Best AI Agents for DevOps

DevOps is one of the places where AI agents either save hours or make expensive mistakes. The difference is whether the tool understands the operational context, not just the syntax. Any agent can write a valid GitHub Actions YAML if you give it a simple enough prompt. The test is whether it writes a pipeline that matches your actual environment: the right runner labels, the secrets you already have, the Docker image tags you actually use, the deployment targets that already exist.

This guide covers the six agents I'd recommend to a DevOps engineer or platform team in 2026. The ranking reflects how each one handles the full DevOps surface: CI/CD pipeline authoring, infrastructure as code, deployment automation, and observability. I tested them on real tasks, not toy examples.

How I evaluated these agents

The evaluation covered four problem areas, weighted roughly equally.

CI/CD pipelines: authoring GitHub Actions, GitLab CI, or Jenkins definitions that match a real repo's structure, secrets, and targets, not a hello-world YAML from a tutorial.

Infrastructure as code: writing Terraform, Pulumi, or CloudFormation that accounts for state, naming conventions, and dependencies between resources.

Deployment automation: scripting blue-green deployments, rolling updates, Kubernetes rollouts, and the rollback paths that make them safe.

Monitoring and observability: writing alert rules, dashboard queries, and runbook automation that connects to real data sources.

A tool that handles IaC well but can't reason about a failing deployment step is only covering part of the job.

1. Claude Code

Claude Code is the best general-purpose AI agent for DevOps work, primarily because of how it handles context across large infrastructure projects. Terraform projects with ten or more modules, Kubernetes clusters with dozens of resource files, monorepos with layered CI configurations, these are the scenarios where context window depth separates serious tools from novelties.

For IaC work, point Claude Code at your Terraform root module and the relevant child modules, describe the change, and it generates output that respects your existing variable names, conventions, and provider versions. On a test involving adding a new RDS instance to an existing AWS setup, it referenced the existing VPC and subnet modules correctly, matched the established naming pattern, and added the right security group rules without inventing resources that didn't exist.

For CI/CD, the terminal-native interface is a real advantage. Claude Code reads your existing pipeline files and extends them consistently. When I added a staging deployment stage to an existing GitHub Actions workflow, it picked up the environment variable names, Docker registry, and approval gate pattern already in the file.

Plan mode matters specifically for DevOps. Before touching anything, Claude Code shows you what it intends to change and which files it will touch. On infrastructure work where a wrong change can take down a service, that review step is not optional.

The limitation is AWS-native tooling. Claude Code has broad IaC knowledge but it doesn't have live access to your AWS console or CloudWatch metrics. For troubleshooting a running environment, you're pasting logs in rather than having the agent query them directly.

Best for: Complex multi-module Terraform, CI/CD pipeline authoring, Kubernetes manifest work, and any DevOps task where codebase context matters. Pricing: Claude Pro ($20/month) or API usage.

2. Devin

Devin handles DevOps tasks in a way that no other tool on this list does: autonomously, end-to-end, without you watching each step. Give it a well-defined infrastructure ticket, "add a CloudWatch alarm for 5xx errors on the production ALB and wire it to the existing PagerDuty integration", and it will work through the task in a sandboxed cloud environment, write the Terraform, test the plan output, and open a pull request.

That autonomous loop is most useful for platform teams with well-defined operational tasks that are time-consuming to implement manually. On a test involving a multi-step ECS task definition update with a corresponding CodePipeline configuration change, Devin completed the full task with correct cross-resource references and a valid plan output.

The constraint is consistent with every autonomous agent: it executes better than it designs. Ask Devin to implement a defined change and it delivers. Ask it to design the monitoring strategy for a new microservice and you'll get something functional but generic.

At $500/month for the Teams plan, Devin needs to replace real engineering time to justify the cost. For a platform team with a high volume of repetitive tickets, that math works. For a solo DevOps engineer, it doesn't.

Best for: Platform teams with well-defined infrastructure tickets, automation of repetitive IaC changes, autonomous PR generation for operational tasks. Pricing: $500/month (Teams).

3. Amazon Q Developer

Amazon Q Developer is the strongest choice if your infrastructure runs on AWS. It's not a general-purpose coding agent. It's a tool built specifically around the AWS service model, with native integration into the AWS console, CloudWatch, CodeCatalyst, and the full CLI toolchain.

The differentiator for DevOps work is live environment awareness. Amazon Q Developer can query your running AWS environment: it knows your existing EC2 instance types, your ECS cluster configuration, your S3 bucket policies, your CloudWatch log groups. When you ask it to write a CloudWatch Logs Insights query to surface errors from a specific Lambda function, it pulls the actual log group names from your account rather than asking you to supply them. That operational context is something no file-only agent can replicate.

For CloudFormation and CDK specifically, Q Developer's suggestions are grounded in your actual AWS account configuration in a way that generic agents aren't. It knows which IAM roles exist, which VPCs have the right subnets, which KMS keys your team uses. The output is less likely to require manual corrections for environment-specific variables.

The limitation is the AWS boundary. Q Developer is not useful for GCP, Azure, or anything outside the AWS ecosystem. If your infrastructure is multi-cloud or you're primarily on Kubernetes without AWS, it's the wrong tool. And its general coding and CI/CD authoring capabilities, while solid, are not as strong as Claude Code on complex multi-file tasks.

Best for: AWS-native infrastructure teams who want an agent with live access to their cloud environment. Pricing: Free tier available; Q Developer Pro at $19/user/month.

4. Cursor

Cursor earns its place on this list for DevOps engineers who live in a code editor and want agentic assistance without leaving VS Code. Most IaC work happens in files: Terraform HCL, Kubernetes YAML, Helm chart templates, pipeline definitions. Cursor's multi-file Composer mode handles that work well.

The Terraform workflow in Cursor is practical. You open the modules you're working with, describe the change you want to make, and Composer generates a diff across all affected files: the main configuration, the variables file, the outputs file, and any child modules that need updating. The diff view is the feature that makes this safer than a chat-based agent: you see every change before it's applied, in a format that's easy to review and reject selectively.

For pipeline authoring, Cursor understands GitHub Actions, GitLab CI, and CircleCI syntax well enough to extend existing configurations correctly. It reads the existing workflow file and uses the patterns already there rather than generating a clean-room template.

The gap versus Claude Code is context depth. On a large Terraform project with many interdependent modules, Claude Code handles more of the full picture in a single pass. Cursor's context window is narrower, so you need to be deliberate about which files you include. For most DevOps tasks that's manageable; for large-state projects it starts to matter.

Best for: DevOps engineers who want VS Code integration for IaC and pipeline work, and prefer a diff-based review workflow. Pricing: Cursor Pro at $20/month.

5. OpenHands

OpenHands is the open-source autonomous agent that is most directly relevant to DevOps automation. It runs locally (or self-hosted), supports multiple model backends, and is built around a task execution model where the agent plans and executes multi-step operations in a sandboxed environment.

For DevOps use cases, OpenHands is worth knowing about for two reasons. First, it can execute shell commands and scripts as part of its task loop, which means it can run terraform plan, read the output, correct the configuration, and run the plan again, all without you intervening after the initial prompt. Second, because it runs locally or on your own infrastructure, it's the right tool for teams with strict data residency requirements who can't send infrastructure code to third-party cloud agents.

The model flexibility is a practical advantage. You can point OpenHands at Claude Sonnet, GPT-4o, or a locally hosted model. The core agent behavior stays the same; only the model changes.

The tradeoff is setup overhead. OpenHands requires more initial configuration than a commercial tool, and some edge cases that commercial tools handle smoothly require workarounds. The confirmation prompts before destructive actions are on by default, which matters for infrastructure work.

Best for: Teams with data residency requirements, open-source preference, or who want a self-hosted autonomous agent for DevOps automation. Pricing: Free (open-source); you pay for API usage at your chosen model provider.

6. Gemini CLI

Gemini CLI is Google's terminal-based agent, and it earns a place on this list for DevOps engineers in the Google Cloud ecosystem. It brings a long context window (1M tokens) to terminal-native work, which is relevant for large Kubernetes or GKE configurations where you want to feed the agent a substantial portion of your cluster state.

For GCP-specific DevOps work, Cloud Build pipelines, GKE cluster configurations, Terraform for Google Cloud resources, Cloud Run deployments, Gemini CLI's knowledge of the Google Cloud service model is notably strong. It generates correct gcloud CLI invocations, understands the IAM model for GCP resources, and produces Cloud Build YAML that uses the right builder images and step patterns.

Beyond GCP, Gemini CLI is a capable general terminal agent. The long context window is useful for feeding large YAML configurations or multi-file Terraform projects without hitting truncation. For pipeline debugging, the context capacity means you can paste a full CI log and ask what's wrong without summarizing it first.

The limitation is that it doesn't have live access to your GCP environment the way Amazon Q Developer has live AWS access. You're working from files, not from a live state query. For non-GCP infrastructure it's a capable general agent, not a specialist.

Best for: Google Cloud infrastructure teams, large Kubernetes configurations, and DevOps engineers who prefer a terminal-native agent with a long context window. Pricing: Free with a Google account (Gemini API quota applies).

Comparison: what each agent handles well

"Fair" means the task is completable with more manual guidance. "Good" means it handles the common case reliably. "Excellent" means it handles complex, production-scale versions of the task without significant hand-holding.

The honest recommendation

For most DevOps engineers doing general infrastructure work across any cloud, Claude Code is the place to start. The combination of deep context, plan mode, and terminal-native workflow fits how infrastructure work actually happens. It's not perfect on cloud-specific operational tasks that require live environment access, but for file-based IaC and pipeline work it outperforms everything else on the list.

If your infrastructure runs primarily on AWS, Amazon Q Developer belongs in your toolkit alongside whatever general agent you use. The live environment awareness makes a real difference for troubleshooting and for CloudFormation/CDK work that needs to match your actual account state.

Devin is worth evaluating if your platform team has a high volume of well-defined operational tickets. The autonomous end-to-end execution is genuinely useful for implementation-heavy tasks, but at $500/month you need clear evidence that it's replacing meaningful human time.

For teams with data handling requirements or open-source mandates, OpenHands is the right answer. The self-hosted model gives you the agentic execution loop without sending infrastructure code to external services.

Cursor makes sense if your DevOps work happens inside VS Code and you want the diff-based review workflow for IaC changes. It's a better fit for engineers doing a mix of application and infrastructure code in the same editor session.

For GCP-focused infrastructure, Gemini CLI is worth pairing with Claude Code for tasks where Google Cloud service knowledge and long-context YAML work are the primary requirements.

For more on how these tools perform on the application code side of your stack, see our guide to the best AI agents for backend development.

Frequently asked questions

Can AI agents replace a DevOps engineer?

No. They replace time on well-understood, implementation-heavy tasks: writing a Terraform module from an existing pattern, extending a pipeline configuration, authoring alert rules from a spec. The judgment work, architectural decisions, evaluating tradeoffs, responding to novel incidents, still requires a human.

Which AI agent is best for Terraform?

Claude Code is the strongest general option. Give it your root module, modules directory, and tfvars files and it generates additions that respect your conventions. Amazon Q Developer is better if you're writing CloudFormation or CDK and want live visibility into your AWS account state.

Are these agents safe to use with production infrastructure?

With guardrails, yes. Use plan mode before applying anything. Never give an agent credentials with direct write access to production. Work in a branch and review AI-generated IaC the same way you'd review any infrastructure change. The risk is plausible-looking output with subtle errors, not malicious output.

Do any of these agents integrate with Datadog or Grafana?

Not natively. These agents work with monitoring configuration files (Terraform resources for Datadog monitors, Grafana dashboard JSON, alertmanager YAML) rather than live platform APIs. Amazon Q Developer is the partial exception for CloudWatch.