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AI Career Skills Course
Module 7: LLMs, RAG And Agents
AI, Machine Learning and Generative AI Basics
LLMs, Tokens and Context Windows
Hallucinations, Confidence and AI Limitations
Clear Task Prompts and Output Formats
Context, Examples and Few-Shot Prompting
Prompt Debugging and Iteration
AI for Writing, Summaries and Emails
AI for Research and Meeting Notes
AI Automation for Routine Office Work
AI Study Planning and Concept Learning
AI Practice Questions and Feedback Loops
Using AI Responsibly in Assignments
AI for Code Explanation and Debugging
AI for Tests, Refactors and Code Review
AI Pair Programming Workflow
AI for Spreadsheet Cleanup and Analysis
Asking Better Data Questions
Chart, Insight and Decision Summaries
Embeddings and Semantic Search
RAG Workflows and Knowledge Bases
AI Agents, Tools and Automation
Privacy, Sensitive Data and Access Control
Bias, Fairness and Harmful Output Checks
Evaluating AI Answers Before Use
CONTENTS

AI Agents, Tools and Automation

Know when tool-using agents help and when they create unnecessary risk.

AI Career Skills Course
Module 7: LLMs, RAG And Agents
AI career skills
generative AI
+7
May 28, 2026
27
A

Learning Outcome

Know when tool-using agents help and when they create unnecessary risk.

Core Ideas

  • Agent: System that plans steps and may use tools.
  • Tool call: Structured request to an external capability.
  • Guardrail: Control that limits unsafe behavior.
  • Rollback: Ability to undo or recover from an action.

Career Use Case

An operations team can use an agent for multi-step tasks only when tool permissions, approvals, and rollback are clear.

Practical Workflow

  1. Start by naming the outcome: what should improve after using AI Agents, Tools and Automation?
  2. Add the input material, constraints, and success criteria before asking for output.
  3. Ask for assumptions and uncertainty when the answer affects a real decision.
  4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

  • List the tools an agent may use for one workflow and mark which actions require human approval.
  • A good agent design limits authority and records what happened.
  • Before moving on, explain how Agent and Tool call change the decision.

Common Mistakes

  • Using a generic prompt when the task needs clear context.
  • Accepting polished wording as proof of accuracy.
  • Sharing private data without redaction or approval.
  • Skipping a final human review for important decisions.

Quick Revision

Module 7: LLMs, RAG And Agents lesson 21 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs

Is AI Agents, Tools and Automation only for technical users?

No. The course treats AI as a practical workplace and learning skill, with technical depth only where it improves judgement.

Should I trust AI output immediately?

No. Use AI to accelerate work, then verify facts, privacy, source fit, and reasoning before relying on the result.

What should I practice after this lesson?

List the tools an agent may use for one workflow and mark which actions require human approval.

How does the linked practice quiz help?

The practice quiz checks the lesson concepts immediately with feedback, while the paid mock bundle uses separate assessment questions.

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hardAI Career Skills
Practice Quiz 21: AI Agents, Tools and Automation
12 questions12 min

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Lesson 3 of 3 in Module 7: LLMs, RAG And Agents
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RAG Workflows and Knowledge Bases
Next section: Module 8: AI Safety, Privacy And Evaluation
Privacy, Sensitive Data and Access Control
Module 8: AI Safety, Privacy And Evaluation
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