AI-Assisted Test Debugging

Find the root cause of test failures in minutes, not hours.

The problem

We all know the drill when a test fails: open up the CI logs, scroll endlessly, check pod status, compare with the last passing run, re-run to see if it happens again, and repeat. It’s a time sink we’ve all felt.

If you’re a platform engineer or part of a QA team juggling hundreds of automated tests running in your CI/CD pipelines, you know how quickly this debugging tax adds up. A single failing test can pull you into an hour of log-diving, and a subtle regression buried across multiple services can derail an entire afternoon.

And it’s only getting tougher. With AI-generated code speeding up development, we’re seeing more changes, more test runs, and more failures to sort through. If we keep debugging the old way, it quickly becomes the bottleneck that slows everything else down.

Why existing approaches fall short

Most teams are stuck piecing together scattered data between logs in CI, pod events in the cluster, test output in the runner, and artifacts who-knows-where. There's no single place to ask the obvious questions:

• What changed between the last passing run and this failure?
• Is this failure coming from the code change, or from flaky infrastructure underneath it?
• Which service or config update caused this?

Generic AI tools just don’t cut it, they don’t know your test history, your Kubernetes setup, or your execution details. So you end up copying and pasting logs into a chat window, crossing your fingers for something useful.

AI can speed up debugging, but only if it has the right context with centralized, structured test observability.

A better approach

Debugging gets a whole lot faster when AI can see everything you see and then some. Here’s what that looks like:

• Full run history for every test workflow
• Logs, artifacts, and metadata captured automatically
• Side-by-side comparison of passing and failing runs
• Pattern detection across executions over time

With this foundation in place, AI isn’t just guessing anymore, it’s pointing straight to the evidence. Whether it’s a specific log line, a config tweak, or a service update, you get clear answers instead of more searching.

How Testkube enables it

Testkube runs your tests natively inside Kubernetes and captures everything in one place. All your run history, logs, artifacts, timing, and resource usage are unified, giving AI the foundation it needs to help you debug faster.

1. AI Assistant for root cause analysis

Ask natural questions about any failing workflow:

• "Why did this start failing yesterday?"
• "What's different between run 47 and run 52?"
• “Compare the latest runs across us-east, us-west and us-central”
• "Is this failure related to the API timeout we saw last week?"

The AI Assistant digs into your execution history, compares runs, and gives you evidence-based answers. It highlights exactly what changed so you know where to look first.

2. Run-to-run comparison

You can visually compare logs from any two runs to see what changed in regard to status and error messages, helping you quickly drill down to differences that matter..

3. Centralized logs and artifacts

Because Testkube executes inside Kubernetes, all test output is captured automatically. No more chasing logs across CI nodes and cluster namespaces.

4. MCP integration for IDE-based debugging

If your team uses MCP with AI-enabled IDEs and local AI agents, debugging is a closed loop. Review the failure in Testkube, fix it in your IDE, generate a targeted regression test, and push it back to the cluster. You can validate the fix without ever leaving your workflow.

Example

Let’s say a critical end-to-end workflow starts failing in staging. Instead of the usual log hunt, here’s how it plays out:

1. Open the failing run in Testkube
2. Ask the AI Assistant: "What changed since the last passing run?"
3. The Assistant identifies that a downstream service updated its image tag and started returning 500 errors on a specific endpoint
4. You fix the service configuration and push the change
5. Testkube re-runs the workflow and confirms the fix

All in, you’re done in five minutes instead of two hours.

Who this is for

• Platform and DevOps engineers who own test infrastructure and want faster incident response across clusters.
• QA and SDET teams managing large test suites who are tired of spending all their time triaging failures instead of improving coverage.
• Developers who want clear, actionable explanations when a build goes red instead of cryptic logs and endless guesswork.

Why it matters now more than ever

AI is speeding up how fast we write code, but testing infrastructure needs to keep up. If every new feature brings a wave of failures that take hours to debug, all those velocity gains go out the window.

Testkube gives AI the observability layer it needs to make debugging fast. Engineers get answers in minutes, not hours. Teams can ship with confidence instead of crossing their fingers.

Get started

Want to see AI-assisted debugging in action? Book a demo and we’ll walk through it together using your own test scenarios. If you’re still exploring, check out the docs to see how the AI Assistant and MCP integration work behind the scenes.

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