Meet SteadyBuddy: Your AI companion for reliability testing.

Building reliable systems requires more than monitoring production. You need to continuously validate how your applications behave under real-world conditions, uncover weaknesses before users do, and verify that resilience improvements actually work.

The challenge is knowing what to test, how to build meaningful experiments, and how to interpret the results.

Today, we’re excited to introduce SteadyBuddy, Steadybit’s AI-powered assistant that helps you design, execute, and understand reliability tests using natural language.

Instead of navigating experiment builders or searching through previous runs, simply tell SteadyBuddy what you want to validate. It can help you create tailored reliability experiments, explain results, and recommend your next tests, all while respecting your existing permissions and keeping you in control. Want to listen, watch, and learn? Then check out this video, or read on if you prefer. 

From Questions to Reliability Tests in Seconds

SteadyBuddy brings conversational AI directly into the Steadybit platform.

Want to validate how your checkout service behaves when a Kubernetes pod disappears? Just ask:

“Test how my checkout service behaves when one of its pods is killed.”

SteadyBuddy understands your request, asks clarifying questions when necessary, and generates a complete experiment draft tailored to your selected environment. From there, you can review and refine it in the experiment editor or run it immediately.

No hunting through actions. No manually assembling experiments. Just describe what you want to learn about your system.

Know What to Test Next

One of the biggest challenges in reliability engineering isn’t running tests. It’s deciding which scenarios provide the most value.

SteadyBuddy analyzes the targets available in your selected environment and recommends reliability tests that are relevant to your infrastructure. Rather than generic suggestions, you receive ready-to-run experiment ideas based on the services, workloads, and resources that actually exist in your environment.

Whether you’re onboarding a new application, validating a production environment, or expanding your reliability program, SteadyBuddy helps identify meaningful opportunities to improve resilience.

Understand Results Faster

Running a reliability test is only half the story.

After an experiment completes, SteadyBuddy can help explain what happened by analyzing the actual execution results.

Instead of digging through logs and timelines, simply ask questions like:

“Why did my last experiment fail?”

SteadyBuddy reviews the relevant experiment run, identifies what happened during execution, and explains the outcome using the available run data. The answers are grounded in the experiment’s actual results, making it easier to understand failures, identify weaknesses, and determine your next steps.

Built with Security and Control in Mind

Introducing AI into engineering workflows requires trust.

That’s why SteadyBuddy operates within the exact same security boundaries as the rest of the Steadybit platform.

SteadyBuddy only has access to the information you’re already allowed to see, including your team’s environments, available actions, reliability advice, experiment history, and the resources you have permission to access.

Just as importantly, SteadyBuddy cannot perform destructive operations on its own.

Today, it operates in a read-only capacity. It can analyze information, answer questions, and prepare experiment drafts, but every action that changes your environment requires explicit human approval. Creating, modifying, or running experiments always remains under your control.

As SteadyBuddy evolves, we’ll continue introducing additional safeguards before enabling more advanced capabilities.

Flexible Deployment for Every Environment

Whether you use Steadybit SaaS or run Steadybit on-premises, SteadyBuddy fits naturally into your deployment model.

For SaaS customers, SteadyBuddy is available as an opt-in feature that can be enabled by a tenant administrator. AI requests are processed using Anthropic models, and customer data is never used to train foundation models.

For self-hosted deployments, you stay fully in control. SteadyBuddy connects to your own configured AI provider, ensuring that data remains within your infrastructure rather than passing through any Steadybit-hosted AI service.

In both deployment models, SteadyBuddy only sends the context necessary to answer your request and never transmits credentials or secrets.

AI That Understands Your Reliability Platform

Unlike general-purpose AI assistants, SteadyBuddy understands your reliability testing workflows.

It knows about your environments, available actions, target types, experiment history, and reliability advice. That context allows it to provide recommendations and explanations that are specific to your systems, instead of generic guidance that still requires significant manual work.

The result is an assistant that helps experienced reliability engineers move faster while making reliability testing more accessible to platform, DevOps, and application teams.

Available Today

SteadyBuddy is available now for eligible Steadybit customers. 

Please complete this form, and we will get you enabled. Once we get you rocking and rolling, you can:

• Generate tailored reliability test ideas for any environment.
• Create complete experiment drafts using natural language.
• Analyze experiment runs and understand failures faster.
• Explore new ways to validate system resilience without manually building every experiment.

We’re excited to see how SteadyBuddy helps teams spend less time configuring experiments and more time improving system reliability.

The future of reliability testing isn’t replacing engineers. It’s giving them a faster, more intuitive way to validate resilient systems with confidence. Read further with our documentation.