Top 6 AI Agents for Platform Engineering That You Should Use in 2025

Over the last few years, I have seen a lot of changes in platform engineering. The work has become more complex. We now have to build, maintain, and scale platforms faster, make fewer mistakes, and keep them ready to adapt when things change. This is where AI agents come to the rescue.

I’m not talking about experimental AI projects that make good conference slides but never see the light of production. I’m talking about tools that solve everyday problems we face in platform engineering. I have even used some of these myself, and I’ve seen other teams benefit from them in measurable ways.

Here are six AI agents for Platform Engineering that are worth your attention.

Top 6 AI Agents For Platform Engineering in 2025

Here’s a detailed breakdown of the six key AI Agents that you can use for Platform Engineering:

1. GitHub Copilot for Infrastructure as Code

If you have ever spent hours writing Terraform scripts or Kubernetes YAML files, you will understand the pain. GitHub Copilot is not just for application developers; it works surprisingly well for Infrastructure as Code, too.

It helps generate configuration files, suggest syntax, and even fix errors before deployment. The advantage is not just faster coding but fewer misconfigurations that could cause issues in production. I have seen teams use Copilot to standardize IaC templates and cut setup times by half.

The key is to treat it as a pair programmer, not an autopilot. It still needs your engineering judgment to ensure everything is secure and compliant.

2. Azure OpenAI Service for Operational Automation

When you manage a multi-cloud or hybrid setup, routine operational tasks like log analysis, incident triage, and alert correlation can eat up a big part of your day. Azure OpenAI Service can act as an operational assistant that processes massive amounts of logs, flags anomalies, and even drafts incident reports for your review.

One engineering team I worked with used it to summarize error patterns across different environments and automatically suggest remediation steps. This cut their mean time to resolution significantly.

If you already run workloads in Azure, integrating this will be easy. It works well with Azure Monitor, Application Insights, and your CI/CD pipelines.

3. DataDog AI for Observability

Observability is one of those areas where AI makes a noticeable difference. DataDog’s AI-driven anomaly detection can catch performance issues before users feel the impact.

In platform engineering, this means the system can automatically identify unusual CPU spikes, slow queries, or memory leaks and alert the right people with context. Instead of receiving a vague alert like “High CPU usage,” your team gets an alert that says, “CPU usage in Service X increased by 70% after the last deployment, likely caused by recent code changes.”

That kind of precision makes troubleshooting faster and avoids the endless back-and-forth during incident calls.

4. Amazon CodeWhisperer for Cross-Cloud Development

Not every platform runs purely on one cloud. I have seen many engineering teams work across AWS, Azure, and GCP. Amazon CodeWhisperer helps developers and platform engineers write code for multiple environments more efficiently.

It can suggest AWS CLI commands, Lambda function code, or even Kubernetes configurations. For teams that handle everything from provisioning infrastructure to integrating microservices, it reduces the friction of switching between different SDKs and APIs.

It is also good at enforcing best practices for security, like recommending secure API key handling or proper encryption settings.

5. Ansible Lightspeed with IBM Watson Code Assistant

For teams that rely on Ansible for automation, Lightspeed is a smart upgrade. It uses IBM Watson’s AI to suggest automation tasks, write playbooks, and even explain what a specific playbook does.

One of the biggest benefits I have seen is onboarding. New engineers often struggle to understand the company’s automation scripts. With Lightspeed, they can get instant explanations, which helps them start contributing sooner.

It also reduces human error in automation scripts, which is critical when you are deploying changes across hundreds of servers.

6. Hugging Face Agents for Custom Platform Tools

Not all AI solutions have to be tied to a commercial vendor. Hugging Face provides open-source models and agents that you can adapt to your own platform needs.

For example, one team built an internal AI assistant that monitored deployment pipelines, ran quick security scans, and answered developers’ questions about API usage. Because it was built on Hugging Face models, it was fully customizable and didn’t lock them into one ecosystem.

This option is ideal if you have specific workflows that commercial AI agents do not address or if you prefer full control over your AI systems.

Final Thoughts

Platform engineering is moving toward a future where AI will be as common as CI/CD pipelines are today. The difference between teams that succeed and those that struggle will come down to how well they blend AI capabilities with engineering expertise.

In my experience, the best AI agents are not there to replace your team. They are there to give your engineers more bandwidth to focus on architecture, innovation, and solving the kinds of problems AI cannot handle yet.

If you are leading a platform team, the real question is not whether to use AI agents but where to start. The six I have listed here are solid options that can deliver value quickly, without overwhelming your team. Start small, integrate carefully, and you will see the results in both performance and morale.

If you need more help, reach out to a platform engineering service provider. Their team of experts and proven experience can help you use these AI agents for platform engineering in a better way.