Agents & Tools Interview Prep

Course Overview

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Free Course

For engineers preparing for AI agent and LLM tool-use interviews who want clear, sayable answers backed by real understanding. You will be able to explain agent loops, function calling message by message, tool and schema design, MCP, memory and orchestration, cost and latency, and agent safety, with production examples for each.

Instructor: MantraMindAI

Sections: 6

Course Content

Section 1: Agent Fundamentals

Prepares you to explain what an agent is, how its loop runs and stops, and how to choose between the main agent shapes when an interviewer asks you to justify a design.

1. What defines an AI agent, and how is it different from a simple LLM response system?

2. How does an agent loop function, and what conditions determine when it stops?

3. What is tool use in LLMs, and why is it essential for building agents?

4. How does the ReAct framework combine reasoning and actions in agents?

5. What is the difference between single-agent and multi-agent architectures?

6. What is the Plan-and-Execute approach, and when is it useful?

7. How do reactive agents differ from proactive agents?

8. What is the difference between tool-calling agents and code-generating agents?

Section 2: Tool Use & Function Calling

Prepares you to walk through a tool call message by message, design tool names and schemas a model uses correctly, and explain tool_choice, parallel calls, error results and small-model routing.

1. How does function calling work in LLM systems from start to finish?

2. Why doesn’t an LLM directly execute tools, and what role does it actually play?

3. What are best practices when defining tools (inputs, outputs, descriptions)?

4. What is the purpose of the tool_choice parameter, and how do you use it?

5. When should you use tool use versus simpler patterns like prompting or chaining?

6. How can smaller models be used effectively for tool delegation?

7. How can smaller models be used effectively for tool delegation beyond simple routing?

Section 3: MCP (Model Context Protocol)

Prepares you to explain what MCP is and is not, how hosts, clients and servers talk over stdio and Streamable HTTP, what tools, resources and prompts are for, and when MCP is worth its extra moving parts.

1. What problem does MCP solve in agent systems?

2. How does MCP’s client-server architecture work?

3. What are the key components involved in MCP interactions?

4. How does MCP differ from traditional function calling approaches?

5. What are the main primitives in MCP (tools, resources, prompts)?

6. What does “discoverability” mean in MCP, and why is it powerful?

7. When should you choose MCP over simple tool integration?

8. How does MCP act as a contract between agents and external systems?

Section 4: Memory, Planning & Orchestration

Prepares you to explain how agents remember across steps and sessions, how an orchestrator runs tool calls under policy, and where human approval, reflection, planning and reasoning techniques earn their cost.

1. What types of memory do AI agents use, and how are they different?

2. What is agent orchestration, and how do multiple components work together?

3. How does human-in-the-loop improve agent reliability?

4. What is agent reflection, and how does it enhance performance?

5. How do planning and feedback loops improve task execution?

6. What reasoning techniques can agents use for decision-making?

Section 5: System Design & Performance

Prepares you to reason out loud about agent token growth, cost and latency, to design hard budgets, and to lay out state so long agent runs can pause, crash and resume safely.

1. How do you manage token usage in long-running agent workflows?

2. What strategies help reduce cost and latency in agent systems?

3. How do you enforce budget limits for agent tasks?

4. How can you speed up slow agent workflows?

5. How do you design state management for complex agent pipelines?

Section 6: Failures, Debugging & Safety

Prepares you to diagnose looping agents, wrong tool choices, bad arguments and conflicting tool results, to name the failure modes that reach production, and to defend an agent against prompt injection and over-broad tool access.

1. How do you detect and fix infinite loops in agent execution?

2. How do you handle incorrect tool selection by an agent?

3. What causes wrong parameter extraction, and how can it be improved?

4. How should an agent handle conflicting outputs from multiple tools?

5. What are common failure modes in production agent systems?

6. What security risks exist in agent systems, and how can you prevent them?