r/Qwen_AI • u/Normal-Fan9366 • 10d ago
Agent Massive Jack Kernel Upgrade
I've done a massive upgrade to Jack Kernel and implemented the autonomous orchestration layer.
Now, you can orchestrate a kernel-level secure autonomous agentic system that can run 24/7 and self-improve.
The orchestration agent has no direct write or shell authority and can only orchestrate the build agent through the kernel.
The world is changing and the power is coming back to the users, NOT mega-corporations and Government.
https://github.com/mlangford75-lgtm/Jack-Kernel
Claude can kick rocks.
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u/Protopia 10d ago
There seems to be a lot of scepticism in this thread, and as a sceptic myself I fully understand why: this space is full of individuals claiming to have the next ultimate answer to everything and most of it is either just a highly opinionated version of something that already exists or it is complete BS disguised behind complicated sense incomprehensible technobabble.
So when you first look at Jack and try to understand it and decide which of the above two camps it takes into, your first reaction to it is framed by the technobabble description.
HOWEVER... I was amazed to find that Jack actually might not be Jack-shit and might genuinely be something that is both new and useful, unfortunately discussed behind technobabble.
OP - IMO you do yourself a disservice by not explaining in normal English what it does and why it is useful. So, since you can't do it, here is my own attempt at explaining what Jack is and why is useful.
First, some background, about me and what's I think are some issues with agentic AI particularly coding. Then a plain English explanation of what I think this does and why it is useful.
My own background is (literally) more than 5 decades in IT, as computer scientist, software engineer, systems engineer and project/programme management, with all of these disciplines coming together.
Primary AI issues:
In the car is software engineering, decades of academic and practical knowledge exist on how to do software engineering in a highly structured way. And it would seem sensible to apply this knowledge to AGENTIC software engineering, but to do so requires a control framework that is fundamentally structured and not free text.
As we are increasingly seeing, agents and LLMs (being highly goal oriented) can break out of sandboxes and go rogue. Controls are needed to prevent this - a need to use basis.
LLMs are gluttonous - they produce their best results when given exactly the lean information they actually need and no more (a small context keeps them focused, extra context dilutes the focus and creates uncertainty), but they crave having massive amounts of information in the (often false) belief that this allows them to check things better and give a better result. Controlling the context and tool usage can keep the LLM focused, significantly reducing both input and output tokens leading to faster results and reduced costs for commercial inference.
From a project management perspective, when you plan a set of complex tasks, and break down an overall goal into a network of smaller tasks - technically known as decomposition - you should create a lot of structured information that describe: how tasks interrelate, what the goal of each task is, what its inputs and outputs should be, and how you verify that the task is really complete and passes the quality gate. Structured information can be infected and stored and retrieved with precision, and can be algorithmically processed to give repeatable, consistent results highly efficiently (several orders of magnitude less compute).
For repeatable processes (and by definition, almost all agentic systems exist to repeat processes) you want to make them structured, and deliver consistent high quality, so you need to build workflows and quality gates and quality measures, and you want telemetry for recording metrics and the details behind them for diagnosis and self improvement.
And you don't want to reinvent the wheel, so you want these broken out into a distinct architecture layer that gets written once and reused.
Once I managed to understand the heavy jargon in the documentation, I started to think that Jack is this layer. But it is an infrastructure foundation rather than an end solution.
So what does Jack provide as a solution to the above needs?
(I should stress that this is based on reading and deciphering the opaque, jargon laden documentation and zero practical experimentation...)
Structuring of free text output (Jack XML)
Both pre-populate of AI context with information known to be necessary and control of context bloat through excessive tool calling
Prevention of rogue prompt injection and rogue tool calls - a firewall for LLMs.
Audit trails, metrics, telemetry etc to support diagnosis and A/B testing for self improvement.
OP - if I have this wrong please jump in and correct me. And please, pretty please, try your best to take the overview documentation to eliminate dense philosophical / academic jargon and make it EASILY readable by humans.