Enterprise AI guidance covering architecture, governance, private AI, agents, inference platforms, operating models, risk, and responsible adoption.
Build a runnable Python harness with complete companion files, deterministic fixtures, and independent grading checks. Test controlled context variations before connecting a read-only application.
Test whether irrelevant context changes alter an AI decision. Preserve task, evidence, and policy while distinguishing genuine behavioral differences from changed settings or grading defects.
Introducing the Inference Resilience Profile, a public proposal for testing AI service continuity across Kubernetes and NVIDIA GPU failure domains.
Design bounded AI control loops for hybrid operations. Connect observation, policy, scoped execution, recovery, and cost limits through a practical autonomy ladder and accountable owners.
Evaluate the complete agent release and its enforcement boundaries. Use fault injection, independently checked outcomes, and explicit limits when introducing production authority.
A practical guide to AI-assisted paper writing that preserves the author’s thinking, verifies claims and quotations, and keeps Word revisions reversible. Learn how to check evidence, tracked changes, document properties, and the final publication copy.
Design legal AI research around authorized matters, validated legal sources, fact status, confidentiality, and accountable attorney review. An abbreviated prompt scaffold shows how those controls become a reviewable research workflow.
Build an AI-assisted contract review workflow that preserves agreement versions, clause evidence, playbook decisions, internal negotiation boundaries, approvals, and the transition from proposed language to executed obligations.
Design an attorney-controlled investigation workflow from allegation through preservation, evidence review, findings, and remediation. Keep AI inside approved data and authority boundaries, with separate human decisions for privilege, discipline, disclosure, hold release, and closure.
Design clinical documentation AI around authorized records, patient and encounter validation, source provenance, medication discrepancies, and human review. Keep record updates and handoff responsibility with authorized clinicians.
Build clinical decision support around a review contract: validate patient evidence, expose uncertainty, separate urgency from extended analysis, and make the basis of every option reviewable by a licensed clinician.
Design patient education and discharge drafting around approved clinical facts. Preserve medication instructions, language and accessibility needs, unresolved questions, and clinician authority over review and release.
Bind agent approval to the exact action, identity, target, policy, and validity window. Revalidate changed conditions and give uncertain execution and recovery their own authority rules.
Assess enterprise AI through a traceable chain of system boundaries, threats, controls, evidence, residual risk, and approval. Distinguish proposed controls from tested behavior and reassess material changes.
Multiple AI reviewers can share the same mistake. Design assurance quorums with isolated judgments, protected evidence, deterministic vetoes, and measured marginal value.
Govern dynamic model routing without allowing fallback to inherit production authority. Record resolved models, qualify route members by action, and define when degraded paths must require approval or hold execution.
A model upgrade can change what an AI agent does with existing permissions. Requalify its action boundaries, compare tool behavior, test independent controls, and expand production autonomy only when the evidence supports it.
Restore agent authority through action-level requalification, measured canary exposure, and enforceable promotion and demotion rules. Use current evidence to decide which autonomy levels can safely return.
Choose reversal, compensation, forward recovery, or containment according to the observed effect. Authorize corrective actions, preserve concurrent changes, and track consequences that cannot be undone.
Reconcile uncertain AI agent actions using target evidence, protected intent, current authority, and idempotency. Keep unresolved effects from becoming duplicate or unauthorized work after recovery.
Find an architecture guide, platform, or operational problem.
Suggested searches
