hive/docs/quizzes/03-build-your-first-agent.md

🤖 Build Your First Agent Challenge

Get hands-on with AI agents! This challenge is for AI/ML engineers who want to understand agent development and contribute to Aden's agent ecosystem.

Difficulty: Intermediate Time: 2-3 hours Prerequisites: Complete Getting Started, Python experience, basic LLM knowledge

---

Part 1: Agent Fundamentals (20 points)

Task 1.1: Core Concepts 📚

Answer these questions about Aden's agent architecture:

1. What is a "node" in Aden's architecture? How does it differ from a traditional function?

2. Explain the SDK-wrapped node concept. What four capabilities does every node get automatically?

3. What's the difference between:

   • A Coding Agent and a Worker Agent
   • Goal-driven vs workflow-driven development
   • Predefined edges vs dynamic connections

4. Why does Aden generate "connection code" instead of using a fixed graph structure?

Task 1.2: Memory Systems 🧠

Aden has sophisticated memory management:

1. Describe the three types of memory available to agents:

   • Shared Memory
   • STM (Short-Term Memory)
   • LTM (Long-Term Memory / RLM)

2. When would an agent use each type?

3. How does "Session Local memory isolation" work?

Task 1.3: Human-in-the-Loop 🙋

Explain the HITL system:

1. What triggers a human intervention point?
2. What happens if a human doesn't respond within the timeout?
3. List three scenarios where HITL would be essential

---

Part 2: Agent Design (25 points)

Task 2.1: Design a Multi-Agent System 🎭

Design a Content Marketing Agent System with multiple worker agents:

Goal: Automatically create and publish blog posts based on company news

Requirements:

• Must use at least 3 specialized worker agents
• Include human approval before publishing
• Handle failures gracefully

Provide:

1. Agent Diagram: Show all agents and how they connect
2. Agent Descriptions: For each agent, describe:
   • Name and role
   • Inputs and outputs
   • Tools it needs
   • Failure scenarios
3. Human Checkpoints: Where would humans intervene?
4. Self-Improvement: How would this system learn from failures?

Task 2.2: Goal Definition 🎯

Write a natural language goal that a user might give to create your system:

Example Goal:
"Create a system that monitors our company RSS feed for news,
writes engaging blog posts about each news item, gets approval
from the marketing team, and publishes to our WordPress site.
If a post is rejected, learn from the feedback to write better
posts in the future."

Your goal should be:

• Clear and specific
• Include success criteria
• Mention failure handling
• Specify human touchpoints

Task 2.3: Test Cases 📋

Design 5 test cases for your agent system:

Test Case	Input	Expected Output	Success Criteria
Happy Path	Normal news item	Published blog post	Post live on site
...	...	...	...

Include at least:

• 1 happy path
• 2 edge cases
• 2 failure scenarios

---

Part 3: Practical Implementation (30 points)

Task 3.1: Agent Pseudocode 💻

Write pseudocode for ONE of your worker agents:

class ContentWriterAgent:
    """
    Agent that takes news items and writes blog posts.
    """

    def __init__(self, config):
        # Initialize with tools, memory, LLM access
        pass

    async def execute(self, input_data):
        # Main execution logic
        pass

    async def handle_failure(self, error, context):
        # How to handle different types of failures
        pass

    async def learn_from_feedback(self, feedback):
        # How to improve based on rejection feedback
        pass

Provide detailed pseudocode with:

• LLM calls and prompts
• Memory reads/writes
• Tool usage
• Error handling

Task 3.2: Prompt Engineering 📝

Write the actual prompts for your agent:

1. System Prompt: The core instructions for your agent
2. Task Prompt Template: How tasks are presented to the agent
3. Feedback Learning Prompt: How rejection feedback is processed

Example format:

SYSTEM PROMPT:
You are a professional content writer for {company_name}...

TASK PROMPT:
Given the following news item:
{news_content}

Write a blog post that...

FEEDBACK PROMPT:
Your previous post was rejected with this feedback:
{feedback}

Analyze what went wrong and...

Task 3.3: Tool Definitions 🔧

Define the tools your agent needs:

tools = [
    {
        "name": "search_company_knowledge",
        "description": "Search internal knowledge base for relevant context",
        "parameters": {
            "query": "string - search query",
            "limit": "int - max results (default 5)"
        },
        "returns": "List of relevant documents"
    },
    # Add more tools...
]

Define at least 3 tools with:

• Clear name and description
• Input parameters with types
• Return value description
• Example usage

---

Part 4: Advanced Challenges (25 points)

Task 4.1: Failure Evolution Design 🔄

Design the self-improvement mechanism in detail:

1. Failure Classification: Create a taxonomy of failures for your agent

   - LLM Failures: rate limit, content filter, hallucination
   - Tool Failures: API down, invalid response, timeout
   - Logic Failures: wrong output format, missing data
   - Human Rejection: quality issues, off-brand, factual error
   

2. Learning Storage: What data do you store for each failure type?

3. Evolution Strategy: How does the Coding Agent use failure data to improve?

4. Guardrails: What prevents the system from making things worse?

Task 4.2: Cost Optimization 💰

Your agent system will be called frequently. Design cost optimizations:

1. Model Selection: When to use GPT-4 vs GPT-3.5 vs Claude Haiku?
2. Caching Strategy: What can be cached to reduce LLM calls?
3. Batching: How can you batch operations for efficiency?
4. Budget Rules: Design budget rules for your system

Task 4.3: Observability Dashboard 📊

Design what metrics should be tracked for your agent system:

1. Performance Metrics: (at least 5)
2. Quality Metrics: (at least 3)
3. Cost Metrics: (at least 3)
4. Alert Conditions: When should the system alert humans?

---

Submission Checklist

• All Part 1 concept answers
• Complete multi-agent design (Part 2)
• Implementation code/pseudocode (Part 3)
• Advanced challenge solutions (Part 4)

How to Submit

1. Create a GitHub Gist with your answers
2. Name it aden-agent-challenge-YOURNAME.md
3. Include code files separately
4. If you created diagrams, include images
5. Email to careers@adenhq.com
   • Subject: [Agent Challenge] Your Name

---

Scoring

Section	Points
Part 1: Fundamentals	20
Part 2: Design	25
Part 3: Implementation	30
Part 4: Advanced	25
Total	100

Passing score: 75+ points

---

Bonus Points (+25)

• +10: Actually implement a working prototype using any framework
• +10: Create a demo video of your agent in action
• +5: Submit a PR adding your agent as a template to the repo

---

Example Agent Templates

Need inspiration? Here are some agent ideas:

1. Research Agent: Gathers information from multiple sources
2. Code Review Agent: Reviews PRs and suggests improvements
3. Customer Support Agent: Handles support tickets with escalation
4. Data Pipeline Agent: Monitors and fixes data quality issues
5. Meeting Agent: Summarizes meetings and creates action items

---

Good luck! We're excited to see your creative agent designs! 🤖✨