Perspectives
Field notes
Short arguments from the operating principles behind the portfolio.Start by naming what the system may observe, recommend, change, and never touch. Add confidence thresholds, escalation triggers, spend limits, and rollback paths before choosing orchestration patterns. The result is an agent whose authority can be inspected—and narrowed—without relying on good intentions.
Read article →High-stakes AI should make its sources, assumptions, gaps, and transformations visible. Grounding is not merely retrieval; it is a chain of accountability from evidence to interpretation to recommendation. If a user cannot challenge the chain, the interface is hiding the most important part of the system.
Read article →A review button is theater if the reviewer lacks context, time, or the ability to stop the action. Design explicit decision rights, usable evidence, override paths, and ownership for exceptions. The human role should be specific enough to test, train, and audit.
Read article →Define success measures and failure modes alongside the workflow. Capture disagreement, low-confidence cases, overrides, and downstream outcomes. Those signals should shape prompts, retrieval, policy, and product behavior through controlled change—not silent model drift.
Read article →Resources
Architecture field guide · PDFProduction architecture, governance decision rights, execution layers, evaluation operations, and required feedback loops.
Open resource ↗
Framework · PNGA seven-stage path from idea and outcome through workflow, rules, integrations, governance, experience, and delivery.
Open resource ↗
Presentation · PDFA practical visual guide to the layers and controls that turn an AI prototype into an operating product.
Open resource ↗Checklist · CSVA portable checklist for evaluating architecture, evidence, governance, experience, and operations.
Open resource ↗