---
title: "Panaversity Certified Agentic and Robotic AI Engineer"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Panaversity Certified Agentic and Robotic AI Engineer

The **Panaversity Certified Agentic and Robotic AI Engineer** certification is a rigorous, multi-level program designed to validate expertise in modern AI, agentic AI, cloud-native technologies, and physical AI systems. The certification is structured into four progressive levels, each building on the previous one, ensuring a comprehensive skill set from foundational to advanced concepts. This guide provides a detailed overview of each level, including exam structures, covered topics, preparation guidance, and resources.

## Certification Structure

The certification consists of four levels, each with specific exams tailored to different expertise levels:

- **Level 1 (Beginner)**: Focuses on foundational knowledge in Python, agentic AI, and related concepts. Exams are conducted online, on a fixed schedule, and are proctored.
- **Level 2 (Professional)**: Targets advanced proficiency in Python, agentic AI, and AI protocols. Exams are manually scheduled by contacting Zia Khan via WhatsApp at +92-300-826-3374. Faculty and product developers must pass all Level 2 exams.
- **Level 3 (Agent Native Cloud Professional)**: Covers advanced cloud-native technologies, including Docker, Kubernetes, and Dapr.
- **Level 4 (Physical AI Professional)**: Focuses on physical AI and robotics, including NVIDIA Isaac ROS, GR00T, and Isaac Sim.

Below is a detailed breakdown of each level and its associated exams.

---

## Level 1 (Beginner)

Level 1 is designed for individuals new to AI and Python programming, providing a foundation for further study. All exams are conducted online, on schedule, and proctored.

### 1. Fundamentals of Modern AI Python Level 1 (Beginner) Quiz

**Status**: Under development by the Panaversity Exam Board  
**Covers**:  
- Core Python programming concepts for AI applications.  
- Colabs 01 to 09 from the Panaversity GitHub repository:  
  [https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab](https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab)  

**Preparation Guidance**:  
- Study basic Python syntax, data structures (lists, dictionaries, tuples), control flow, and functions.  
- Practice with Colabs 01–09 to gain hands-on experience with Python in AI contexts.  
- Focus on understanding Python’s role in AI development, including data manipulation and basic scripting.

---

### 2. Advanced Modern AI Python Level 1 (Beginner) Quiz

**Status**: Under development by the Panaversity Exam Board  
**Covers**:  
- Advanced Python concepts, including type hints and static typing.  
- Resources:  
  - [MyPy Cheat Sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)  
  - [MyPy Built-in Types](https://mypy.readthedocs.io/en/stable/builtin_types.html)  
  - [MyPy Protocols](https://mypy.readthedocs.io/en/stable/protocols.html)  
  - [MyPy Kinds of Types](https://mypy.readthedocs.io/en/stable/kinds_of_types.html)  
  - Colabs 12 to 17: [https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab](https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab)  

**Preparation Guidance**:  
- Study static typing with MyPy, including type hints, unions, and optional types.  
- Understand structural subtyping and protocols for flexible type checking.  
- Practice with Colabs 12–17 to apply advanced Python concepts in AI workflows.  
- Review MyPy documentation for a deeper understanding of type systems in Python.

---

### 3. Fundamentals of Agentic AI Level 1 (Beginner) Quiz

**Status**: Under development by the Panaversity Exam Board  
**Covers**:  
- Introduction to agentic AI and the OpenAI Agents SDK.  
- Basic Markdown syntax for documentation.  
- Resources:  
  - [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)  
  - [OpenAI Cookbook: Multi-Agent Portfolio Collaboration](https://cookbook.openai.com/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration)  
  - [OpenAI GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)  
  - [Kaggle Whitepaper on Prompt Engineering](https://www.kaggle.com/whitepaper-prompt-engineering)  
  - [Markdown Basic Syntax](https://www.markdownguide.org/basic-syntax/)  
  - [Markdown Cheat Sheet](https://www.markdownguide.org/cheat-sheet/)  
  - Colabs: [https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first](https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first)  

**Preparation Guidance**:  
- Learn the basics of agentic AI, including the concept of agents, tools, and their interactions.  
- Study the OpenAI Agents SDK to understand its Python-first design and core components.  
- Practice writing simple Markdown documents for AI project documentation.  
- Complete hands-on exercises from the provided GitHub repository to build familiarity with agentic AI workflows.

---

## Level 2 (Professional)

Level 2 targets professionals with strong programming and AI skills. Exams are manually scheduled by contacting Zia Khan via WhatsApp at +92-300-826-3374. Faculty and product developers must pass all Level 2 exams.

### 1. Fundamentals of Modern AI Python Level 2 (Professional) Quiz

**Total Questions**: 46  
**Duration**: 90 minutes  
**Difficulty Rating**: Upper-intermediate  
- **Easy (≈25%, 12 questions)**: Solvable in ≤30 seconds by those with introductory Python knowledge.  
- **Moderate (≈50%, 23 questions)**: Requires mentally running short code snippets or understanding CPython quirks.  
- **Advanced (≈25%, 11 questions)**: Covers generator protocol, walrus/precedence chains, default-argument capture, for–else edge cases, nested loop flow control, and subtle identity vs. equality distinctions.  

**Covers**:  
- Colabs 01 to 09: [https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab](https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab)  

**Preparation Guidance**:  
- Deepen your understanding of Python’s advanced features, such as generators, list comprehensions, and flow control.  
- Study CPython-specific behaviors, including identity vs. equality and default argument pitfalls.  
- Practice with Colabs 01–09 to reinforce core Python skills in AI contexts.  
- Focus on debugging and analyzing code snippets to predict outcomes accurately.

---

### 2. Advanced Modern AI Python Level 2 (Professional) Quiz

**Total Questions**: 50  
**Duration**: 2 hours 30 minutes  
**Difficulty Rating**: Advanced (upper-intermediate to professional)  

**Overview**:  
This quiz assesses advanced proficiency in modern Python programming, focusing on static typing, asynchronous programming, object-oriented principles, and modern libraries like Pydantic v2 and dataclasses.  

**Covers**:  
- Advanced static typing with MyPy and Pyright (type hints, generics, variance, structural subtyping).  
- Asynchronous programming with asyncio (coroutines, event loops, asyncio.gather, asyncio.wait_for).  
- Object-oriented programming (multiple inheritance, MRO, composition vs. aggregation, duck typing).  
- Modern Python libraries (Pydantic v2, dataclasses).  
- CPython’s Global Interpreter Lock (GIL) and .pyc file behavior.  
- Resources:  
  - [MyPy Cheat Sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)  
  - [MyPy Built-in Types](https://mypy.readthedocs.io/en/stable/builtin_types.html)  
  - [MyPy Protocols](https://mypy.readthedocs.io/en/stable/protocols.html)  
  - [MyPy Kinds of Types](https://mypy.readthedocs.io/en/stable/kinds_of_types.html)  
  - Colabs 12 to 17: [https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab](https://github.com/panaversity/learn-modern-ai-python/tree/main/00_python_colab)  

**Preparation Guidance**:  
- Master advanced static typing with MyPy, including generics, variance, and protocols.  
- Study asyncio for concurrency in I/O-bound tasks, focusing on coroutines and event loops.  
- Deepen knowledge of OOP, including multiple inheritance and MRO.  
- Practice using Pydantic v2 for data validation and dataclasses for efficient class definitions.  
- Review CPython’s GIL and its impact on concurrency.  
- Complete Colabs 12–17 and study MyPy documentation to prepare for complex scenarios.

---

### 3. Fundamentals of Agentic AI Level 2 (Professional) Quiz

**Total Questions**: 48  
**Duration**: 120 minutes  
**Difficulty Rating**: Advanced (not beginner-friendly)  

**Overview**:  
This quiz tests deep knowledge of the OpenAI Agents SDK, focusing on its architecture, Pydantic models, async programming, and prompt engineering. It is designed for intermediate to advanced learners.  

**Covers**:  
- OpenAI Agents SDK architecture (Agents, Tools, Handoffs, Runner).  
- Pydantic models for typed inputs/outputs.  
- Async programming and multi-agent workflows.  
- Prompt engineering (Chain-of-Thought, system message design, sensitive data handling).  
- Basic Markdown syntax.  
- Resources:  
  - [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)  
  - [OpenAI Cookbook: Multi-Agent Portfolio Collaboration](https://cookbook.openai.com/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration)  
  - [OpenAI GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)  
  - [Kaggle Whitepaper on Prompt Engineering](https://www.kaggle.com/whitepaper-prompt-engineering)  
  - [Markdown Basic Syntax](https://www.markdownguide.org/basic-syntax/)  
  - [Markdown Cheat Sheet](https://www.markdownguide.org/cheat-sheet/)  
  - Colabs: [https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first](https://github.com/panaversity/learn-agentic-ai/tree/main/01_ai_agents_first)  

**Preparation Guidance**:  
- Study the OpenAI Agents SDK, focusing on Agents, Tools, Handoffs, and Runner.run_sync().  
- Practice async programming and multi-agent orchestration.  
- Learn prompt engineering techniques, including Chain-of-Thought and system message design.  
- Review Pydantic models for validation and error handling.  
- Complete hands-on exercises from the provided GitHub repository.  
- Spend 2–3 weeks studying SDK documentation and practicing code analysis.

---

### 4. Agentic AI Protocols Level 2 (Professional) Quiz

**Total Questions**: 100  
**Duration**: 2 hours 30 minutes  
**Difficulty Rating**: Advanced (not beginner-friendly)  

**Covers**:  
- Foundational AI protocols (Chapters 01 to 05).  
- Model Context Protocol (MCP) and streamable HTTP transports.  
- Agent-to-Agent (A2A) communication protocols.  
- Resources:  
  - [MCP Concepts](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols/01_mcp_concepts)  
  - [MCP Specification: Streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http)  
  - [A2A Protocols](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols/03_a2a)  
  - [Model Context Protocol](https://github.com/panaversity/learn-agentic-ai/tree/main/03_ai_protocols/02_model_context_protocol)  

**Preparation Guidance**:  
- Study the Model Context Protocol (MCP) and its application in AI systems.  
- Understand streamable HTTP transports for real-time communication.  
- Learn Agent-to-Agent (A2A) protocols for multi-agent collaboration.  
- Review Chapters 01–05 from the provided GitHub repository.  
- Practice implementing and analyzing protocol-based AI systems.

---

### 5. Agentic AI Memory, RAG, and Design Patterns Level 2 (Professional) Quiz

**Total Questions**: Under development  
**Difficulty Rating**: Advanced (not beginner-friendly)  

**Covers**:  
- Memory management in agentic AI systems.  
- Retrieval-Augmented Generation (RAG) techniques.  
- Design patterns for scalable AI architectures.  

**Preparation Guidance**:  
- Study memory management techniques in agentic AI, including context retention.  
- Learn RAG for enhancing AI responses with external data.  
- Explore design patterns for building robust AI systems.  
- Monitor Panaversity updates for exam availability and additional resources.

---

## Level 3 (Agent Native Cloud Professional)

Level 3 focuses on cloud-native technologies for deploying and managing agentic AI systems.

### Agent Native Cloud Level 3 Quiz

**Total Questions**: Under development  
**Difficulty Rating**: Advanced (not beginner-friendly)  

**Covers**:  
- Docker for containerization.  
- Kubernetes for orchestration.  
- Dapr for distributed application runtimes.  

**Preparation Guidance**:  
- Study Docker for building and deploying containerized AI applications.  
- Learn Kubernetes for managing containerized workloads at scale.  
- Explore Dapr for simplifying microservices and event-driven architectures.  
- Practice deploying AI systems in cloud environments using these technologies.

---

## Level 4 (Physical AI Professional)

Level 4 focuses on physical AI and robotics, integrating AI with hardware systems.

### Physical AI and Robotics Level 4 Quiz

**Total Questions**: Under development  
**Difficulty Rating**: Advanced (not beginner-friendly)  

**Covers**:  
- NVIDIA Isaac ROS for robotic operating systems.  
- NVIDIA Isaac GR00T for general-purpose robotic intelligence.  
- NVIDIA Isaac Sim for robotic simulation.  

**Preparation Guidance**:  
- Study NVIDIA Isaac ROS for building robotic applications.  
- Learn GR00T for advanced robotic intelligence and decision-making.  
- Practice using Isaac Sim for simulating robotic environments.  
- Explore real-world applications of physical AI in robotics.

---

## General Preparation Tips

1. **Hands-On Practice**: Use the provided GitHub repositories to complete all relevant Colabs and exercises.  
2. **Study Official Documentation**: Review MyPy, OpenAI Agents SDK, and NVIDIA documentation thoroughly.  
3. **Time Management**: Practice solving questions under timed conditions to simulate exam environments.  
4. **Community Support**: Engage with the Panaversity community or contact Zia Khan for scheduling and guidance.  
5. **Iterative Learning**: Start with Level 1 materials and progressively build skills for higher levels.

---

## Contact and Scheduling

- **Level 1 Exams**: Conducted online, on schedule, and proctored. Check Panaversity’s official channels for updates.  
- **Level 2 Exams**: Schedule manually by contacting Zia Khan via WhatsApp at +92-300-826-3374.  
- **Faculty/Product Developer Requirement**: Must pass all Level 2 exams.  

For the most current information on exam availability and updates, visit the Panaversity GitHub repositories or official website.
