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...
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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...