This hub gathers focused analyses and decision lenses for AI output quality governance for RAG and AI agents. Coverage is scoped to operational frameworks and measurement constructs used by operators: governance operating model components, failure taxonomy and severity levels, provenance archetypes, canonical event model, and measurement artifacts such as a composite uncertainty index and per-interaction cost.
The collection addresses category-level operational challenges and decision points encountered in live RAG and agent flows. Topics examined include detection and instrumentation considerations, sampling and hybrid sampling approaches for review, human review and reviewer note schema, triage and escalation patterns, templates and RACI for role clarity, and synthetic test harnesses for observable behavior sampling.
These articles are intended as analytical resources and decision-clarity aids for experienced operators and decision-makers. They emphasize frameworks, trade-offs, and scoped templates rather than step-by-step implementation instructions; they do not represent exhaustive coverage and are best used alongside organization-specific constraints and the broader pillar material.
For a consolidated overview of the underlying system logic and how these topics are commonly connected within a broader operating model, see:
AI output quality governance for RAG and agents: Operational model for taxonomy, detection & review.
Reframing the Problem & Common Pitfalls
- When Keeping Every Interaction Snapshot Becomes a Privacy and Compliance Liability
- How to spot signs your RAG or agent outputs are unreliable (before customers complain)
- Why your human-review sampling is missing the worst RAG failures (and what teams immediately misunderstand)
Frameworks & Strategic Comparisons
- How to structure a failure taxonomy and calibrate severity levels for RAG and AI agents
- Why sampling strictly by volume is hiding high-severity failures in live RAG systems
- Who owns output-quality in RAG and agent flows? Why unclear RACI slows remediation in enterprise teams
- When to Trust Detectors — and When You Still Need Human Review in RAG Pipelines
Methods & Execution Models
- When to Roll Back a RAG/Agent Release: a Deliberation Template for High-stakes Incidents
- Why hybrid sampling beats volume-only audits — designing sampling rates for enterprise RAG review
- Why RAG regressions still slip through CI — where synthetic tests need to change
- Why your per-interaction cost assumptions are hiding risky trade-offs in RAG flows
- Why a single confidence number fails: building a composite uncertainty index for RAG triage
- Why inconsistent reviewer notes are costing your RAG pipeline credibility (and what to decide next)
- What telemetry your RAG pipeline really needs — and where instrumentation decisions break teams
- When Should You Escalate Flagged Outputs in Live RAG Flows? Key Decision Triggers and Ownership Tensions