Closing the Reliability Gap in AI Systems
Why plausible answers need source checks, software validation, observable outcomes and an operating process around them.
Ideas grounded in engineering.
Practical writing on AI systems, the controls around them and the work of bringing them into a business.
Insights
8 articles
Finding the Real Cost of an AI Workflow
Measure model calls, retrieval, retries, tool activity and human review to understand cost per completed task.
Production AI: Design for the Failure Paths
How to handle incomplete tasks, uncertain external writes and operational recovery when an AI prototype becomes a working system.
Taking an AI Pilot into Production
A staged approach to integration, acceptance testing, rollout and operating ownership without treating a pilot as proof of readiness.
Designing Model-Agnostic AI Applications
How to separate model interfaces from business logic while testing the real limits of portability and provider changes.
Managing Context in Enterprise AI Systems
How to select, preserve and validate the information an AI workflow needs without confusing a context window with durable memory.
Connecting AI to Existing Business Systems
How to integrate AI with established software through narrow interfaces, explicit data ownership and tested recovery paths.
An AI Dictionary for Practical Engineering Decisions
Twenty-five AI terms explained through what they mean, what they change and the limits to keep in mind.