Oracle AI for Fusion Cloud CX: AI Agent Studio Developer Training Ed 1

Course Description

Oracle AI for Fusion Cloud CX: AI Agent Studio Developer Training Ed 1 prepares learners to work confidently with AI Agent Studio architecture, agent use cases, personas, intents, Agent creation, instructions, actions. Each module links design intent with realistic transactions, exception paths, access controls, and the checks required before a solution is handed to users or support teams.

The course also covers Testing, evaluation datasets, response quality, hallucination controls, Deployment, monitoring, feedback. Through AI agent use-case canvas and architecture design, working agent prototype and tool-call trace, agent evaluation report and red-team findings, governed release record and monitoring dashboard, participants learn how to recognise weak assumptions, confirm the real cause of an error, and leave a result that another practitioner can reproduce.

Participants leave prepared to deliver a safe and useful Fusion Cloud CX AI agent designed, grounded, tested, deployed, monitored, and governed for business service. Their closing plan converts test findings into concrete actions for governance, adoption, monitoring, remediation, and future releases.

What You Will Learn

• Explain how AI Agent Studio architecture, agent use cases, and personas establish the operating foundation and shape later configuration decisions.
• Configure intents, tools, grounding, and security boundaries, then confirm readiness through AI agent use-case canvas and architecture design rather than relying on screen messages alone.
• Execute a controlled workflow covering Agent creation, instructions, and actions, preserving input traceability, approvals, and the resulting changes.
• Read working agent prototype and tool-call trace to isolate an instruction conflict causing the agent to choose the wrong action, separate symptoms from causes, and select a correction that can be safely retested.
• Integrate Testing, evaluation datasets, and response quality with ownership, access, and downstream process expectations.
• Challenge hallucination controls, permissions, and exception handling using normal and boundary cases, then defend the outcome with agent evaluation report and red-team findings.
• Investigate a model update reducing task accuracy without triggering review by reviewing Deployment, monitoring, and feedback before applying a reversible fix.
• Prepare governed release record and monitoring dashboard that demonstrates lifecycle management, compliance, change control, and human oversight and supports handover, monitoring, and the next improvement decision.

SkillDynamo Training Approach

Instruction includes product demonstrations, practical setup, process execution, role-based scenarios, diagnostic exercises, acceptance review, and production-readiness planning. Particular attention is given to evidence quality, ownership, safe change, and supportability.