A staged collection of practical steps and exercises to help teams design, prototype, test, deploy, and monitor reliable AI assistants for real workflows.
Welcome — Build AI agents that actually work for your team. If your team is curious about AI assistants but worries about surprises, this resource is for you. It shows practical, low-risk ways to design, prototype, test, deploy, and operate AI agents that save time, reduce errors, and fit into real workflows. Teams...
Designing and scoping AI agents that work. Teams build effective agents when they treat the project like a product with a narrow, testable outcome—not a vague automation experiment. This guide helps you translate a real problem into a scoped agent project with measurable success criteria, clear boundaries, and...
Explore this interactive audit, assessment, reflection, or practical tool. Sign in to save your responses and return to them later.
Agent Acceptance Tests & Quality Checklist. Before expanding use, make sure the agent passes these acceptance tests. Treat each item as pass/fail and record evidence. Functional tests. Representative scenarios: The agent completes the primary outcome in a set of 10 representative scenarios (include edge cases)...
Operate, monitor, and improve: Lightweight agent operations. Deploying an agent isn’t the finish line—it's the start of an operational lifecycle. This guide describes a lightweight, practical ops approach so your agent improves without creating a maintenance burden. Key metrics to track. Adoption: daily active users...
Composite example: Customer triage assistant for a small service team. This composite case illustrates how a small customer service team used the design patterns above to build an effective agent without heavy engineering. Problem. The team spent large portions of the day triaging inbound service requests. Managers...
Practical patterns, decision criteria, checklists, test scenarios, security controls, and example architectures to design, prototype, deploy, and maintain reliable multi-step AI agents that integrate into team workflows.
A practical, step-by-step playbook for prototyping reliable AI agents: persona templates, memory patterns, tool integration contracts, scripted test flows, evaluation heuristics, and deployment readiness checklists.
An operational, interactive checklist to enforce layered guardrails, logging, constraint enforcement, and anomaly detection for agents. Designed to capture evidence, assign owners, record risk, and save review records.
Operational runbook to prevent, detect, and respond to agent misbehavior. Includes concise safety checklist, allowed/disallowed actions, example policy rules, concrete monitoring signals and thresholds, anomaly-detection patterns, a clear escalation and rollback flow, and a reusable post-incident review template.
Practical, operational checklist and templates to monitor agent behavior, enforce policy constraints, detect anomalies, and make agent actions observable and auditable. Includes signals, logging requirements, guardrail examples, escalation flows, KPIs, and periodic review guidance tailored for teams deploying autonomous agents or automated workflows.