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.
    
• "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:
  
  1. Workflow Agent: Gather information about the task interactively from the user.
     
  2. 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.
     
• Use MCP:
  
  1. Workflow Agent gathers context information from Slack, Github, etc.
     
  2. Workflow Agent triggers a Coding Agent.
     
• Create a "Standard Task" library with reliable prompts.
  1. Rebasing rancher-monitoring to a new version of kube-prom-stack
     
  2. Update charts to use new images
     
  3. Apply changes to comply with a new linter
     
  4. Bump complex Go dependencies, like k8s modules
     
  5. Backport pull requests to other branches
• Add “review agents” that review the generated PR.
  

See also

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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

• https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema
• Anything else :)

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

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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

1. Learn something about RAG, LLM, AI.
2. Find out if everything works offline as intended.
3. 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.
4. 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.

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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

Bugzilla goes AI Phase 1

Description

Project Achievements during Hackweek

In this file you can read about what we achieved during Hackweek.

Project Achievements

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Docs Navigator MCP: SUSE Edition by mackenzie.techdocs

Description

Docs Navigator MCP: SUSE Edition is an AI-powered documentation navigator that makes finding information across SUSE, Rancher, K3s, and RKE2 documentation effortless. Built as a Model Context Protocol (MCP) server, it enables semantic search, intelligent Q&A, and documentation summarization using 100% open-source AI models (no API keys required!). The project also allows you to bring your own keys from Anthropic and Open AI for parallel processing.

Goals

• [ X ] Build functional MCP server with documentation tools
• [ X ] Implement semantic search with vector embeddings
• [ X ] Create user-friendly web interface
• [ X ] Optimize indexing performance (parallel processing)
• [ X ] Add SUSE branding and polish UX
• [ X ] Stretch Goal: Add more documentation sources
• [ X ] Stretch Goal: Implement document change detection for auto-updates

Coming Soon!

• Community Feedback: Test with real users and gather improvement suggestions

Resources

• Repository: Docs Navigator MCP: SUSE Edition GitHub
• UI Demo: Live UI Demo of Docs Navigator MCP: SUSE Edition

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Enable more features in mcp-server-uyuni by j_renner

Description

I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

• System operations and infos
• System groups
• Maintenance windows
• Ansible
• Reporting
• ...

At the end of the week I managed to enable basic system group operations:

• List all system groups visible to the user
• Create new system groups
• List systems assigned to a group
• Add and remove systems from groups

Goals

• Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
• Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
• Create a PR to the repo [DONE]

Resources

• MCP Server Uyuni
• MLM API Docs

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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

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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

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MCP Perl SDK by kraih

Description

We've been using the MCP Perl SDK to connect openQA with AI. And while the basics are working pretty well, the SDK is not fully spec compliant yet. So let's change that!

Goals

• Support for Resources
• All response types (Audio, Resource Links, Embedded Resources...)
• Tool/Prompt/Resource update notifications
• Dynamic Tool/Prompt/Resource lists
• New authentication mechanisms

Resources

• https://github.com/mojolicious/mojo-mcp
• https://modelcontextprotocol.io/specification/2025-06-18

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Uyuni Health-check Grafana AI Troubleshooter by ygutierrez

Description

This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

Goals

• Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
• Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
• Evaluate support for local LLMs and external APIs through the plugin.
• Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

Resources

Grafana LMM plug-in

Uyuni Health-check

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MCP Trace Suite by r1chard-lyu

Description

This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.

The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.

• Repo: https://github.com/r1chard-lyu/systracesuite
• Demo: Slides

Goals

1. Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.

2. Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.

Resources

• Gemini CLI: https://geminicli.com/
• eBPF: https://ebpf.io/
• bpftrace: https://github.com/bpftrace/bpftrace/
• perf: https://perfwiki.github.io/main/
• ftrace: https://github.com/r1chard-lyu/tracium/

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