AI Architecture AI Agent
Design notes for MyInvestPilot — an agent-friendly investment workflow system built with strategy primitives, DAG engines, validation loops, and observable decision infrastructure.
AI Architecture is an open-source ai agent by myinvestpilot, with 331 GitHub stars and the MIT license. Its code was last updated Mar 28, 2026.
AI-Native System Builder: MyInvestPilot Architecture Series
Public architecture notes from building MyInvestPilot — an AI-native investment system built through AI-driven development, constrained DSL design, hybrid agents, and a cost-aware solo cloud stack.
Why This Repo Exists
This repository is the external technical narrative of MyInvestPilot: design choices, trade-offs, and failures from building a production system where AI writes most implementation code.
It is not:
- a product user manual (that belongs to product docs)
- an internal AI blueprint repo (used for private planning/execution)
- a generic prompt cookbook
The goal is straightforward: document what it actually takes to operate as an AI-native system builder, not just ship demos.
The Series
01. Engine
From Rule-Based Scripts to AI-Native Engines: Why I Built a Constrained DSL
How constrained IR (JSON DSL), schema-driven contracts, and deterministic execution work together for reliable decision systems.
02. Methodology
Why I Stopped Writing Code: The 60/40 Rule for AI-Native Engineering
How AI-driven solo development evolved from 60/40 alignment/execution toward 90/10 in many cycles, with human quality gates still mandatory.
03. Agent Architecture
The Agent Paradox: Why We Built a "Boring" Hybrid Architecture
Why we split Local Agent (flexible orchestration) and Remote Agent (deterministic async processing) instead of relying on a single autonomous agent.
04. Cloud Infrastructure
Running a Multi-Vendor, Event-Driven Cloud Architecture as a Solo Builder
How a multi-vendor, async-first, serverless-friendly stack supports real usage with low operational cost.
Blog Publication
A synthesized version of this series was published on the author's blog:
How I Built an AI-Native Quantitative System — bmpi.dev
This post distills the key architectural decisions across all four parts: constrained DSL design, schema supply chain, prompt evolution, and the hybrid agent paradox.
Audience
This repo is for builders working on:
- AI-native application architecture
- agent systems with reliability boundaries
- cloud-native/serverless systems for solo teams
About
I'm Dawei (@madawei2699), an independent builder. I previously shipped myGPTReader and invest-alchemy, which later evolved into MyInvestPilot.
License
MIT
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