Overview

Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.

Syllabus

Applying Intent Classification Techniques

You will design and test multiple intents in Dialogflow CX using training phrase variation, entity annotation, and simulator-based validation to improve intent classification reliability

Analyzing Chat Logs for Routing Optimization

You will export and analyze recent conversation logs (e.g., past few days or week) to identify representative mis-routed utterances and diagnose their root causes, and apply corrections through entity or training phrase refinements.

Evaluating Agent Quality Metrics

You will export fulfillment diagnostics from Dialogflow CX, calculate NLU accuracy, latency, and human-handoff rate, and produce a data-backed recommendation memo proposing two optimization actions.

Creating Secure Webhook Integrations

You will write and deploy a secure Node.js or Python webhook using Google Cloud Functions that calls an external REST API and returns dynamic order status data to the Dialogflow CX agent in under one second round-trip.