Stage 6: Capstone and Certification

Stage 6: Capstone and Certification

You have reached the final stage. This is where you demonstrate everything you have learned by building a complete AI agent system from scratch. There are no tutorials here. Just you, your knowledge, and a real problem to solve.

Module 6.1: Capstone Project (3 hours)

Project Overview

Your capstone project is to design, build, and document a complete AI agent system that solves a real-world problem. This is not a toy example. It should be something you could actually use or deploy.

6.1.1 Project Requirements

Core Requirements (All projects must include):

6.1.2 Project Ideas

Choose a project that interests you. Here are some suggestions organised by difficulty:

Foundation Level (Recommended for first-time builders):

1. Personal Research Assistant

   • Searches the web for information
   • Summarises findings
   • Saves notes to files
   • Tools: web_search, file_write, summarise

2. Email Draft Generator

   • Takes bullet points as input
   • Generates professional email drafts
   • Adjusts tone based on recipient type
   • Tools: tone_classifier, email_template, grammar_check

3. Code Documentation Bot

   • Reads Python files
   • Generates docstrings for functions
   • Creates README sections
   • Tools: file_read, docstring_generator, markdown_writer

Intermediate Level:

4. Customer Support Agent

   • Classifies incoming queries
   • Searches knowledge base for answers
   • Escalates complex issues to humans
   • Tools: classifier, knowledge_search, ticket_create, human_escalate

5. Data Analysis Assistant

   • Loads CSV files
   • Performs statistical analysis
   • Generates charts
   • Explains findings in plain English
   • Tools: csv_loader, calculator, chart_generator, explain

6. Meeting Summariser

   • Processes meeting transcripts
   • Extracts action items
   • Identifies decisions made
   • Creates structured summaries
   • Tools: text_splitter, entity_extractor, summarise, format_output

Advanced Level:

7. Multi-Agent Research Team

   • Supervisor coordinates specialists
   • Researcher finds information
   • Writer creates content
   • Critic reviews and improves
   • Tools: Multiple per agent, handoff between agents

8. Workflow Automation Agent

   • Monitors email for triggers
   • Executes automated responses
   • Logs all actions for audit
   • Supports human-in-the-loop for critical decisions

9. Security Assessment Agent

   • Analyses code for vulnerabilities
   • Checks configurations
   • Generates security reports
   • Prioritises findings by severity

6.1.3 Project Structure

Use this structure for your submission:

my-capstone-project/
├── README.md              # Project overview and setup
├── ARCHITECTURE.md        # Design decisions and diagrams
├── SECURITY.md            # Security considerations
├── requirements.txt       # Python dependencies
├── src/
│   ├── __init__.py
│   ├── agent.py           # Main agent implementation
│   ├── tools/
│   │   ├── __init__.py
│   │   ├── tool_1.py
│   │   ├── tool_2.py
│   │   └── tool_3.py
│   └── utils/
│       ├── __init__.py
│       ├── validation.py
│       └── logging.py
├── tests/
│   ├── test_agent.py
│   ├── test_tools.py
│   └── test_integration.py
└── examples/
    ├── example_1.py
    └── example_2.py

6.1.4 Documentation Template

Your README.md should include:

# [Project Name]

## Overview
[One paragraph describing what your agent does and why it is useful]

## Architecture

[Include a Mermaid diagram showing your agent's structure]

## Features
- Feature 1
- Feature 2
- Feature 3

## Setup

### Prerequisites
- Python 3.10+
- Ollama installed and running

### Installation
```bash
pip install -r requirements.txt

Configuration

[Any environment variables or config files needed]

Usage

Basic Example

from src.agent import MyAgent

agent = MyAgent()
result = agent.run("Your query here")
print(result)

Security Considerations

[Document your security measures and limitations]

Testing

pytest tests/

Limitations

[Be honest about what your agent cannot do]

Future Improvements

[What would you add with more time?]

---

### 6.1.5 Evaluation Criteria

Your project will be evaluated on these criteria:

| Criteria | Weight | Description |
|----------|--------|-------------|
| Functionality | 25% | Does the agent work correctly? |
| Code Quality | 20% | Clean, readable, well-organised code |
| Security | 20% | Appropriate security measures implemented |
| Documentation | 15% | Clear, complete, and accurate docs |
| Testing | 10% | Adequate test coverage |
| Innovation | 10% | Creative problem-solving |

**Grading Scale:**

- **Distinction (90%+)**: Exceptional work exceeding all requirements
- **Merit (70-89%)**: Strong work meeting all requirements
- **Pass (50-69%)**: Adequate work meeting minimum requirements
- **Refer (Below 50%)**: Needs improvement before certification

---

## Module 6.2: Peer Review and Certification (2 hours)

### 6.2.1 Peer Review Process

Before receiving your certification, you will review another learner's project and receive feedback on yours.

**Giving Feedback:**

1. Clone their repository
2. Run their agent locally
3. Review their documentation
4. Test edge cases
5. Provide constructive feedback using this template:

```markdown
## Peer Review: [Project Name]

### What Works Well
- [Specific positive observations]

### Suggestions for Improvement
- [Constructive feedback with specific recommendations]

### Security Review
- [ ] Input validation present
- [ ] Output sanitisation present
- [ ] Appropriate access controls
- [ ] Audit logging implemented
- [ ] Secrets properly managed

### Documentation Review
- [ ] Clear setup instructions
- [ ] Architecture diagram included
- [ ] Limitations acknowledged
- [ ] Examples provided

### Overall Assessment
[Summary of your review]

Receiving Feedback:

• Read feedback with an open mind
• Ask clarifying questions if needed
• Implement improvements where appropriate
• Respond professionally to all feedback

6.2.2 Final Certification Assessment

After completing your capstone, take this final assessment to earn your certification.

6.2.3 Certification Details

Upon successful completion, you will receive:

Module 6.3: Architecture Challenge Game

Before your final assessment, test your architectural decision-making skills with this professional simulation game. Design optimal agent architectures for real-world scenarios.

Congratulations

You have completed the AI Agents: From Foundation to Mastery course. This is a genuine achievement. You have progressed from understanding basic AI concepts to building production-ready agent systems with proper security and ethical considerations.

Resources for Continued Learning:

1. LangChain and LangGraph Documentation: Deep dive into production frameworks
2. Anthropic and OpenAI Research Blogs: Latest developments from frontier labs
3. Hugging Face Community: Model sharing and collaboration
4. This Course Updates: I will continue updating this course as the field evolves

Stay Connected:

• Review new modules as they are added
• Share your capstone projects
• Join the community discussions
• Provide feedback to improve the course

Thank you for learning with me. Now go build something amazing.