The Architecture of a Trustworthy AI Workflow
Trust emerges from evidence, bounded actions, explicit decisions and recoverable execution across the whole workflow.
Articles tagged with Software Architecture.
Trust emerges from evidence, bounded actions, explicit decisions and recoverable execution across the whole workflow.
Reliable agents come from permissions, validation and controlled execution, not increasingly elaborate instructions.
RAG succeeds through owned sources, measurable retrieval and disciplined lifecycle management.
Useful observability connects requests, evidence, decisions, actions and outcomes without retaining data indiscriminately.
Copilot experiences are shaped by identity, permissions, retrieval and integration architecture long before a prompt reaches the model.
Enterprise AI projects fail when compelling demos are mistaken for operational systems. The remedy is a narrow outcome, owned data and production evidence.
Non-delegable formulas can silently produce incomplete results, making data-source choice and query design correctness issues.
Retries and duplicate triggers are normal; flows must prevent the same business action from happening twice.
Production readiness is not a model benchmark. It is the ability to operate, explain, recover and improve a complete AI system.
Governance becomes effective when policies compile into controls that can be tested, observed and evidenced in running systems.
Policies become useful only when they are translated into enforceable decisions across identity, data, models and runtime behaviour.
The conversational interface is the visible edge of a system spanning identity, retrieval, policy, tools, telemetry and human escalation.