MuhammadYossry

AgentSWEOS vNext

A Phase-Oriented Software Engineering Runtime for LLM Coding Agents

"Human teams compensate for incomplete docs with meetings, intuition, and shared mental models. LLM systems cannot. Therefore every artifact is a coordination layer, and every phase is a contract."

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The Core Premise

MuhammadYossry

The Minimal Agent Specification (MAS)

One File. Few Tokens. Any Agent.

While working on AgentOS I came across this problem: You have 20 specialist agents. Your orchestrator needs to know: Who are they? What can they do? Where's their state?

You don't want to load 20 full personas into memory. You don't want to parse 20,000 tokens of backstory just to route a simple task.

You want a business card. A tiny header file that tells you everything you need before you decide to have a conversation.

MuhammadYossry

Problem-Solving Techniques Inside Neural Networks: A Beginner’s Guide for ML Practitioners

Machine learning often looks like magic from the outside. You feed data into a model, train it long enough, and somehow it begins recognizing images, translating languages, or predicting outcomes.

But under the surface, neural networks are not magic at all.

They are built on classic problem-solving techniques that computer scientists and mathematicians have used for decades. What changed is that these strategies were combined, scaled, and optimized to work with large data and modern hardware.

For a beginner ML practitioner, understanding these techniques is more valuable than memorizing frameworks. Tools change. Principles remain.

MuhammadYossry

Why Problem Solving Matters for Agentic Engineering

Problem-solving strategies are the backbone of agentic engineering because an agentic system is fundamentally a system that must repeatedly:

1. understand goals
2. decompose ambiguity
3. choose actions
4. adapt from feedback
5. coordinate tools / subagents
6. improve over time

MuhammadYossry

AccountantAgentOS — Custom AgentOS Runtime Blueprint

Personal Tax Intelligence & Year-Round Accounting Assistant

Design philosophy: Useful over impressive. Inspectable over magical. Narrow scope. Tool-backed evidence. Human in the loop for every consequential output.

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Agent Configuration

MuhammadYossry

Example: AgentResearchOS — Custom Runtime Design

Prompt template instantiation for: Agent-Native Research Artifact (ARA) system
Paradigm: ARA-first, paper-as-compiled-view, failure-trace as first-class memory
Key constraints: Docker-sandboxed execution · User-supplied model · Zero documentation burden

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Template Variable Decisions

MuhammadYossry

Example: AgentSWEOS Blueprint

Production-Grade Software Engineering Agent Runtime

Design philosophy: AgentSWEOS is not a general-purpose coding assistant. It is a disciplined SWE process modeled as a containerized agent — one that applies the same structured workflows a senior engineer brings to every task, learns what actually works, and forgets what doesn't.

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MuhammadYossry

Part 4: Containerizing AgentOS — Build, Customize, and Deploy a Self-Learning World-Modeling Agent

Draft version (Revised Architecture)

"Make it reproducible, or it never happened."

Prerequisites:

• Part 0: Agent Bootstrapping Kit

MuhammadYossry

Part 4: Containerizing AgentOS — Build, Customize, and Deploy Your Self‑Learning Agent

• Draft version

"Make it reproducible, or it never happened."

Prerequisites:

• Part 0: Agent Bootstrapping Kit
• Part 1: Agent Filesystem
• Part 2: Inter‑Agent Communication
• Part 3: Git as Agent Memory

MuhammadYossry

LLM Agent Filesystem — Part 2: Inter-Agent Communication

“The power of UNIX comes from the ability of programs to work together.” — Brian Kernighan

“The power of an agent system comes from agents that compose without coupling.” — adapted for LLM multi-agent systems

Prerequisite: This article extends Part 1: LLM Agent Filesystem. All file ownership rules, frontmatter conventions, budgets, and pruning policies from Part 1 still apply.