How to Become an Autonomous Cloud
As we talked about earlier there are many steps to becoming an autonomous cloud. Here are some tips to get you started.
1. Understand your current cloud operations
First we need to know where we are today. What platforms and applications are we running? What are we currently monitoring and alerting on? What do we do when an incident happens?
Find areas that we spend too much time on or could be more efficient. Once we know this we can build some metrics to help us measure success.
2. Know your data
We want to make sure we have the right data that our AI can learn from. This could be metrics from your environment, application logs, deployments, etc. Make sure your AI can access this data in a timely manner.
If your data is scattered or not up to date you won’t get the best results from your AI. So take the time to understand what data you have and where it’s located.
3. Pick your use cases
Once you know what data you have we can start picking use cases for your AI to help with. Think about what tasks take the most time or what could be improved with AI.
Ask yourself these questions when selecting your use cases.
- How often does this task happen?
- How much time do we spend on this?
- What happens if we don’t complete this task?
- Can we automate this task?
- How will we know if it’s successful?
Start with one or two use cases and build from there. Don’t try to automate everything at once.
4. Connect AI to your existing processes
Once you’ve picked your use cases we want to connect AI to the processes your team already has. This could be your monitoring platform, ticketing system or even your cloud provider.
You want to make sure that when your AI is taking an action it’s going to the right place. And that your team is aware of what it’s doing.
5. Create guardrails
Before you let your AI start taking actions you’ll want to create some guardrails. What can it do and what does it need human input for.
You may want to allow your AI to restart a service if it detects one that’s down. But maybe it needs approval before it can delete a resource or change a network rule.
Have some type of logging and monitoring setup to know what your AI is doing.
6. Measure your success
After you’ve implemented some AI into your cloud you want to make sure it’s working. Go back to the metrics you created in step 1 and see if there’s been an improvement.
Are your incidents being resolved faster? Are we getting fewer false-positive alerts? Is our cloud bill decreasing?
If you see that you’re not improving in an area maybe it’s not the right use case for AI. Or you may need to adjust how your AI is learning.
Why You Need Governance With An Autonomous Cloud
As we allow AI to take more actions we want to make sure we have proper governance around it. We don’t want our AI making changes that could hurt our business or cause outages. The AWS Operational Excellence Pillar provides related guidance on observability, safe automation, operational KPIs, and reversible changes.
Maybe your AI finds a way to reduce costs by scaling down a service but it causes an outage. Or your AI has too much access and can change things it shouldn’t.
We want to have proper roles and permissions around what our AI can and can’t do. Have a process to escalate to a human if something doesn’t seem right. Microsoft Azure also outlines operational excellence design principles for monitoring, automation, standardized operations, and safe deployments.
The states that we should have actionable observability, automate safely and make small changes. These are all things to consider when allowing AI to take actions.
Same goes for with their Well-Architected framework. They want us to have observability, automate, have standards and deploy safely.
So as we allow our cloud to become more autonomous we want to continue to monitor and understand what’s happening.
What To Look Out For
Here are some things to be mindful of when implementing AI into your cloud.
Automating bad processes If you have a bad process in place AI will just make it worse. Make sure your processes are solid before trying to automate them.
Assuming your AI is always right There will be times that your AI makes a mistake. Monitor what it’s doing and make sure it’s doing what you expect.
Not knowing your data If your data isn’t clean or you don’t know what you have your AI will struggle to find what it’s looking for.
Giving too much permission Be careful about what you allow your AI to do. Start small and grow from there.
Measuring automation vs results Just because you automated a task doesn’t mean its successful. Look at your overall results to see if you’re improving.
Going too autonomous too fast Take your time and build trust with your AI. Don’t jump into allowing it to do everything at once.
How Hexaware Can Help With AI For Your Cloud
If you’re looking to improve cloud operations and expand automation, Hexaware’s Enterprise Service Management and CloudOps capabilities can support the move toward autonomous cloud operations.
Hexaware combines AI services and solutions, automation, Tensai® capabilities, and Amaze® for cloud transformation to support governed, increasingly autonomous cloud operations.
Read about Hexaware’s ISG recognition for AI-powered cloud operations and the capabilities described in the announcement.
So if you’re looking to take your cloud operations to the next level and allow for more automation. Let’s chat about how we can help.
Final Thoughts
As we’ve learned there are many ways to use AI in your cloud. From monitoring to auto remediating issues. There are many benefits to using AI and becoming an autonomous cloud.
Just remember that it won’t happen overnight. Take your time and understand where you want to go. Then start building the processes and automation to get you there.
