Cerebras Analysis & Intelligence

The Cerebras Agent represents the "brain" of your multi-agent system, leveraging powerful cloud-based AI models for complex analysis, advanced reasoning, and sophisticated problem-solving. In this lab, you'll explore its capabilities and learn to harness its full potential.

!!! info "Learning Focus" Master Cerebras cloud AI integration, advanced analysis capabilities, and intelligent request handling for complex scenarios.

Understanding Cerebras Technology

🧠 What Makes Cerebras Special

Cerebras Systems creates the world's largest computer chips specifically designed for AI:

• Massive Scale: 850,000 AI cores on a single chip
• High Speed: 20x faster than traditional GPUs for AI workloads
• Large Models: Support for models with 70B+ parameters
• Efficient Processing: Optimized for transformer architectures

🌐 Cerebras Cloud Models

Available models through the API:

Model	Parameters	Best For
Llama 3.1 8B	8 billion	Quick analysis, code generation
Llama 3.1 70B	70 billion	Complex reasoning, detailed analysis
Llama 3.1 405B	405 billion	Advanced research, complex problem solving

Quick Test

Prompt 1: Can I talk to Cerebras?

Prompt 2: Search documentation for Express Routing

Prompt 3: Create a function that adds two numbers and test it in the sandbox

Hands-On Cerebras Agent Exploration

🎯 Exercise 1: Complex Analysis Tasks

Let's test scenarios that specifically benefit from Cerebras' advanced capabilities:

Advanced Code Analysis

Complex Algorithm Analysis:

You: Analyze this sorting algorithm for time/space complexity and suggest optimizations:

def complex_sort(arr, key_func=None, reverse=False):
    if not arr:
        return arr

    if len(arr) == 1:
        return arr

    # Multi-dimensional sorting with custom key
    if key_func:
        decorated = [(key_func(item), i, item) for i, item in enumerate(arr)]
    else:
        decorated = [(item, i, item) for i, item in enumerate(arr)]

    # Recursive merge sort implementation
    def merge_sort(lst):
        if len(lst) <= 1:
            return lst

        mid = len(lst) // 2
        left = merge_sort(lst[:mid])
        right = merge_sort(lst[mid:])

        return merge(left, right, reverse)

    def merge(left, right, rev):
        result = []
        i = j = 0

        while i < len(left) and j < len(right):
            if (left[i][0] <= right[j][0]) != rev:
                result.append(left[i])
                i += 1
            else:
                result.append(right[j])
                j += 1

        result.extend(left[i:])
        result.extend(right[j:])
        return result

    sorted_decorated = merge_sort(decorated)
    return [item for key, original_index, item in sorted_decorated]

Expected Cerebras Response: - Detailed complexity analysis (O(n log n) time, O(n) space) - Identification of the Schwartzian transform pattern - Suggestions for optimization and edge case handling - Comparison with built-in sorting methods

Architecture Design

System Architecture Challenge:

You: Design a microservices architecture for a real-time collaborative document editing platform like Google Docs. Consider scalability, consistency, conflict resolution, and real-time synchronization. Include specific technologies, data flow, and handling of 100,000+ concurrent users.

Monitor the Processing:

# Watch for Cerebras API calls in logs
docker compose logs -f devduck-agent | grep -i "cerebras\|api\|routing"

🚀 Exercise 2: Multi-Step Reasoning

Complex Problem Solving

Business Analysis Scenario:

You: A startup has the following metrics:
- Monthly users: 50,000 (growing 15% monthly)
- Conversion rate: 2.5%
- Average revenue per user: $25/month
- Churn rate: 5% monthly
- Customer acquisition cost: $30

Analyze the business model viability, calculate key metrics like LTV:CAC ratio, identify potential issues, and recommend strategies for improvement. Project the business trajectory over 24 months.

Technical Deep Dive:

You: Explain how blockchain consensus mechanisms work, compare Proof of Work vs Proof of Stake vs Delegated Proof of Stake, analyze their trade-offs in terms of security, scalability, and energy efficiency, and suggest which would be best for a new cryptocurrency focused on micropayments.

📊 Exercise 3: Cerebras Performance Analysis

Response Quality Comparison

Create a systematic comparison between Local Agent and Cerebras Agent:

# Create cerebras_comparison.py
import requests
import time
import json

def compare_agents():
    """Compare Local vs Cerebras Agent responses."""

    test_scenarios = [
        {
            "query": "Explain recursion in programming",
            "complexity": "medium",
            "expected_cerebras": "detailed_explanation"
        },
        {
            "query": "Design a distributed caching system for a global application",
            "complexity": "high", 
            "expected_cerebras": "architectural_analysis"
        },
        {
            "query": "Analyze the time complexity of this nested loop algorithm",
            "complexity": "high",
            "expected_cerebras": "mathematical_analysis"
        }
    ]

    results = []

    for scenario in test_scenarios:
        print(f"\n🧪 Testing: {scenario['query'][:50]}...")

        # Test with system (should route to appropriate agent)
        start_time = time.time()

        response = requests.post(
            'http://localhost:8000/chat',
            json={
                "message": scenario['query'],
                "conversation_id": f"compare-{int(time.time())}"
            },
            timeout=60
        )

        duration = time.time() - start_time

        if response.status_code == 200:
            response_data = response.json()
            response_length = len(response_data.get('response', ''))

            results.append({
                "query": scenario['query'][:50],
                "complexity": scenario['complexity'],
                "duration": duration,
                "response_length": response_length,
                "success": True
            })

            print(f"  ✅ Duration: {duration:.2f}s")
            print(f"  📝 Response length: {response_length} chars")
        else:
            print(f"  ❌ Failed: {response.status_code}")
            results.append({
                "query": scenario['query'][:50],
                "complexity": scenario['complexity'],
                "duration": duration,
                "success": False
            })

    # Analysis
    print("\n📊 Comparison Results:")
    for result in results:
        if result['success']:
            print(f"  {result['complexity']:6} | {result['duration']:6.2f}s | {result['response_length']:4d} chars | {result['query']}")

    return results

if __name__ == '__main__':
    compare_agents()

Run the comparison:

python cerebras_comparison.py

Advanced Cerebras Features

🎛️ Model Selection and Optimization

Dynamic Model Selection

Configure different models for different scenarios:

# Create model configuration
cat > cerebras_config.py << 'EOF'
class CerebrasModelConfig:
    """Configuration for different Cerebras models."""

    MODELS = {
        "fast": {
            "name": "llama3.1-8b",
            "max_tokens": 1000,
            "temperature": 0.7,
            "use_case": "Quick analysis, code generation"
        },
        "balanced": {
            "name": "llama3.1-70b", 
            "max_tokens": 2000,
            "temperature": 0.5,
            "use_case": "Complex reasoning, detailed analysis"
        },
        "advanced": {
            "name": "llama3.1-405b",
            "max_tokens": 4000,
            "temperature": 0.3,
            "use_case": "Research, complex problem solving"
        }
    }

    @classmethod
    def get_model_for_complexity(cls, complexity_score):
        """Select model based on query complexity."""
        if complexity_score < 0.3:
            return cls.MODELS["fast"]
        elif complexity_score < 0.7:
            return cls.MODELS["balanced"]
        else:
            return cls.MODELS["advanced"]

    @classmethod
    def estimate_complexity(cls, query):
        """Estimate query complexity (simplified heuristic)."""
        complexity_indicators = [
            'analyze', 'design', 'architecture', 'optimize',
            'compare', 'evaluate', 'research', 'complex',
            'algorithm', 'system', 'scalable', 'distributed'
        ]

        query_lower = query.lower()
        matches = sum(1 for indicator in complexity_indicators 
                     if indicator in query_lower)

        base_complexity = len(query) / 1000  # Length factor
        keyword_complexity = matches * 0.2   # Keyword factor

        return min(base_complexity + keyword_complexity, 1.0)

# Example usage
query = "Design a distributed microservices architecture for real-time data processing"
complexity = CerebrasModelConfig.estimate_complexity(query)
model = CerebrasModelConfig.get_model_for_complexity(complexity)

print(f"Query complexity: {complexity:.2f}")
print(f"Recommended model: {model['name']}")
print(f"Use case: {model['use_case']}")
EOF

python cerebras_config.py

🔧 Advanced Configuration

Temperature and Parameter Tuning

# Create cerebras_tuning.py
import requests
import json

def test_temperature_effects():
    """Test how temperature affects Cerebras responses."""

    query = "Explain the concept of machine learning in one paragraph."
    temperatures = [0.1, 0.5, 0.9]

    print("🌡️ Testing Temperature Effects on Cerebras:")

    for temp in temperatures:
        print(f"\nTesting temperature: {temp}")

        # Note: This is a conceptual example
        # Actual temperature control would be implemented in the agent
        response = requests.post(
            'http://localhost:8000/chat',
            json={
                "message": f"[TEMPERATURE={temp}] {query}",
                "conversation_id": f"temp-test-{temp}"
            }
        )

        if response.status_code == 200:
            resp_data = response.json()
            resp_text = resp_data.get('response', '')[:200]
            print(f"Response preview: {resp_text}...")

        time.sleep(2)  # Rate limiting

def test_max_tokens_impact():
    """Test how max_tokens affects response completeness."""

    query = "Provide a comprehensive guide to setting up a CI/CD pipeline with Docker, including best practices and security considerations."

    print("\n📏 Testing Max Tokens Impact:")

    # This would be configured in the agent settings
    max_tokens_settings = [500, 1500, 3000]

    for max_tokens in max_tokens_settings:
        print(f"\nTesting max_tokens: {max_tokens}")

        response = requests.post(
            'http://localhost:8000/chat', 
            json={
                "message": f"[MAX_TOKENS={max_tokens}] {query}",
                "conversation_id": f"tokens-test-{max_tokens}"
            }
        )

        if response.status_code == 200:
            resp_data = response.json()
            response_length = len(resp_data.get('response', ''))
            print(f"Response length: {response_length} characters")

if __name__ == '__main__':
    test_temperature_effects()
    test_max_tokens_impact()

Specialized Use Cases

📈 Business Intelligence and Analysis

Exercise 4: Market Analysis

Complex Business Scenario:

You: A SaaS company is considering expanding to the European market. They currently serve 10,000 customers in North America with $2M ARR. Analyze the key factors they should consider for European expansion:

1. Market analysis and sizing
2. Regulatory considerations (GDPR, data localization)
3. Technical infrastructure requirements
4. Competitive landscape
5. Go-to-market strategy
6. Financial projections and investment requirements
7. Risk assessment and mitigation strategies

Provide a comprehensive expansion plan with timeline and milestones.

🏗️ Technical Architecture and Design

Exercise 5: System Design Challenge

Scalability Challenge:

You: Design a chat system like Discord that can handle:
- 10 million concurrent users
- Real-time messaging with <100ms latency
- File sharing up to 100MB
- Voice/video calling
- Message history and search
- Mobile and web clients

Include:
- Complete system architecture diagram
- Technology stack recommendations
- Database design and sharding strategy
- Caching and CDN approach
- Real-time communication protocols
- Scalability and reliability considerations
- Security and privacy measures
- Deployment and monitoring strategy

🔬 Research and Analysis

Exercise 6: Technical Research

Emerging Technology Analysis:

You: Compare quantum computing approaches for solving optimization problems:

1. Gate-based quantum computers (IBM, Google)
2. Quantum annealing (D-Wave)
3. Photonic quantum computing (Xanadu)
4. Trapped ion systems (IonQ)

Analyze:
- Current capabilities and limitations
- Suitable problem types for each approach
- Commercial viability timeline
- Integration challenges with classical systems
- Cost-benefit analysis for enterprise adoption
- Future development roadmaps

Provide recommendations for a logistics company considering quantum optimization for route planning.

Monitoring and Optimization

📊 Cerebras Performance Monitoring

Create Monitoring Dashboard

# Create cerebras_monitor.py
import requests
import time
import statistics
from datetime import datetime

class CerebrasMonitor:
    def __init__(self):
        self.api_calls = []
        self.response_times = []
        self.token_usage = []

    def log_api_call(self, duration, tokens_used, success=True):
        """Log an API call for monitoring."""
        self.api_calls.append({
            'timestamp': datetime.now(),
            'duration': duration,
            'tokens_used': tokens_used,
            'success': success
        })

        if success:
            self.response_times.append(duration)
            self.token_usage.append(tokens_used)

    def get_stats(self):
        """Get comprehensive statistics."""
        if not self.api_calls:
            return {"message": "No API calls recorded"}

        successful_calls = [call for call in self.api_calls if call['success']]

        return {
            'total_calls': len(self.api_calls),
            'successful_calls': len(successful_calls),
            'success_rate': len(successful_calls) / len(self.api_calls),
            'avg_response_time': statistics.mean(self.response_times) if self.response_times else 0,
            'median_response_time': statistics.median(self.response_times) if self.response_times else 0,
            'avg_tokens_used': statistics.mean(self.token_usage) if self.token_usage else 0,
            'total_tokens_used': sum(self.token_usage),
            'calls_per_minute': self._calculate_rate(),
            'uptime_minutes': self._calculate_uptime()
        }

    def _calculate_rate(self):
        if len(self.api_calls) < 2:
            return 0

        first_call = self.api_calls[0]['timestamp']
        last_call = self.api_calls[-1]['timestamp']
        duration_minutes = (last_call - first_call).total_seconds() / 60

        return len(self.api_calls) / max(duration_minutes, 1)

    def _calculate_uptime(self):
        if not self.api_calls:
            return 0

        first_call = self.api_calls[0]['timestamp']
        return (datetime.now() - first_call).total_seconds() / 60

    def print_dashboard(self):
        """Print a monitoring dashboard."""
        stats = self.get_stats()

        if 'message' in stats:
            print(stats['message'])
            return

        print("🧠 Cerebras Agent Dashboard")
        print("============================")
        print(f"Total Calls: {stats['total_calls']}")
        print(f"Success Rate: {stats['success_rate']:.1%}")
        print(f"Avg Response Time: {stats['avg_response_time']:.2f}s")
        print(f"Median Response Time: {stats['median_response_time']:.2f}s")
        print(f"Avg Tokens/Call: {stats['avg_tokens_used']:.0f}")
        print(f"Total Tokens Used: {stats['total_tokens_used']}")
        print(f"Rate: {stats['calls_per_minute']:.1f} calls/min")
        print(f"Uptime: {stats['uptime_minutes']:.1f} minutes")

# Usage example
monitor = CerebrasMonitor()

# Simulate monitoring
test_queries = [
    "Simple explanation of Docker",
    "Complex microservices architecture design", 
    "Algorithm analysis and optimization"
]

for query in test_queries:
    start_time = time.time()

    # Simulate API call
    try:
        response = requests.post(
            'http://localhost:8000/chat',
            json={
                "message": query,
                "conversation_id": f"monitor-{int(time.time())}"
            },
            timeout=30
        )

        duration = time.time() - start_time

        # Estimate token usage (simplified)
        tokens_used = len(query.split()) + 100  # Rough estimate

        success = response.status_code == 200
        monitor.log_api_call(duration, tokens_used, success)

        print(f"✅ Processed: {query[:40]}... ({duration:.2f}s)")

    except Exception as e:
        duration = time.time() - start_time
        monitor.log_api_call(duration, 0, False)
        print(f"❌ Failed: {query[:40]}... ({e})")

    time.sleep(1)

monitor.print_dashboard()

Run the monitor:

python cerebras_monitor.py

💰 Cost Optimization

Token Usage Optimization

# Create cost_optimizer.py
class CebrasCostOptimizer:
    # Approximate pricing (check current Cerebras pricing)
    TOKEN_COSTS = {
        "llama3.1-8b": 0.00002,    # $0.02 per 1K tokens
        "llama3.1-70b": 0.00008,   # $0.08 per 1K tokens  
        "llama3.1-405b": 0.0002,   # $0.20 per 1K tokens
    }

    def __init__(self):
        self.usage_history = []

    def estimate_cost(self, model, input_tokens, output_tokens):
        """Estimate cost for a request."""
        total_tokens = input_tokens + output_tokens
        cost_per_token = self.TOKEN_COSTS.get(model, 0.0001)
        return total_tokens * cost_per_token

    def suggest_optimization(self, query):
        """Suggest cost optimization strategies."""
        suggestions = []

        # Check query length
        if len(query.split()) > 500:
            suggestions.append("Consider breaking long queries into smaller parts")

        # Check complexity
        if any(word in query.lower() for word in ['comprehensive', 'detailed', 'complete', 'thorough']):
            suggestions.append("Consider using a smaller model for initial analysis")

        # Check for code analysis
        if 'analyze' in query.lower() and len(query) > 1000:
            suggestions.append("Pre-process code analysis with Local Agent first")

        return suggestions

    def daily_cost_estimate(self, daily_queries, avg_tokens_per_query, model="llama3.1-70b"):
        """Estimate daily costs."""
        daily_tokens = daily_queries * avg_tokens_per_query
        cost_per_token = self.TOKEN_COSTS.get(model, 0.0001)

        daily_cost = daily_tokens * cost_per_token
        monthly_cost = daily_cost * 30
        annual_cost = daily_cost * 365

        return {
            'daily_cost': daily_cost,
            'monthly_cost': monthly_cost,
            'annual_cost': annual_cost,
            'tokens_per_day': daily_tokens
        }

# Usage
optimizer = CebrasCostOptimizer()

# Estimate costs for different usage patterns
scenarios = [
    {"name": "Light Usage", "queries": 50, "tokens": 500},
    {"name": "Medium Usage", "queries": 200, "tokens": 1000},
    {"name": "Heavy Usage", "queries": 1000, "tokens": 2000}
]

print("💰 Cerebras Cost Analysis")
print("==========================")

for scenario in scenarios:
    costs = optimizer.daily_cost_estimate(
        scenario['queries'], 
        scenario['tokens']
    )

    print(f"\n{scenario['name']}:")
    print(f"  Daily: ${costs['daily_cost']:.2f}")
    print(f"  Monthly: ${costs['monthly_cost']:.2f}")
    print(f"  Annual: ${costs['annual_cost']:.2f}")

Best Practices and Tips

🎯 Optimization Strategies

1. Query Design:
2. Be specific and clear in your prompts
3. Use structured formats for complex requests

4. Provide context but avoid redundancy

5. Model Selection:

6. Use 8B model for quick analysis
7. Use 70B model for complex reasoning

8. Reserve 405B model for research-level tasks

9. Error Handling:

10. Implement retries with exponential backoff
11. Have fallback to Local Agent for failures

12. Monitor API rate limits

13. Performance Tuning:

14. Cache common responses
15. Batch similar queries when possible
16. Monitor response times and adjust timeouts

🚨 Common Pitfalls to Avoid

• Over-reliance: Don't route every query to Cerebras
• Context Bloat: Avoid sending unnecessary context
• Poor Error Handling: Always handle API failures gracefully
• Ignoring Rate Limits: Respect API quotas and limits
• Cost Blindness: Monitor token usage and costs

Next Steps

Excellent! You've mastered:

• ✅ Cerebras AI capabilities and model selection
• ✅ Complex analysis and reasoning tasks
• ✅ Performance monitoring and optimization
• ✅ Cost management and token usage
• ✅ Best practices for cloud AI integration

In the next section, you'll learn about agent routing and communication patterns - how DevDuck decides which agent to use and how agents can work together on complex multi-step tasks.

Ready to explore intelligent agent coordination? Let's dive in! 🤖🔄