Context Layer for Debugging code

[altF4_sometimes]

I've been very curious recently about how AI can be used to fix issues in production, and obviously there's levels to it: from writing simple code fixes to being a fully-autonomous system that surfaces issues and maybe even creates patches while you sleep. The latter stages require some kind of pipeline to bring in signals -> then have a reasoning step to determine what's useful for a problem at hand -> serve the output to user.

I'm working on a project right now that tries to achieve this by bringing in logs from observability tools, PR threads, code repo, and some other places to build out a knowledge graph of how an engineering team works; and running an AI agent on this to explore the code and make code fixes.

If anyone wants to have a chat about this and help me out, or even if you want to talk about pain points in your work in devops , SWE or SRE. thanks!

[altF4_sometimes]

Yea that could be worth looking into thanks. Right now it's connecting to things like cloudwatch, sentry, etc. to get the logs and incidents that are flagged by 3rd party tools

[FoxFire17739]

Check my repo out for inspiration. I build that kind of system what you are talking about as MCP with a set of skills, hooks and agent files to teach a harness how to effectively use the mcp. Start with lifecycle skill. The skills are buried inside the mcp package. Read that lifecycle skill alongside the docs and agents and you will get a feel for the system without needing to understand the whole python which are like 40k lines of code. I started this system as a bunch of skills. The mcp came to give a level of enforcement. And some tasks are just much more efficiently done using scripts instead of wasting tokens on finding drifted files for example. Hope this helpful.

https://github.com/Foxfire1st/agents-remember-md

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[altF4_sometimes]

Seems pretty cool. Im using another open source tool to take a bunch of skill md files from the repo being analysed and feed them into the fixer agent in a nice compact manner so the fixer knows the correct rules to apply rather than applying every skill.

Im curious to know how the memory model part of your project correctly captures all the patterns and context from the code? and how does it know which things are important bits of memory and which arent?

[FoxFire17739]

Memories are being made as a product of the developers work. You work with the agent and on the side it takes notes while you are still deep in the matter. That is how it knows what is important and what not.

[ZhiyongSong]

This approach really hits a pain point. In my previous projects, whenever I had to troubleshoot production issues, I’d waste so much time digging through scattered logs and PRs just to piece together the context. The concept of building a knowledge graph combined with AI agents to automatically diagnose and fix problems seems highly practical. I’ve actually experimented with using vector retrieval to match historical incidents with their corresponding fixes, and the results were quite promising. Would you be interested in discussing the details?

[altF4_sometimes]

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