Java Application Development with Agentic AI Foundations — Lesson 4: Evaluation, Security, Observability, Performance, Deployment, and Responsible Operations

Learning Objectives

• Trace prerequisites, ownership, dependencies, and failure conditions for evaluation, security, observability, performance, deployment, and responsible operations.
• Configure the course-specific workflow while recording evidence at each material checkpoint.
• Defend the solution against security, control, reliability, performance, and support criteria.

Detailed Topics

• Evaluation
• Security
• Observability
• Performance
• Deployment
• Responsible Operations
• ownership, prerequisites, data, and acceptance criteria for evaluation, security, observability, performance, deployment, and responsible operations
• security, control, integration, performance, recovery, and support implications

Instructor Demonstration

Demonstrate evaluation, security, observability, performance, deployment, and responsible operations within a Java application enhanced with an AI agent that retrieves trusted context, invokes approved tools, and operates with measurable safety. Use representative data and capture evaluation scores, security and latency metrics so another practitioner can verify the result.

Hands-On Exercise

Complete a controlled Java Application Development with Agentic AI Foundations activity focused on evaluation, security, observability, performance, deployment, and responsible operations. Introduce a realistic case of sensitive-data leakage, isolate the cause, apply a reversible correction, and rerun the acceptance checks.

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

Scenario Analysis

• Establish current state, desired state, constraints, and acceptable service impact.
• Compare alternative implementation or diagnostic approaches and their rollback consequences.
• Document the decision, evidence, exception, corrective action, and measurable improvement target.

Troubleshooting Guidance

When evaluation, security, observability, performance, deployment, and responsible operations does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the most relevant evaluation scores, security and latency metrics before editing configuration. Test one reversible hypothesis at a time, verify recovery, and confirm that the fix does not create sensitive-data leakage elsewhere.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The result is reproducible and supported by evaluation scores, security and latency 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 Evaluation, Security, Observability, Performance, Deployment, and Responsible Operations in a realistic Java Application Development with Agentic AI Foundations situation and defend the result with course-specific evidence.