Practical patterns, guardrails, and playbooks to safely apply AI and automation for capturing knowledge, improving search, and scaling routine decisions.
Playbook for designing automated alerts and routines that escalate to owners with clear context and learning steps, while preserving human review and audit trails.
Practical playbook with decision checklists, human-in-the-loop agent templates, audit and explainability requirements, monitoring & rollback runbook, governance guardrails, and ready-to-adapt sample micro-automations (daily KPI summaries, ETL routines, repository curation). Designed to help teams scale routine work while preserving learning, ownership, and safety.
A practical, field-ready spec template and checklist for designing, implementing, testing, and operating retrieval‑augmented agents that connect to internal knowledge with clear rules for retrieval, scoring, citation, safety, monitoring, and failure handling.
A practical playbook for selecting, designing, testing, deploying, and operating lightweight agents (summarizers, note-takers, alert actors, ETL assistants) with explicit human oversight, audit trails, safe defaults, rollback plans, and measurement strategies to keep learning and ownership intact.
A practical, step-by-step playbook to design, build, evaluate, and operate retrieval-augmented assistants (RAAs) for organizational knowledge. Covers use-case mapping, index design, chunking, provenance, prompt patterns, privacy and hallucination guardrails, human-in-the-loop escalation, monitoring, and a rollout checklist with lightweight evaluation metrics.
Practical patterns, templates, and governance guardrails for automating routine alerts, ETL jobs, scheduled reports, and multi-step workflows while preserving human review, learning loops, ownership, auditability, and safe rollback paths.