Oracle Cloud Infrastructure Generative AI Professional — Lesson 3: Agents, Tools, Retrieval, Evaluation, Guardrails, and Human Review

Learning Objectives

• Interpret the Oracle objects, services, calculations, reports, interfaces, or code involved in agents, tools, retrieval, evaluation, guardrails, and human review.
• Perform a hands-on implementation or diagnostic sequence without bypassing governance or approvals.
• Prove reproducibility by comparing expected and actual results and documenting corrective action.

Detailed Topics

• Agents
• Tools
• Retrieval
• Evaluation
• Guardrails
• Human Review
• requirements, ownership, prerequisites, data, and acceptance criteria for agents, tools, retrieval, evaluation, guardrails, and human review
• security, controls, integration, performance, recovery, and support implications for agents, tools, retrieval, evaluation, guardrails, and human review

Instructor Demonstration

Demonstrate agents, tools, retrieval, evaluation, guardrails, and human review within an OCI generative-AI assistant grounded in enterprise content, evaluated for answer quality, protected by access controls, and operated within cost limits. Use representative data and capture model and endpoint settings, prompt tests, embedding and retrieval results, grounded responses, evaluation scores, guardrail output, access logs, latency, and cost metrics so another practitioner can independently verify the result.

Hands-On Exercise

Complete a controlled Oracle Cloud Infrastructure Generative AI Professional exercise focused on agents, tools, retrieval, evaluation, guardrails, and human review. Introduce one realistic failure involving hallucination, poor retrieval, sensitive-data exposure, prompt injection, unsafe output, uncontrolled tool use, high latency, and unexpected consumption cost; isolate the cause, apply the smallest reversible correction, and rerun the acceptance checks.

1. Record the requirement, environment, identities, dependencies, data, controls, and expected result.
2. Perform the lesson workflow and capture evidence before and after each material change.
3. Test normal, boundary, failure, security, reconciliation, recovery, and operational conditions relevant to the topic.
4. Document the root cause, correction, rollback, owner, support action, and next improvement priority.

Scenario Analysis

• Map the end-to-end flow from source input through Oracle processing to the business output.
• Locate the control point where incorrect data, configuration, access, or integration could alter the result.
• Recommend preventive monitoring and an operational handoff with named ownership.

Troubleshooting Guidance

When agents, tools, retrieval, evaluation, guardrails, and human review does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the Oracle-specific logs, reports, traces, calculations, workflow history, transaction status, or diagnostic output relevant to the lesson. Test one reversible hypothesis at a time and verify that the correction does not create hallucination, poor retrieval, sensitive-data exposure, prompt injection, unsafe output, uncontrolled tool use, high latency, and unexpected consumption cost elsewhere in the process.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The workflow is reproducible and supported by model and endpoint settings, prompt tests, embedding and retrieval results, grounded responses, evaluation scores, guardrail output, access logs, latency, and cost metrics.
• Security, controls, data quality, integration, performance, recovery, and support conditions are reviewed.
• Exceptions, corrective action, rollback, approvals, monitoring, and handover are recorded.

Lesson Outcome

Learners can apply Agents, Tools, Retrieval, Evaluation, Guardrails, and Human Review in a realistic Oracle Cloud Infrastructure Generative AI Professional situation and defend the result with course-specific evidence.