[ LIBRARY ]

AI Guides for Product Managers

Verified and connected references for understanding agents, governing risk and improving product decisions.

This library organizes guides that help teams move from curiosity about artificial intelligence to decisions they can verify. The material combines explanations, evaluation criteria, operating examples, and questions a product team can bring to discovery, prioritization, or risk-review meetings.

Start with the cluster that matches your problem. Agents cover architecture, evaluation, and operations; governance covers inventory, risk classification, and controls; product connects adoption, strategy, research, and outcomes. Pillar guides provide entry points and link to more specific references.

These pages are maintained as living references: they show update dates, related sources, and practical downloads when available. Use the cluster navigation to compare approaches, and return to the underlying sources before turning a recommendation into a policy or production requirement.

Each guide is designed to answer a practical question and make the next action clearer, whether that means mapping systems, choosing an evaluation method, or defining who owns a control.

[ TOPICAL CLUSTER ]

AI Agents

Understand, build, evaluate and operate AI agents with clear goals and boundaries.

PILLAR GUIDEAI Agents: what they are, how they work and when to use themLearn how AI agents use tools, state, guardrails and evaluations, and decide when agentic systems make sense for products and operations.UPDATED 2026-07-22How to build AI agents: from outcome to operationsBuild AI agents with a practical method for outcomes, tools, context, safety, evaluations, staged launch and monitoring.UPDATED 2026-07-22AI agents with n8n: architecture and production checklistBuild AI agents with n8n using triggers, tools, optional memory, human approval, evaluations and explicit error handling.UPDATED 2026-07-22AI agent examples: use cases, metrics and risksExplore AI agent examples for product, support, operations and engineering, including tools, outcomes, metrics and risks.UPDATED 2026-07-22AI agent evaluation templateEvaluate AI agents for task completion, tools, safety, cost, latency, oversight and readiness to launch.UPDATED 2026-08-31AI agent evaluation: test cases, metrics and release gatesLearn how to evaluate AI agents for outcomes, trajectories, tools, safety, cost and readiness before releasing a change.UPDATED 2026-08-17How to operate AI agents in production: observability, SLOs and incidentsLearn how to operate AI agents with observability, proportionate SLOs, cost controls, incident response and reversible changes.UPDATED 2026-08-10Research: which controls do official documents recommend for operating AI agents?A reproducible audit of seven official documents covering observability, evaluation, metrics, human control and incidents in AI agents.UPDATED 2026-08-17AI agent incident response: containment, recovery and rollbackLearn how to detect, contain, recover from and learn from AI agent incidents without confusing configuration rollback with reversal of external effects.UPDATED 2026-08-17Research: which official controls appear in AI agent recovery?A reproducible audit of eight official documents on detection, containment, resumption, fallback and approval in AI agent recovery.

[ TOPICAL CLUSTER ]

AI Governance

Turn responsible AI principles into operational decisions, controls and evidence.

PILLAR GUIDEAI governance: a practical guide for products and companiesImplement AI governance with inventory, risk classification, evaluations, human oversight, monitoring and incident response.UPDATED 2026-07-22AI governance checklist for products and organizationsAn AI governance checklist for discovery, data, evaluation, approval, launch, monitoring, incidents and retirement.UPDATED 2026-07-22AI risk matrix: classify impact and controlsClassify AI risk by impact, likelihood, scale, reversibility and contestability to set evaluation, approval and oversight.UPDATED 2026-07-22AI system inventory: template and essential fieldsCreate an AI inventory with purpose, owner, data, model, vendor, risk, evaluations, incidents and review dates.

[ TOPICAL CLUSTER ]

Product Management

Connect strategy, discovery, delivery and metrics to the responsible use of artificial intelligence.

PILLAR GUIDEProduct Management: what it is, how it works and the PM roleUnderstand product management, strategy, discovery, prioritization, roadmaps, delivery, metrics and the Product Manager role.UPDATED 2026-07-22AI product management: strategy, discovery and metricsUse artificial intelligence in discovery, roadmaps and product operations while preserving evidence, context and human accountability.UPDATED 2026-08-27Spec Kit in Practice: an SDD Tutorial for Product Managers and Tech LeadsLearn how to use GitHub Spec Kit to turn a product idea into a specification, plan, tasks and verifiable code with a Product Manager, Tech Lead and AI coding agents.UPDATED 2026-07-22Artificial intelligence for Product Managers: a practical guideLearn how to evaluate opportunities, design experiences, measure quality and operate AI products without relying on hype.UPDATED 2026-07-22State of AI in Product Management 2026An author analysis of public sources on AI adoption, agents, productivity, governance and product management capabilities in 2026.