An AI evaluation dataset should cover normal work, edge cases, refusals and tool outcomes, with release thresholds tied to one real production workflow.
Insights
Field notes on production AI, workflow automation, custom software, integrations, and the operating tradeoffs behind shipping them.
An AI evaluation dataset should cover normal work, edge cases, refusals and tool outcomes, with release thresholds tied to one real production workflow.
AI agent memory should separate session context, durable facts, source records and deletion rules before it stores information across work conversations.
A vibe-coded AI app is production-ready only after code ownership, security, data, tests, deployment, observability and rollback evidence pass one review.
An AI model fallback plan defines retry, switching, degraded mode, escalation and recovery evidence before a production workflow depends on one provider.
A RAG knowledge base is ready for staff only when access, source freshness, citations, deletion, refusal and answer tests pass against real work questions.
Use 12 acceptance tests to check call scope, noisy audio, tool accuracy, human handoff, consent, rollback and ownership before an AI voice agent goes live.
A five-level AI agent permissions matrix sets read, prepare, write, approval and action limits before the first production workflow can go live safely.
Use 10 acceptance tests to verify code, cloud, data, models, access, monitoring, rollback, and ownership before completing an AI vendor handover safely.
Software development proposals should align scope, assumptions, QA, security, ownership, delivery and support before price. See the 10-area framework.
No articles match your search. Try a different keyword.