Ai Agent

Which statement best explains the difference between a normal chatbot and an AI agent?

• A chatbot is smarter than an AI agent

• A chatbot can use more tools than an AI agent

• A chatbot mainly answers questions, while an AI agent follows a workflow with tools, context, and actions

• They are exactly the same

In practice, what makes an AI agent more reliable for repeated team workflows?

• Longer prompts every time

• More creativity in each answer

• Stable instructions, defined scope, reusable skills, and consistent output format

• Fewer constraints

Which prompt is the strongest example of giving useful context?

• “Check this.”

• “Fix the file.”

• “Analyze this project.”

• “Review this PR for security risks, focus on auth and secrets handling, list high-severity issues first, and suggest safe fixes without changing production code.”

What is the main role of guardrails in an AI agent system?

• To help the slide design look cleaner

• To make the model more creative

• To define safety boundaries, permissions, and actions the agent must not cross

• To replace the need for human review

Which of the following is the clearest example of a missing-guardrails risk?

• The output is shorter than expected

• The summary uses the wrong tone

• The agent reads .env, exposes API keys, or accesses sensitive credentials outside scope

• The model asks a follow-up question

What is the key difference between Agent.md and guardrails?

• Guardrails are for formatting, Agent.md is for design

• Agent.md is only for developers, guardrails are only for managers

• Agent.md defines how the agent should work, while guardrails define the boundaries it must not cross

• They are two names for the same thing

Why would a marketing team use an AI agent instead of normal chat?

• To make the UI look more advanced

• To avoid using any data files

• To run repeatable workflows on files, research, and structured inputs with more consistent outputs

• To replace all marketing decisions automatically

Which workflow is the best example of AI agent use in marketing?

• Ask for one slogan and stop there

• Generate random social captions without context

• Read Excel research, group competitors by segment, identify feature gaps, analyze revenue signals, and produce a report

• Design the full mobile app from scratch

What does MCP add to an AI agent?

• It removes the need for context

• It replaces skills and guardrails

• It connects the agent to real tools, files, systems, or design/code context so it can work with actual data

• It only changes the visual interface

Which example is more about guardrails than Agent.md?

• Output findings in markdown

• Prioritize high-severity issues first

• Never read .env, keystore, or secret files

• Explain the reasoning before suggesting fixes