AI DevOps: Use Cases, Agents & Safe Adoption

AI DevOps is now crucial for delivering increasingly complex daily activities quickly. Using AI in DevOps processes can speed up your deployments, but how do you manage the guardrails around it?

What we’ll cover:

1. What is AI DevOps?
2. AI agents in DevOps
3. Use cases: Where AI helps most in the DevOps lifecycle
4. How to adopt AI in DevOps safely
5. What are the best AI tools for DevOps?

AI DevOps, often called AIOps (Artificial Intelligence for IT Operations), uses machine learning (ML) and artificial intelligence (AI) to accelerate software delivery and improve security. In other words, it is the integration of artificial intelligence into the DevOps toolchain.
Engineers use it to handle repetitive tasks, debug issues faster, and analyze large volumes of data, freeing up time to focus on more complex tasks.

AI can help DevOps engineers:

• Develop code: Create pipeline configurations, infrastructure templates, and various types of automation scripts.
• Improve governance and compliance: AI can help implement role-based access control (RBAC), policies to restrict the creation of certain resources or their parameters, and more.
• Implement better security: AI can offer recommendations on how to improve your security posture by helping you implement shift-left mechanisms such as vulnerability scanners, policy as code, and linters, and also help you improve your runtime security.
• Improve observability: AI analyzes telemetry at scale, detects anomalies, predicts failures, and can also act to solve these issues for you.
• Reduce costs: AI can give you insights into your resource costs and help you reduce them based on your utilization.

What’s the difference between AI in DevOps and DevOps for AI (MLOps)?

AI in DevOps means applying AI to improve processes in the software development lifecycle (SDLC), such as using it in your pipelines and deployments to make them more efficient and optimized, or even to generate Terraform/OpenTofu modules used by multiple configurations across your organization.

On the other hand, DevOps for AI (MLOps) is the utilization of DevOps principles to manage the lifecycle of machine learning models. In this case, building a pipeline to validate and deploy a recommendation model is an MLOps use case.

Platform teams use both of these concepts, but they address different problems and require different expertise.

AI agents in DevOps differ from traditional automation; the latter is deterministic and  executes exactly what you script, usually line by line. AI agents, on the other hand, are adaptive, goal-oriented, and determine the steps they need to follow based on the objective you give them.

1. Coding and IaC agents

Many tools can help you generate full Terraform modules, Helm charts, or CI/CD pipeline definitions by providing only a description of the infrastructure you need.

These tools include GitHub Copilot, Cursor, and Amazon Q Developer. These agents are still evolving and helping engineers skip repetitive IaC work every day, compressing hours of work into minutes.

One tool that stands out for IaC is Spacelift Intent. It frees developers who need a lighter way to deploy infrastructure when they implement new features.
Traditionally, infrastructure required an entire provisioning process, even for something as ephemeral as a quick test. Now, with Spacelift Intent, you can easily spin up this infrastructure and tear it down once your work is complete.

2. Security agents

These agents secure the systems they interact with and protect them against various types of cyberattacks. They not only flag or detect vulnerabilities, but they also explain them and suggest fixes.

Security agents can be a powerful tool for developers who are not security specialists. Agents handle research and synthesis and provide solutions to engineers, but they must not make the decisions; human engineers must evaluate and decide whether the proposed solution is suitable.

3. Incident response agents

Incident response agents are very helpful when an alert fires, as they can correlate signals across metrics and logs and identify the root cause. After identifying the root cause, they suggest or execute remediation runbooks that you define.

In addition, incident response agents are particularly useful for addressing alert fatigue. Imagine you are working in a large enterprise with alerts set up for pretty much everything. If these alerts are also routed to Slack, your engineers might see hundreds of alerts daily, which can be really hard to follow up.

In this case, having an incident response agent can help you triage these alerts, mark the important ones for review, and automatically resolve the easy tasks with a runbook.

The areas where AI helps most in the DevOps Lifecycle are:

• IaC and containers: AI helps DevOps engineers write Terraform modules, Kubernetes manifests, and Dockerfiles, resulting in faster iteration and fewer configuration errors. This helps them be more efficient and avoid manual errors.
• Log analysis: Many companies need to collect and process log data. The sheer volume of data makes it difficult for engineers to perform manual review. AI helps streamline processes and enables DevOps engineers to understand how systems and applications are functioning.For example, before releasing to the production environment, AI can flag anomalies and misconfigurations in your code, making it easier for developers to correct them before release.
• Cloud costs: AI tools can help analyze your infrastructure utilization, identify overprovisioned resources, and suggest right-sizing. This can easily result in savings that manual reviews often miss.
• Security scanning in your CI pipelines: In DevOps workflows, whenever code is committed, your CI system should automatically run tests and scan your code for vulnerabilities. In this way, security issues are detected early in the SDLC.You can also leverage AI-augmented SAST (Static Application Security Testing) to analyze your source code without executing it, and you can use supply chain security tools to catch vulnerabilities earlier and receive suggestions on how to fix these issues.
• Observability: You can gain better insights into which resources are running and how much CPU and memory they consume. You can also receive recommendations to improve your overall reliability, and, in some cases, get automatic adjustments.
• Incident triage: On-call engineers must manually review each of the many alerts they receive each day. AI can help you group alerts and route incidents to the appropriate responders, so engineers receive a single consolidated alert rather than multiple ones.

Enterprises have started adopting AI in their DevOps workflows for one simple reason: They want to become more efficient and remain as competitive as possible.

Of course, native adoption of AI introduces real risks. Here are steps you can take to minimize risk when you decide to adopt AI in DevOps:

Step 1. Start with low-risk tasks

Begin with lower environments and tasks such as code review assistance, documentation generation, and test writing. The output of these tasks is easy to verify, and there is no chance of downtime. Don’t give AI access to your production deployment pipeline from the start.

Step-by-step adoption with constant validation is the way to be safe. Once you are more familiar with your AI assistants and have more confidence in their abilities, you can start using them in more projects.

Step 2. Human review

Make human verification of all AI-generated code and configurations a team rule. Engineers who don’t respect it can be the primary source of AI-related incidents.

Step 3. Adopt approval gates

Your production environment needs to be protected and secure. If you use AI agents to make changes in production, you should require human approval before making these changes.

Imagine that you upgrade your Kubernetes cluster to a newer version using AI for the first time, without reviewing or obtaining approval from the DevOps team. A mistake could result in substantial losses and damage to your organization.

Step 4. Implement security as a first-class citizen for AI

You need to apply the same security rigor to your AI tools as to any other third-party integration. AI agents access your cloud credentials, monitor different data, and also have access to your code.

A good practice is to grant only the minimum necessary privileges to protect your data (principle of least privilege).

Step 5. Constantly evaluate AI outcomes

Monitor AI suggestions that your team accepts and what happens when they do, and then evaluate the data. Based on the outcome, you can easily adjust the trust you have in the AI tools you are using.

The abundance of AI tools for engineers makes it challenging to choose the right ones. Here are some of the best AI tools for DevOps:

• Coding and IaC:
  • GitHub Copilot is the most popular AI coding assistant for both application development and IaC. Developed by GitHub and OpenAI, it can assist users in Visual Studio Code, Neovim, Eclipse, and many other IDEs.
  • Cursor is a leading AI-powered IDE. It integrates AI directly into the software development workflow, helping users generate, refactor, evaluate, and edit code.
  • Claude Code is another popular AI tool that also helps DevOps engineers with infrastructure code, offering solutions, suggestions, and plain-English explanations (or any other language, for that matter).
  • Spacelift Intent helps you create infrastructure resources using natural language and maintains a state for all these resources, so you can easily deprovision them when they are no longer needed. This is particularly useful for experimenting with new features.
• Containerization: 
  • K8sGPT is a powerful AI tool for Kubernetes operators, helping with scanning Kubernetes clusters, diagnosing issues, and providing remediation advice.
  • Cast AI was built to scan and detect errors at the Kubernetes cluster level and to adjust and remediate them.
  • Lens Prism is a context-aware AI assistant embedded directly within Lens Kubernetes IDE. Users can use their preferred language to interact with their Kubernetes clusters, troubleshoot issues quickly, and receive actionable insights.
• Observability:
  • Datadog has an AI assistant called Bits AI SRE. Bits AI SRE specializes in telemetry and understands your organisational context. It helps you investigate alerts and surface actionable root causes in minutes.
  • Grafana Cloud is another powerful observability tool. Its AI offering helps engineers reduce alert fatigue and accelerate multi-step incident investigations. It analyzes dashboards and logs, offering fast identification of the root cause of incidents.
• Security: 
  • Snyk has AI features that explain vulnerabilities and suggest fixes. It helps engineers minimize alert fatigue, secure containers at scale and AI-generated code, and reduce the security backlog.
  • Wiz specializes in cloud security posture management, mapping every relationship across code, cloud, data, and runtime.

Read more: Top 12 AI Tools For DevOps

Spacelift is an infrastructure orchestration platform that helps you manage Terraform, OpenTofu, Terragrunt, Pulumi, CloudFormation, Ansible, and Kubernetes workflows from a single control plane. 

Spacelift includes policy as code, so you can control which resources engineers can create, which parameters they can use, how many approval runs require, where notifications go, and more. 

It brings together orchestration, governance, and visibility so teams can move quickly without losing control. That fits Spacelift’s two-path deployment model: rigorous IaC and GitOps workflows for production, and Intent for fast, non-critical work.

Spacelift also provides a native way to create dependencies between stacks and share outputs across them, which makes it easier to keep state files small and workflows modular. To balance speed and control, teams can extend governed self-service with offerings like Blueprints and Templates, as well as integrations with ServiceNow and Backstage.

Spacelift Intelligence adds an AI-assisted layer to that model.

In practice, Spacelift can help with three distinct use cases:

• You can provision resources using natural language: Leveraging Spacelift Intent, you simply describe in plain English what kind of infrastructure resources you need, and Intent will create the resources for you. Intent is designed  for experimentation, and the best part is that you still get a centralized state, so all the resources that are generated with Intent can also be deleted easily
• Import existing resources into Intent: Most enterprises have orphaned resources inside their cloud providers that they know nothing about. With Spacelift Intent, these resources can be easily identified and added to Intent’s state. Check out this video to learn more: 

• Detect and remediate infrastructure drift: Drift is inevitable, but it doesn’t have to be a big issue. As well as having scheduled drift detection and remediation built in, Spacelift also supports drift detection and remediation via Spacelift Intent. If you create a resource through Intent and modify it elsewhere, you can use natural language to easily identify and fix it. Learn more in this video:

In short, Spacelift combines infrastructure orchestration, governance, and Spacelift Intelligence to help teams move faster without losing visibility or control. Intent reduces IaC ceremony for the right workloads, which makes experimentation easier even for engineers without deep infrastructure experience, while platform teams keep the guardrails they need.

Adopting AI in DevOps can help your engineers write code faster, troubleshoot issues quickly, improve observability, and even help you improve your overall security posture. 

You should adopt a safe approach to AI in DevOps, starting with low-risk tasks, continuous human review, and security and audit controls. Having proper guardrails in place is the key difference between an organization that innovates and one that has to firefight AI misconfigurations.

If you want to learn more about how Spacelift can help you with adopting AI in your DevOps processes, book a demo with one of our engineers.