Java Application Development with Agentic AI Foundations — Lesson 2: LLM and Agentic AI Foundations, Prompts, Tools, Memory, and Orchestration

Learning Objectives

• Describe prerequisites, ownership, dependencies, and failure conditions for llm and agentic ai foundations, prompts, tools, memory, and orchestration.
• Construct the course-specific workflow while recording evidence at each material checkpoint.
• Evaluate the solution against security, control, reliability, performance, and support criteria.

Detailed Topics

• LLM
• Agentic AI Foundations
• Prompts
• Tools
• Memory
• Orchestration
• ownership, prerequisites, data, and acceptance criteria for llm and agentic ai foundations, prompts, tools, memory, and orchestration
• security, control, integration, performance, recovery, and support implications

Instructor Demonstration

Demonstrate llm and agentic ai foundations, prompts, tools, memory, and orchestration 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 agent traces, prompt results so another practitioner can verify the result.

Hands-On Exercise

Complete a controlled Java Application Development with Agentic AI Foundations activity focused on llm and agentic ai foundations, prompts, tools, memory, and orchestration. Introduce a realistic case of hallucination, 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 llm and agentic ai foundations, prompts, tools, memory, and orchestration does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the most relevant agent traces, prompt results before editing configuration. Test one reversible hypothesis at a time, verify recovery, and confirm that the fix does not create hallucination elsewhere.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The result is reproducible and supported by agent traces, prompt results.
• 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 LLM and Agentic AI Foundations, Prompts, Tools, Memory, and Orchestration in a realistic Java Application Development with Agentic AI Foundations situation and defend the result with course-specific evidence.