Agentic AI Patterns

Course Overview

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Free Course

For engineers preparing for AI engineer, agent developer and AI architect interviews who need to explain agent design patterns clearly, without relying on any one framework. You will be able to answer questions on workflows versus agents, tool calling and MCP, planning, reflection, memory, multi-agent topologies and A2A, human-in-the-loop, guardrails, cost and evaluation, each with a short interview answer and a worked real-world example.

Instructor: MantraMindAI

Sections: 9

Course Content

Section 1: Basics of Agentic AI

Prepares you to define an agent precisely, tell it apart from a workflow, and answer the opening questions on components, uses, ethics, security and skills.

1. What do you understand about Agentic AI? How is it different from Traditional AI?

2. Elaborate on a few real-world applications of Agentic AI.

3. What are the key components of an Agentic AI system?

4. What are the ethical considerations specific to Agentic AI?

5. What are the security risks associated with deploying autonomous AI agents?

6. What skills are necessary to develop and implement Agentic AI systems?

Section 2: AI Agent Design & Architecture

Prepares you to walk through how an agent is built end to end, and to explain prompt roles, native tool calling and planning in the depth a design interview expects.

1. Explain the end-to-end process of building an AI agent, from ingestion to evaluation.

2. How do system and user prompts differ, and how does each shape an agent’s behavior and responses?

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

4. What is a planning module in AI agents, and why is it important?

Section 3: Tool Augmentation, Autonomy, and Execution Environments

Prepares you to explain how tools and MCP extend an agent, how much autonomy to grant, where agent code should run safely, and which specialised agent roles are worth having.

1. What is tool augmentation in Agentic AI, and how does it expand agent capabilities?

2. What are autonomous agents vs. assisted agents, and when is each preferred?

3. What is an execution environment in agent systems (e.g., sandboxing, secure runtimes)?

4. What are the core types of specialized agents commonly used in Agentic AI architectures?

Section 4: Memory Systems and Optimization

Prepares you to explain short-term, long-term and episodic memory, the reflection and evaluator-optimizer loop, where memories should be stored, and how to keep an agent's context small, cheap and accurate.

1. How does memory work in AI agents (short-term, long-term, episodic)?

2. What is reflection in agentic workflows, and how does it improve reasoning?

3. How do memory stores (vector DBs, SQL stores) enhance agent performance?

4. How does memory optimization improve model performance in Agentic AI systems?

5. What is an agent’s context window, and how does it influence design decisions?

Section 5: Observability, Reasoning, and Execution Patterns

Prepares you to explain how agents are traced and monitored, how chain-of-thought and reasoning models fit in, and which execution pattern to choose, from prompt chaining and routing to orchestrator-workers and parallel fan-out.

1. What is LLM observability, and why is it critical for production systems?

2. What is Chain-of-Thought (CoT) reasoning? Why is it important for complex tasks?

3. What’s the difference between Chain-of-Thought (CoT) and Chain-of-Decision (CoD) prompting?

4. What is tracing in LLM pipelines? What are spans?

5. What are the different task execution patterns in Agentic AI?

6. How does an Agentic AI system decide between parallel vs. sequential execution?

Section 6: RAG, Multimodality, and External Integrations

Prepares you to explain agentic RAG, multimodal and computer-use agents, reliable API integration, enterprise-scale automation, and how live data keeps an agent's answers true.

1. What is Retrieval-Augmented Generation (RAG), and how does it support agent intelligence?

2. What is multimodality, and how do agents leverage multimodal models?

3. How do AI agents use external knowledge bases and APIs effectively?

4. How does Agentic AI enable scalable automation across enterprise workflows?

5. How does real-time data augmentation improve agent performance?

Section 7: Applications and Industry Use Cases

Prepares you to discuss generative and synthetic-data agents, the failure modes that make agents unreliable, prompt drift, debugging agent runs, and how to evaluate an agent's path as well as its result.

1. How can AI agents be designed for image generation, art workflows, or synthetic data creation?

2. What are the biggest challenges in building robust AI agents?

3. How do you ensure prompt robustness and avoid prompt drift in production systems?

4. How do you debug agentic workflows using LangChain, AutoGen, or LangGraph?

5. How do you evaluate the performance of an AI agent system?

Section 8: Governance, HITL, and Enterprise Outcomes

Prepares you to design human-in-the-loop checkpoints and autonomy tiers, explain stateful agents and durable checkpoints, put numbers on agent cost and savings, and discuss personalised, proactive customer service safely.

1. What is the human-in-the-loop (HITL) approach, and when is it necessary?

2. How does an Agentic AI system balance autonomy with human oversight?

3. What are Stateful Agents, and how do they enhance decision-making accuracy?

4. How does Agentic AI support cost reduction and operational efficiency?

5. How can Agentic AI enhance customer service through personalization and prediction?

Section 9: Advanced Design Scenarios

Prepares you to answer open-ended design questions, from multi-agent topologies and LLM planning to security, healthcare, finance, gaming and IoT agents, with a clear structure of clarify, architecture, controls, metrics and what to ship first.