AI DevOps Troubleshooter
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What do you check first when a deploy goes out and the service will not come up? Everyone has a personal order, learned from whatever broke last. That order is usually fine and occasionally wrong, and when it is wrong you spend forty minutes proving something that was never the problem.
Short answer: AI DevOps Troubleshooter takes a description of what is broken and returns an ordered diagnostic plan, with the commands to run, what each result rules out, and the likely causes ranked.
What is AI DevOps Troubleshooter?
It is a free page for the moment something is failing and you are not sure where to start. You describe the symptom, the environment and what you have already tried. What comes back is a sequence of checks ordered by how much each one narrows the search, not by how obvious it is.
That ordering is the value. A good diagnostic sequence eliminates half the possibilities with the first command. A bad one confirms three things you already suspected and leaves you where you began.
Tip Say what you have already checked and what the result was. Every eliminated cause changes the ordering, and skipping this step means the first three suggestions are things you did ten minutes ago.
Why Use AI DevOps Troubleshooter?
Because troubleshooting under pressure is where experience shows and where it runs out. You are good at the failures you have seen. The one you have not seen is the one taking the site down, and the documentation for it assumes you already know which subsystem to read about.
A structured plan also helps when the problem crosses boundaries. Networking, permissions, container runtime, application configuration and the platform each have their own experts, and the failure sits between two of them. Working through an ordered list is how you find which side it belongs to without pulling four people into a call.
What works well
- Orders checks by how much each one eliminates, not by familiarity.
- Covers the boundaries between subsystems, which is where failures hide.
- Gives you the actual commands, so there is no translation step.
- Free, so working through two hypotheses in parallel costs nothing.
What to watch for
- It cannot see your system, so everything rests on your description.
- Suggested commands can be destructive and need reading before running.
- A confident ranking is still a ranking of guesses.
- Vague symptoms produce a generic plan, which helps nobody.
How Does AI DevOps Troubleshooter Work?
You describe the failure, it builds the plan. Four things make the difference, and a description missing any of them produces a generic list.
| What you supply | How it changes the plan |
|---|---|
| The exact symptom and error text | Points the first check at the right subsystem |
| What changed in the last day | Puts recent changes at the top or removes them entirely |
| What you already ruled out | Deletes whole branches instead of repeating your work |
| The environment and your access | Keeps every command to something you can actually run |
The page is the shared operations shell: a prompt box for the description, a model selector, an options accordion, a result card with a live word count, an export row and the session history panel beneath.
Step-by-Step Guide
- Open AI DevOps Troubleshooter. Free, no account, nothing to install.
- Describe the symptom precisely, including the exact error text and who or what sees it.
- Say what changed in the last day, even if you are confident it is unrelated.
- List what you have already checked and what each check showed.
- Choose a model. OpenAI ChatGPT, Qwen, MSB AI, NVIDIA AI and others are on the selector.
- Set Platform to your stack and Output to Step by Step, then generate.
- Work down the plan, and feed each result back with the reuse button to narrow it.
Before you run anything the plan suggests, check these.
- ✅ You have read the command and know what it changes, if anything.
- ✅ Nothing in the sequence restarts a service you are still collecting evidence from.
- ✅ Read only checks come before anything that alters state.
- ✅ You are on the host or context you think you are on.
- ✅ Someone else knows what you are about to do, if this is production.
- ✅ You are recording what you tried, for the write up afterwards.
Important Read every suggested command before running it, particularly anything that deletes, restarts or reconfigures. A diagnostic plan is a suggestion from something that cannot see your system, and a restart during an incident can destroy the evidence you needed.
Key Features
Checks in eliminating order
The first command is chosen to rule out the most, rather than to confirm the obvious.
Real commands included
Each step comes with something you can run and a note on what each outcome means.
Crosses subsystem boundaries
Network, permissions, runtime and configuration are all considered rather than one at a time.
Causes ranked with reasons
Likely explanations arrive with the evidence that would support or eliminate each.
Narrows as you go
Feed a result back and the next plan is shorter, because the search space shrank.
Advanced Options Guide
Ten controls sit behind the accordion. Output and Platform matter most here, and Custom Instructions is where your constraints belong.
| Option | What it controls | When to change it | Suggested starting point |
|---|---|---|---|
| Platform / Tool | Target across Auto, Docker, Kubernetes, GitHub Actions, GitLab CI, Jenkins, Terraform, Ansible, AWS and Nginx. | Set it so the commands match your environment. | The system the failure is on. |
| Environment | Development, Staging, Production or Multi Environment. | Production makes the plan more cautious about state changing steps. | Production when it is live. |
| Output | Config File, Script, Pipeline, Step by Step or Explained. | Step by Step for the plan, Explained when you want the reasoning first. | Step by Step. |
| Detail | Minimal, Standard, Detailed or Production Grade. | Detailed when the failure crosses several subsystems. | Detailed. |
| Add Comments | Notes on what each command proves or rules out. | Always. It is the difference between a plan and a list. | On. |
| Follow Best Practices | Puts read only checks before anything that changes state. | Leave on, especially in production. | On. |
| Include Security Notes | Flags where a symptom could be a security issue rather than a fault. | Any public facing service behaving strangely. | On. |
| Add Examples | Shows sample output for each check so you can compare. | When you are unfamiliar with the command being suggested. | On. |
| Detail Level | Slider from 1 to 100 for how much explanation accompanies each step. | High when learning the system, low when you want the commands only. | High. |
| Custom Instructions | Free text up to 1000 characters for constraints and context. | Access you do not have, changes you are not allowed to make, tooling available. | A concrete line such as "read only access to production, cannot restart anything, kubectl and logs only". |
Example Inputs
A precise symptom produces a precise plan. Compare a vague description with this.
Kubernetes 1.29, three replicas of a Node API. After a deploy this morning
two pods are Running and one is CrashLoopBackOff. The failing pod logs
"listen EADDRINUSE :::8080" then exits. The image is the same one running
on the two healthy pods. Nothing changed in the manifests. I have already
checked resource limits and node capacity, both fine. I have read only
access and cannot restart anything without approval.Every clause narrows the search. The same image on healthy pods eliminates the build. The specific error points at the process rather than the platform. The already checked list removes two branches. And the access constraint means the plan stays within what you can actually run.
Example Outputs
With Output on Step by Step and Detail on Detailed, the plan arrives ordered and annotated.
1. Confirm it is always the same node
kubectl get pod -o wide -l app=checkout-api
If the failing pod is always on one node, the cause is node local.
If it moves, the cause travels with the pod.
2. Check for a leftover process holding the port
kubectl debug node/NODE -it --image=busybox -- netstat -tlnp | grep 8080
A process outside the pod holding 8080 explains EADDRINUSE with an
identical image.
3. Look at the container restart history
kubectl describe pod POD | sed -n '/Last State/,/Exit Code/p'
A previous container that has not fully exited can hold the port
during a fast restart loop.
4. Check whether hostPort is set
kubectl get deploy checkout-api -o yaml | grep -A2 hostPort
A hostPort binding means only one pod per node can start, which
matches two healthy and one failing exactly.Notice that step four is the likely answer and it is placed last on purpose. The first three are cheap, read only and narrow the field. If step one shows the failure is tied to a node, step four becomes obvious. A plan that led with the clever guess would have been right this time and unhelpful as a habit.
Pro tip Paste the output of each check back in before running the next one. The plan reorders itself around what you now know, and by the third round it is usually down to one hypothesis.
Comparison Table
| Approach | Speed | Coverage | Best for |
|---|---|---|---|
| Checking what broke last time | Instant | Only failures you have seen | Recurring problems |
| Searching the error text | Fast | Other people's contexts | Well known errors |
| Asking a colleague | Depends who is free | Their experience of your system | Problems tied to local history |
| AI DevOps Troubleshooter | Seconds | Ordered across subsystems | Unfamiliar failures and boundary cases |
Once the cause is found and the service is back, the write up is the step that stops it recurring, and AI Incident Postmortem Writer is built for exactly that.
AIToolsay works by giving each job a page that already knows the shape of the problem, so this prompt box expects a symptom description rather than an open question. The options carry a platform selector that changes the commands you get and an output setting that decides whether you receive a plan or an explanation, and the model selector lets a second engine propose a different order when the first plan does not fit. It is free with no account step, which matters when you want three attempts in ten minutes. Session history keeps each plan under the result as the investigation narrows. The rest of the operations tooling on AIToolsay follows the same pattern, so log analysis, monitoring and the postmortem are each a page away.
Frequently Asked Questions
Is AI DevOps Troubleshooter free?
Yes, with no account and no limit on how many problems you work through.
Does it connect to my systems?
No. It reasons from your description and gives you commands to run yourself, which means nothing is executed on your behalf.
How much detail should I give?
The exact error text, what changed recently, and what you have already ruled out. Those three make more difference than anything else you could add.
Are the suggested commands safe to run?
Read each one first. With Follow Best Practices on, read only checks come before state changing ones, but you are the only one who knows what is safe on your systems right now.
Can I use it if I have limited access?
Yes, and you should say so. Putting your access constraints in Custom Instructions keeps the plan to steps you can actually perform.
What if the first plan does not find it?
Feed the results back in. Each round eliminates branches, and a plan built on four known results is much sharper than the first one.
Next time something fails in a way you do not recognise, write the symptom down properly before you start clicking. That description is most of the work, and having it makes both the plan and the later write up better. The Telegram community is a reasonable place to compare diagnostic habits, and the newsletter or push notifications will tell you when new operations tools land here.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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