Reliable AI Systems
Engineering patterns for building reliable real-world software with probabilistic models.
Modern software is beginning to interact directly with real-world systems.
Examples include AI agents navigating websites, browser automation workflows, document processing pipelines, operational tooling that interacts with external APIs, and business systems driven by AI-assisted reasoning.
These systems differ from traditional software in one important way. They operate inside environments whose behavior changes independently of the code.
Websites update. Authentication flows evolve. APIs introduce edge cases. Business processes adapt.
As a result, reliability becomes a systems problem rather than a modeling problem. Improving prompts alone rarely solves it.
Instead, reliable systems separate responsibilities:
environment ↓ AI reasoning ↓ deterministic reliability boundaries ↓ software execution ↓ effects on the environment
This site explores engineering patterns for building systems that remain stable under those conditions.
The ideas here emerged while building automation systems interacting with dynamic environments, where reliability problems appear early and often.
The sections that follow explore the architectural pieces required to make these systems work.
Real-World Software Loop
Reliability Architecture
Reliable AI systems are not just model integrations. They are software systems operating in dynamic real-world environments.
Framework
Foundations
Core Framework