Quick Prototyping - From Idea to Working Multi-Agent System in Minutes

In the fast-paced world of AI development, the ability to rapidly prototype and iterate is crucial. AgentRouter transforms the traditionally complex process of building multi-agent systems into something achievable in minutes, not months. Let's explore how you can go from concept to working prototype faster than ever before.

The Speed Advantage

Traditional AI system development typically involves:

• Weeks of architecture design
• Months of implementation
• Extensive infrastructure setup
• Complex integration work
• Lengthy testing cycles

With AgentRouter:

• Minutes to first working prototype
• Hours to production-ready system
• Zero infrastructure setup required
• Built-in integrations
• Instant testing and iteration

Your First Agent in 60 Seconds

Let's build a working agent system right now:

# Step 1: Install AgentRouter (30 seconds)
# pip install agentrouter

# Step 2: Create your first agent (30 seconds)
from agentrouter import ManagerAgent

agent = ManagerAgent(
    name="assistant",
    api_key="your-api-key",
    goal="Help users with their queries"
)

# That's it! You have a working agent
response = await agent.execute([
    {"role": "user", "content": "Explain quantum computing"}
])

print(response)

Building a Complete System in 5 Minutes

Let's create a full customer service system:

# Complete customer service system - under 5 minutes
from agentrouter import ManagerAgent, tool
import asyncio

# 1. Define a simple tool (30 seconds)
@tool(
    schema={
        "type": "function",
        "function": {
            "name": "check_order_status",
            "description": "Check the status of an order",
            "parameters": {
                "type": "object",
                "properties": {
                    "order_id": {"type": "string"}
                },
                "required": ["order_id"]
            }
        }
    }
)
async def check_order_status(order_id: str):
    # Mock order status - replace with real API
    return {
        "order_id": order_id,
        "status": "shipped",
        "tracking": "TRK123456",
        "estimated_delivery": "2 days"
    }

# 2. Create the manager agent (30 seconds)
manager = ManagerAgent(
    name="customer_service_manager",
    api_key="your-api-key",
    backstory="I manage customer inquiries and coordinate responses",
    goal="Provide excellent customer service"
)

# 3. Add specialized workers (1 minute)
order_specialist = manager.create_worker(
    name="Order_Specialist",
    role="Order tracking and status",
    goal="Help customers with order-related queries"
)

# 4. Register tools (30 seconds)
order_specialist.register_tool(check_order_status)

# 5. Test the system (30 seconds)
async def test_system():
    response = await manager.execute([
        {"role": "user", "content": "Where is my order ORD-12345?"}
    ])
    print(response)

# Run it!
asyncio.run(test_system())

Total time: Under 3 minutes!

Rapid Iteration Patterns

Pattern 1: The Minimal Viable Agent (MVA)

Start with the absolute minimum:

# Version 1: Basic agent (1 minute)
agent = ManagerAgent(
    name="helper",
    api_key="key",
    goal="Assist users"
)

# Version 2: Add specialization (30 seconds)
agent.backstory = "Expert in technical support"
agent.temperature = 0.3  # More focused responses

# Version 3: Add a worker (30 seconds)
worker = agent.create_worker(
    name="Specialist",
    role="Handle complex queries"
)

# Version 4: Add tools (1 minute)
@tool(schema={...})
async def search_knowledge(query: str):
    return knowledge_base.search(query)

worker.register_tool(search_knowledge)

# Total evolution time: 3 minutes

Pattern 2: The Rapid Expansion

Quickly scale from one to many agents:

# Start with one agent
main_agent = ManagerAgent(name="main", api_key="key")

# Rapidly add specialists (30 seconds each)
specialists = [
    "Sales_Agent",
    "Support_Agent", 
    "Technical_Agent",
    "Billing_Agent"
]

for specialist in specialists:
    main_agent.create_worker(
        name=specialist,
        role=f"Handle {specialist.split('_')[0].lower()} queries"
    )

# Now you have a 5-agent system in under 3 minutes

Pattern 3: The Tool-First Approach

Build tools, then wrap with agents:

# Define your tools first (2 minutes)
tools = {
    "search": search_database,
    "calculate": perform_calculation,
    "analyze": analyze_data,
    "report": generate_report
}

# Create agent and register all tools (1 minute)
agent = ManagerAgent(name="analyst", api_key="key")
for tool_name, tool_func in tools.items():
    agent.register_tool(tool_func)

# Ready to handle complex workflows!

Prototyping Real-World Systems

1. E-Commerce Assistant (3 minutes)

from agentrouter import ManagerAgent, tool
from datetime import datetime

# Quick e-commerce prototype
class EcommerceAssistant:
    def __init__(self, api_key):
        # Main coordinator
        self.manager = ManagerAgent(
            name="ecommerce_manager",
            api_key=api_key,
            goal="Assist customers with shopping"
        )
        
        # Quick worker setup
        self.product_agent = self.manager.create_worker(
            name="Product_Expert",
            role="Product recommendations"
        )
        
        self.order_agent = self.manager.create_worker(
            name="Order_Handler",
            role="Order processing"
        )
        
        # Register quick tools
        self.setup_tools()
    
    def setup_tools(self):
        @tool(schema={"type": "function", "function": {"name": "search_products"}})
        async def search_products(query: str):
            # Mock product search
            return [
                {"name": "Product A", "price": 99.99, "rating": 4.5},
                {"name": "Product B", "price": 149.99, "rating": 4.8}
            ]
        
        self.product_agent.register_tool(search_products)
    
    async def chat(self, message):
        return await self.manager.execute([
            {"role": "user", "content": message}
        ])

# Use it immediately
assistant = EcommerceAssistant("your-api-key")
response = await assistant.chat("Show me laptops under $1000")

2. Data Analysis Pipeline (4 minutes)

# Quick data analysis system
from agentrouter import ManagerAgent
import pandas as pd

class DataAnalyzer:
    def __init__(self, api_key):
        self.orchestrator = ManagerAgent(
            name="data_orchestrator",
            api_key=api_key,
            goal="Analyze data and provide insights"
        )
        
        # Create analysis pipeline
        self.create_pipeline()
    
    def create_pipeline(self):
        # Data ingestion agent
        self.ingestion = self.orchestrator.create_worker(
            name="Data_Ingestion",
            role="Load and validate data"
        )
        
        # Analysis agent
        self.analysis = self.orchestrator.create_worker(
            name="Data_Analysis",
            role="Perform statistical analysis"
        )
        
        # Visualization agent
        self.viz = self.orchestrator.create_worker(
            name="Visualization",
            role="Create charts and reports"
        )
        
        # Quick tool registration
        self.register_quick_tools()
    
    def register_quick_tools(self):
        @tool
        async def load_csv(filepath: str):
            return pd.read_csv(filepath).to_dict()
        
        @tool
        async def calculate_stats(data: dict):
            df = pd.DataFrame(data)
            return {
                "mean": df.mean().to_dict(),
                "std": df.std().to_dict(),
                "correlation": df.corr().to_dict()
            }
        
        self.ingestion.register_tool(load_csv)
        self.analysis.register_tool(calculate_stats)
    
    async def analyze(self, filepath):
        prompt = f"Analyze the data in {filepath} and provide insights"
        return await self.orchestrator.execute([
            {"role": "user", "content": prompt}
        ])

# Ready to analyze!
analyzer = DataAnalyzer("your-api-key")
insights = await analyzer.analyze("sales_data.csv")

3. Content Generation System (2 minutes)

# Rapid content generation setup
from agentrouter import ManagerAgent

class ContentSystem:
    def __init__(self, api_key):
        # Create content team in seconds
        self.editor = ManagerAgent(
            name="chief_editor",
            api_key=api_key,
            goal="Produce high-quality content"
        )
        
        # Add writers
        self.blog_writer = self.editor.create_worker(
            name="Blog_Writer",
            role="Write engaging blog posts"
        )
        
        self.social_media = self.editor.create_worker(
            name="Social_Media_Manager",
            role="Create social media content"
        )
        
        self.seo_optimizer = self.editor.create_worker(
            name="SEO_Specialist",
            role="Optimize content for search"
        )
    
    async def create_content(self, topic, content_type="blog"):
        prompt = f"Create a {content_type} about {topic}"
        return await self.editor.execute([
            {"role": "user", "content": prompt}
        ])

# Start creating content immediately
content = ContentSystem("your-api-key")
blog_post = await content.create_content("AI trends 2025", "blog")
tweet = await content.create_content("AI trends 2025", "tweet")

Rapid Testing Strategies

1. Interactive Testing Loop

# Quick interactive testing setup
from agentrouter import ManagerAgent
import asyncio

class InteractiveTester:
    def __init__(self, api_key):
        self.agent = ManagerAgent(
            name="test_agent",
            api_key=api_key,
            goal="Test various scenarios"
        )
        
    async def test_loop(self):
        print("Interactive Agent Tester")
        print("Type 'exit' to quit\n")
        
        while True:
            user_input = input("You: ")
            if user_input.lower() == 'exit':
                break
            
            response = await self.agent.execute([
                {"role": "user", "content": user_input}
            ])
            
            print(f"Agent: {response['choices'][0]['message']['content']}\n")

# Start testing immediately
tester = InteractiveTester("your-api-key")
asyncio.run(tester.test_loop())

2. Batch Testing

# Test multiple scenarios quickly
test_cases = [
    "What's the weather like?",
    "Book a flight to Paris",
    "Translate 'hello' to Spanish",
    "Calculate 15% tip on $85.50"
]

async def batch_test(agent, test_cases):
    results = []
    for test in test_cases:
        start = time.time()
        response = await agent.execute([
            {"role": "user", "content": test}
        ])
        results.append({
            "input": test,
            "output": response['choices'][0]['message']['content'],
            "time": time.time() - start
        })
    return results

# Run all tests
results = await batch_test(agent, test_cases)
for r in results:
    print(f"Q: {r['input']}")
    print(f"A: {r['output'][:100]}...")
    print(f"Time: {r['time']:.2f}s\n")

From Prototype to Production

Step 1: Prototype (5 minutes)

# Quick and dirty prototype
prototype = ManagerAgent(
    name="prototype",
    api_key="key",
    goal="Test the concept"
)

Step 2: Add Structure (10 minutes)

# Add configuration and error handling
class StructuredAgent:
    def __init__(self, config):
        self.agent = ManagerAgent(**config)
        self.setup_error_handling()
    
    def setup_error_handling(self):
        self.agent.on_error = self.handle_error
    
    async def handle_error(self, error):
        # Log and recover
        print(f"Error: {error}")
        return {"error": str(error), "status": "failed"}

Step 3: Add Monitoring (15 minutes)

# Add basic monitoring
class MonitoredAgent(StructuredAgent):
    def __init__(self, config):
        super().__init__(config)
        self.metrics = {
            "requests": 0,
            "successes": 0,
            "failures": 0,
            "total_time": 0
        }
    
    async def execute(self, messages):
        start = time.time()
        self.metrics["requests"] += 1
        
        try:
            result = await self.agent.execute(messages)
            self.metrics["successes"] += 1
            return result
        except Exception as e:
            self.metrics["failures"] += 1
            raise
        finally:
            self.metrics["total_time"] += time.time() - start

Step 4: Add Persistence (20 minutes)

# Add data persistence
import json
from pathlib import Path

class PersistentAgent(MonitoredAgent):
    def __init__(self, config, storage_path="agent_data"):
        super().__init__(config)
        self.storage_path = Path(storage_path)
        self.storage_path.mkdir(exist_ok=True)
        self.load_state()
    
    def save_state(self):
        state = {
            "metrics": self.metrics,
            "config": self.agent.to_dict(),
            "timestamp": datetime.now().isoformat()
        }
        
        with open(self.storage_path / "state.json", "w") as f:
            json.dump(state, f)
    
    def load_state(self):
        state_file = self.storage_path / "state.json"
        if state_file.exists():
            with open(state_file) as f:
                state = json.load(f)
                self.metrics = state.get("metrics", self.metrics)

Tips for Rapid Prototyping

1. Start Simple, Iterate Fast

# Don't do this:
complex_agent = ManagerAgent(
    name="super_complex_agent",
    backstory="200 lines of backstory...",
    # 50 parameters...
)

# Do this:
simple_agent = ManagerAgent(name="agent", api_key="key")
# Then iterate based on results

2. Use Mock Data First

# Mock external dependencies
@tool
async def get_data(id: str):
    # Start with mock data
    return {"id": id, "data": "mock"}
    
    # Later replace with real API
    # return await real_api.get(id)

3. Leverage Templates

# Create reusable templates
def create_customer_service_agent(api_key, company_name):
    return ManagerAgent(
        name=f"{company_name}_support",
        api_key=api_key,
        backstory=f"I represent {company_name} customer support",
        goal="Provide excellent customer service",
        temperature=0.7
    )

# Instant agents for different companies
agent1 = create_customer_service_agent(key, "TechCorp")
agent2 = create_customer_service_agent(key, "RetailCo")

4. Progressive Enhancement

# Start minimal
agent = ManagerAgent(name="bot", api_key=key)

# Add features as needed
if needs_memory:
    agent.memory = LongTermMemory()

if needs_tools:
    agent.register_tool(my_tool)

if needs_workers:
    agent.create_worker(name="Helper")

Common Prototyping Patterns

The Swiss Army Knife

One agent with many tools:

versatile_agent = ManagerAgent(name="swiss", api_key=key)
for tool in [search, calculate, translate, summarize]:
    versatile_agent.register_tool(tool)

The Assembly Line

Sequential processing:

agents = []
for step in ["validate", "process", "format", "deliver"]:
    agent = ManagerAgent(name=f"{step}_agent", api_key=key)
    agents.append(agent)

# Chain them together
async def pipeline(data):
    for agent in agents:
        data = await agent.execute(data)
    return data

The Committee

Multiple agents vote on decisions:

committee = [
    ManagerAgent(name=f"member_{i}", api_key=key)
    for i in range(5)
]

async def committee_decision(question):
    votes = []
    for member in committee:
        response = await member.execute([
            {"role": "user", "content": question}
        ])
        votes.append(response)
    return majority_vote(votes)

Debugging Your Prototypes

Quick Debug Wrapper

class DebugAgent:
    def __init__(self, agent):
        self.agent = agent
        self.history = []
    
    async def execute(self, messages):
        print(f"[DEBUG] Input: {messages}")
        
        start = time.time()
        result = await self.agent.execute(messages)
        duration = time.time() - start
        
        print(f"[DEBUG] Output: {result}")
        print(f"[DEBUG] Duration: {duration:.2f}s")
        
        self.history.append({
            "input": messages,
            "output": result,
            "duration": duration
        })
        
        return result

# Wrap any agent for debugging
debug_agent = DebugAgent(your_agent)

Conclusion

Rapid prototyping with AgentRouter transforms AI development from a months-long endeavor into a matter of minutes. By starting simple and iterating quickly, you can test ideas, validate concepts, and build working systems faster than ever before.

The key is to embrace the iterative process:

1. Start with the simplest possible implementation
2. Test immediately with real scenarios
3. Add complexity only as needed
4. Keep what works, discard what doesn't
5. Ship early and often

With AgentRouter, the gap between idea and implementation has never been smaller. What will you build in your next 5 minutes?

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Start prototyping now: Check out our Quick Start Guide.

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