GenAI-Powered Systemic Bug Evaluation and Management Assistant
Motivation
What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?
Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?
Description
To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.
Goals
Set up a Python environment
Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).
Build the Dialogue Loop
- Write a basic Python script using the Gemini API.
- Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation
Socratic/Systemic Strategy Implementation
- Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
- Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
- Implement Bugzillla call to collect the
- Implement Questioning Framework as LLVM pre-conditioning
- Define set of instructions
- Assemble the Tool
Resources
What are Systemic Questions?
Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.
Gitlab Project
This project is part of:
Hack Week 25
Activity
Comments
-
3 days ago by rtsvetkov | Reply
=== 1 Circular Questions Focus on feedback loops and mutual influence. Example debugging prompts: - "What components influence this module, and what does this module influence in return?" - "If Service A slows down, how does Service B respond?"
=== 2 Difference Questions Explore variations, exceptions, or changes over time. Example debugging prompts: - "When does the bug not occur? What is different then?" - "What changed in the system right before the issue appeared?"
=== 3 Scaling Questions Quantify experience, severity, or uncertainty. Example debugging prompts: - "On a scale from 1–10, how reproducible is this issue?" - "How much worse does the system behave under peak load versus normal load?"
=== 4 Hypothetical (‘If…Then’) Questions Explore consequences, alternative actions, or simulated scenarios. Example debugging prompts: - "If we disable caching, what do we expect to happen?" - "If the input doubles, which component fails first?" - "If had a unlimited time to prevent this exact bug from ever happening again, where in our development cycle (e.g., design, code review, testing) would we invest the most effort?" - "If we had to ship the next feature without fixing this bug, what workarounds or manual steps would we need to put in place?"
=== 5 Resource / Strength Questions Identify what works well and what can be reused. Example debugging prompts: - "Which environments run without this problem and why?" - "What parts of the system are stable and can guide the fix?"
=== 6 Perspective-Shifting Questions Examine the situation through different roles or components. Example debugging prompts: - "If you were the database, what would you ‘say’ is overwhelming you?" - "How would a network engineer interpret these logs differently from a backend developer?"
== 2. Example Debugging Process Using Systemic Questions
=== Step 1: Clarify the Pattern - "When exactly does the API fail, and when does it succeed?"
=== Step 2: Identify Boundaries - "Which systems are definitely not involved?"
=== Step 3: Explore Changes - "What recent deployments or config changes might correlate?"
=== Step 4: Map Influences - "How does the latency of Service X influence the behaviour of Service Y?"
=== Step 5: Hypothesis Testing - "If we simulate traffic spikes, does the behaviour match production incidents?"
=== Step 6: Leverage What Works - "Why does staging not show the issue? What can this teach us about production?"
== 3. Key Benefits for IT and Systems Theory * Makes hidden dependencies visible
* Avoids tunnel vision in debugging
* Encourages team alignment through shared system understanding
* Supports root-cause analysis rather than symptom chasing -
2 days ago by rtsvetkov | Reply
Example manual research session: https://docs.google.com/document/d/1kgM0lBVavBnN0VeP1OgssWVjwIdE2hGf3jHmi2rxc/edit?usp=sharing
as also the additional transaction on https://bugzilla.suse.com/show_bug.cgi?id=1245907
the session https://gemini.google.com/app/dd379133b4af2ec8?utmsource=applauncher&utmmedium=owned&utmcampaign=base_all
-
-
-
-
-
Reply to rtsvetkov
Adding a port for DeepSeek 
# A First Level Header ## A Second Level Header Use one asterisk to *emphasize* Use two asterisks for **strong emphasis** - Use hyphens - for unordereed - lists This is an [link to example.com](http://example.com/) This is an image  This is a user link @hans This is a project link hw#some-cool-title
Similar Projects
SUSE Edge Image Builder MCP by eminguez
Description
Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.
Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.
Goals
- Familiarize myself with MCPs
- Unrealistic: Have an MCP that can generate an EIB config file
Resources
Result
https://github.com/e-minguez/eib-mcp
I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.
I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)
Example:
Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy
a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as:
* hostname: node1, IP: 1.1.1.1, role: initializer
* hostname: node2, IP: 1.1.1.2, role: agent
* hostname: node3, IP: 1.1.1.3, role: agent
The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user
called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4
Generates:
``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack
Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse
Description
For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.
I might use qwen3-coder or something similar as a starting point.
Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).
Goals
- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
- Be flexible to pivot in any direction, as long as there are new things learned.
Resources
To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
- Add a Web UI that works from another computer even though the container sees network is down
Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
- Battle with OpenCode that was both slow and kept on piling up broken things.
- Call it success as after all the agentic AI was working locally.
- Clean up the mess left behind a bit.
Blog Post
Summarized the findings at blog post.
Background Coding Agent by mmanno
Description
I had only bad experiences with AI one-shots. However, monitoring agent work closely and interfering often did result in productivity gains.
Now, other companies are using agents in pipelines. That makes sense to me, just like CI, we want to offload work to pipelines: Our engineering teams are consistently slowed down by "toil": low-impact, repetitive maintenance tasks. A simple linter rule change, a dependency bump, rebasing patch-sets on top of newer releases or API deprecation requires dozens of manual PRs, draining time from feature development.
So far we have been writing deterministic, script-based automation for these tasks. And it turns out to be a common trap. These scripts are brittle, complex, and become a massive maintenance burden themselves.
Can we make prompts and workflows smart enough to succeed at background coding?
Goals
We will build a platform that allows engineers to execute complex code transformations using prompts.
By automating this toil, we accelerate large-scale migrations and allow teams to focus on high-value work.
Our platform will consist of three main components:
- "Change" Definition: Engineers will define a transformation as a simple, declarative manifest:
- The target repositories.
- A wrapper to run a "coding agent", e.g., "gemini-cli".
- The task as a natural language prompt.
- The target repositories.
- "Change" Management Service: A central service that orchestrates the jobs. It will receive Change definitions and be responsible for the job lifecycle.
- Execution Runners: We could use existing sandboxed CI runners (like GitHub/GitLab runners) to execute each job or spawn a container.
MVP
- Define the Change manifest format.
- Build the core Management Service that can accept and queue a Change.
- Connect management service and runners, dynamically dispatch jobs to runners.
- Create a basic runner script that can run a hard-coded prompt against a test repo and open a PR.
Stretch Goals:
- Multi-layered approach, Workflow Agents trigger Coding Agents:
- Workflow Agent: Gather information about the task interactively from the user.
- Coding Agent: Once the interactive agent has refined the task into a clear prompt, it hands this prompt off to the "coding agent." This background agent is responsible for executing the task and producing the actual pull request.
- Workflow Agent: Gather information about the task interactively from the user.
- Use MCP:
- Workflow Agent gathers context information from Slack, Github, etc.
- Workflow Agent triggers a Coding Agent.
- Workflow Agent gathers context information from Slack, Github, etc.
- Create a "Standard Task" library with reliable prompts.
- Rebasing rancher-monitoring to a new version of kube-prom-stack
- Update charts to use new images
- Apply changes to comply with a new linter
- Bump complex Go dependencies, like k8s modules
- Backport pull requests to other branches
- Rebasing rancher-monitoring to a new version of kube-prom-stack
- Add “review agents” that review the generated PR.
See also
SUSE Observability MCP server by drutigliano
Description
The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.
This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.
Goals
- Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
- Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
- Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
- Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.
Hackweek STEP
- Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.
Scope
- Implement read-only MCP server that can:
- Connect to a live SUSE Observability instance and authenticate (with API token)
- Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
- Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
- Return the data as a structured JSON payload compliant with the MCP specification.
Deliverables
- MCP Server v0.1 A running Golang MCP server with at least one tool.
- A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.
Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.
Resources
- https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
- https://www.datadoghq.com/blog/datadog-remote-mcp-server
- https://modelcontextprotocol.io/specification/2025-06-18/index
- https://modelcontextprotocol.io/docs/develop/build-server
Basic implementation
- https://github.com/drutigliano19/suse-observability-mcp-server
Results
Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.
Example execution
Local AI assistant with optional integrations and mobile companion by livdywan
Description
Setup a local AI assistant for research, brainstorming and proof reading. Look into SurfSense, Open WebUI and possibly alternatives. Explore integration with services like openQA. There should be no cloud dependencies. Mobile phone support or an additional companion app would be a bonus. The goal is not to develop everything from scratch.
User Story
- Allison Average wants a one-click local AI assistent on their openSUSE laptop.
- Ash Awesome wants AI on their phone without an expensive subscription.
Goals
- Evaluate a local SurfSense setup for day to day productivity
- Test opencode for vibe coding and tool calling
Timeline
Day 1
- Took a look at SurfSense and started setting up a local instance.
- Unfortunately the container setup did not work well. Tho this was a great opportunity to learn some new podman commands and refresh my memory on how to recover a corrupted btrfs filesystem.
Day 2
- Due to its sheer size and complexity SurfSense seems to have triggered btrfs fragmentation. Naturally this was not visible in any podman-related errors or in the journal. So this took up much of my second day.
Day 3
- Trying out opencode with Qwen3-Coder and Qwen2.5-Coder.
Day 4
- Context size is a thing, and models are not equally usable for vibe coding.
- Through arduous browsing for ollama models I did find some like
myaniu/qwen2.5-1m:7bwith 1m but even then it is not obvious if they are meant for tool calls.
Day 5
- Whilst trying to make opencode usable I discovered ramalama which worked instantly and very well.
Outcomes
surfsense
I could not easily set this up completely. Maybe in part due to my filesystem issues. Was expecting this to be less of an effort.
opencode
Installing opencode and ollama in my distrobox container along with the following configs worked for me.
When preparing a new project from scratch it is a good idea to start out with a template.
opencode.json
``` {
Try out Neovim Plugins supporting AI Providers by enavarro_suse
Description
Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.
Goals
Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.
Resources
- Neovim 0.11.5
- AI-enabled Neovim plugins:
- avante.nvim: https://github.com/yetone/avante.nvim
- Gp.nvim: https://github.com/Robitx/gp.nvim
- parrot.nvim: https://github.com/frankroeder/parrot.nvim
- gemini.nvim: https://dotfyle.com/plugins/kiddos/gemini.nvim
- ...
- Accounts or API keys for AI model providers.
- Local model serving setup (e.g., Ollama)
- Test projects or codebases for practical evaluation:
- OBS: https://build.opensuse.org/
- OBS blog and landing page: https://openbuildservice.org/
- ...
Bugzilla goes AI - Phase 1 by nwalter
Description
This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.
Goals
To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.
Project Charter
https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.
https://docs.google.com/document/d/14gtG9-ZvVpBgkh33Z4AM6iLFWqZcicQPD41MM-Pg0/edit?usp=sharing
issuefs: FUSE filesystem representing issues (e.g. JIRA) for the use with AI agents code-assistants by llansky3
Description
Creating a FUSE filesystem (issuefs) that mounts issues from various ticketing systems (Github, Jira, Bugzilla, Redmine) as files to your local file system.
And why this is good idea?
- User can use favorite command line tools to view and search the tickets from various sources
- User can use AI agents capabilities from your favorite IDE or cli to ask question about the issues, project or functionality while providing relevant tickets as context without extra work.
- User can use it during development of the new features when you let the AI agent to jump start the solution. The issuefs will give the AI agent the context (AI agents just read few more files) about the bug or requested features. No need for copying and pasting issues to user prompt or by using extra MCP tools to access the issues. These you can still do but this approach is on purpose different.
Goals
- Add Github issue support
- Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
- Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
- Clean-up and test the implementation and create some documentation
- Create a blog post about this approach
Resources
There is a prototype implementation here. This currently sort of works with JIRA only.