Skip to content

🎯📝⚙️ Session 1: Bare Metal Agents Hub - Building from First Principles

When your data engineering team processes petabyte-scale datasets through cloud-native ETL pipelines, framework abstractions can mask critical bottlenecks that impact both processing costs and pipeline reliability. Modern data platforms handle thousands of concurrent data streams from IoT sensors, application logs, and real-time analytics systems - requiring transparent control over resource allocation, API costs, and execution flow to maintain the SLA requirements that data-driven businesses demand.

This session reveals how to build AI agents from fundamental components - pure Python and direct LLM API calls - providing the precise control essential for production-scale data engineering. You'll learn to construct intelligent systems that can orchestrate complex data workflows, optimize cloud resource utilization, and ensure compliance with data governance standards.

Understanding bare metal implementation becomes your competitive advantage when managing distributed data pipelines that process streaming data lakes, validate data quality across massive datasets, and coordinate complex cloud workloads across multiple availability zones.

🎯📝⚙️ Learning Path Overview

This session offers three distinct learning paths designed to match your goals and time investment:

Focus: Understanding concepts and architecture

Activities: Core agent patterns, basic implementation concepts, architectural foundations

Ideal for: Decision makers, architects, overview learners

Focus: Guided implementation and analysis

Activities: Complete implementations, production considerations, real-world patterns

Ideal for: Developers, technical leads, hands-on learners

Focus: Complete implementation and customization

Activities: Advanced patterns, specialized modules, enterprise-scale systems

Ideal for: Senior engineers, architects, specialists

🎯 Observer Path: Bare Metal Agent Fundamentals

Code Repository: src/session1/ Quick Start: cd src/session1 && python base_agent_course.py Complete Demo: cd src/session1 && python demo_runner_course.py

Learning Outcomes

By the end of this path, you will understand:

  • Core agent architecture patterns using Python and LLM APIs
  • The five fundamental agentic patterns and their applications
  • Model-application layer separation for production systems
  • Integration strategies with cloud data services
  • Cost optimization approaches for enterprise-scale processing

The Bare Metal Approach: Essential for Data Pipeline Control

Technical Context & Requirements

Consider the challenge facing data engineers today: A single modern application can generate hundreds of gigabytes of log data per day, multiplied by microservices architectures spanning thousands of containers. Add real-time streaming data from IoT devices, user analytics, and business metrics, and you're orchestrating petabyte-scale processing workflows that must maintain strict SLA requirements for business-critical analytics. Framework abstractions often obscure the resource consumption patterns that directly impact your cloud spending and processing latency.

When processing real-time streaming data for fraud detection, or coordinating ETL pipelines across distributed Kubernetes clusters, you need granular visibility into every API call, memory allocation, and processing decision. The bare metal approach eliminates the abstraction layer that can hide critical performance bottlenecks in your data pipeline.

Your production environment demands predictable resource allocation within Kubernetes pods, transparent cost tracking across thousands of LLM API calls, and complete observability through Grafana dashboards. This level of control becomes essential when a single processing error could cascade through dependent systems and impact critical business reporting.

Core Knowledge & Applications

You'll master the fundamental patterns that enable intelligent orchestration of data engineering workflows. These patterns integrate naturally with your existing infrastructure: Apache Kafka for high-throughput data ingestion, PostgreSQL for metadata management, S3 for petabyte-scale storage, and Elasticsearch for rapid search across billions of records.

This knowledge empowers you to build agents that intelligently route data through appropriate processing pipelines based on schema analysis, optimize batch processing sizes for cost efficiency while maintaining throughput requirements, and automatically coordinate failure recovery across distributed data processing workloads.

Architectural Separation

The model-application layer separation becomes critical when managing costs across petabyte-scale processing. Your model layer handles intelligent decisions about data routing strategies and resource optimization, while your application layer executes concrete operations through Kubernetes jobs, database transactions, and cloud service APIs. This separation allows you to dynamically switch between different LLM providers based on real-time cost analysis without disrupting your core pipeline logic.

Real-World Data Processing Applications

Engineering teams leverage bare metal agents for:

  • Pipeline Orchestration: Agents that analyze incoming data characteristics and route processing through optimal transformation workflows
  • Quality Assurance: Systems that detect data anomalies in streaming pipelines and trigger automated data cleansing workflows
  • Cost Optimization: Agents that monitor processing patterns and dynamically adjust resource allocation to meet budget constraints
  • Compliance Monitoring: Systems ensuring all data handling adheres to GDPR requirements and industry data governance standards

Core Implementation: Building Intelligence from First Principles

Part 1: Agent Architecture Fundamentals

Basic Agent Structure

Every data processing agent requires these core components, engineered for cloud-native deployment at scale:

Agent Pattern Control

File: src/session1/base_agent_course.py

📁 Course Implementation: The complete implementation matching this course content is available in base_agent_course.py. The original base_agent.py contains a different implementation focusing on general agent architecture patterns.

This foundation class defines essential capabilities while operating within cloud resource constraints. The memory management system respects Kubernetes pod limits while maintaining processing context, and the tool registry provides seamless integration with your existing cloud infrastructure including S3 data lakes, RDS metadata stores, and Kafka message streams.

Agent Architecture Foundation - BaseAgent Class

The BaseAgent class provides the architectural blueprint for data processing agents, solving the fundamental challenge of intelligent orchestration within distributed cloud infrastructure:

class BaseAgent:
    """Foundation class for data pipeline agent implementations"""
    def __init__(self, model_name="gpt-4", max_memory_mb=512):
        self.model_name = model_name      # LLM endpoint configuration
        self.memory = []                  # Processing context
        self.tools = {}                   # Cloud service integrations
        self.max_memory_mb = max_memory_mb  # Pod memory limit
        self.metrics_client = self._init_metrics()  # Prometheus metrics

This BaseAgent foundation provides the core architecture needed for data processing agents operating in cloud-native environments. The model_name allows dynamic switching between different LLM providers based on cost and performance requirements, while max_memory_mb ensures the agent operates within Kubernetes pod constraints.

Memory System Design: The memory system maintains processing context within Kubernetes pod constraints while caching critical metadata about data characteristics, recent processing decisions, and error patterns. This approach mirrors how experienced data engineers mentally track pipeline state across multiple concurrent workflow executions, enabling the agent to make informed decisions based on historical processing patterns.

Tool Registry Pattern: The tools registry connects your agent with essential cloud services - S3 for raw data storage, PostgreSQL for metadata and processing results, Kafka for real-time data streaming, and Argo Workflows for complex processing orchestration. Each integration includes comprehensive retry logic and circuit breaker patterns essential for maintaining reliability in distributed data systems.

Agent Execution Loop - Cloud-Native Processing Pipeline

The execution loop implements a production-ready processing pipeline designed for the demands of modern data engineering:

    def run(self, input_data: dict, timeout_seconds: int = 30) -> dict:
        """Processing cycle with cloud resource management and cost optimization"""
        try:
            # Track API costs and latency for budget management
            with self.metrics_client.timer('agent_processing_time'):
                processed_input = self.process_input(input_data)
                action_plan = self.decide_action(processed_input)
                result = self.execute_action(action_plan)

The main execution loop wraps all processing in performance monitoring to track latency and resource usage. This visibility is crucial for optimizing data pipeline performance and managing costs in production environments where agents process thousands of requests daily.

                # Track LLM API costs for enterprise-scale processing
                self.metrics_client.increment('llm_api_calls',
                    tags=['model:' + self.model_name])

                return self.format_response(result)
        except TimeoutError:
            return self.handle_timeout(input_data)

API call tracking with model tags enables detailed cost analysis across different LLM providers. The timeout handling ensures agents don't hang indefinitely, maintaining system responsiveness even when processing complex data transformation tasks.

Execution Flow Context: Each processing step includes comprehensive monitoring designed for enterprise data scale requirements. The metrics integration provides real-time visibility through Grafana dashboards, enabling your team to balance processing speed against API costs while maintaining the throughput necessary for business-critical analytics cycles.

Key Concepts

These concepts form the foundation of reliable data processing:

  1. Model Interface: Sophisticated LLM API management with rate limiting and cost tracking across petabyte-scale operations
  2. Memory Management: Efficient context caching within pod memory constraints while maintaining processing history
  3. Tool Registry: Standardized integration framework for cloud services and data stores

Input/Output Handling

Input Processing Pipeline - Multi-Format Data Handling

Data processing agents must seamlessly handle the diverse data formats generated by modern distributed systems:

    def process_input(self, data: Union[str, dict, bytes, pd.DataFrame]) -> dict:
        """Standardize input from data sources"""
        if isinstance(data, bytes):  # Binary data streams (Protobuf, Avro)
            return self.parse_binary_format(data)
        elif isinstance(data, pd.DataFrame):  # Structured analytics results
            return self.extract_dataframe_features(data)

The input processing handles diverse data formats common in modern data engineering environments. Binary formats like Protobuf and Avro are standard for high-throughput data streams, while DataFrames represent processed analytics results from upstream pipeline stages.

        elif isinstance(data, dict) and 'stream_protocol' in data:  # Real-time data streams
            return self.convert_stream_format(data)
        else:  # Natural language queries from data analysts
            return {"type": "nl_query", "content": data}

Real-time streaming data requires special handling to extract relevant metadata and processing instructions. Natural language queries from data analysts are normalized into a standard format, enabling agents to process both structured data and human requests through the same pipeline.

Pattern Implementation - Resilient Cloud Processing

Each processing pattern addresses the unique challenges of distributed data systems:

    def decide_action(self, input_data: dict) -> dict:
        """Intelligent decision making with enterprise-scale cost optimization"""
        # Estimate processing cost based on data complexity and throughput requirements
        estimated_cost = self.estimate_processing_cost(input_data)

Cost estimation analyzes the data complexity, expected processing time, and throughput requirements to predict LLM API costs. This enables intelligent model selection based on budget constraints while maintaining processing quality.

        if estimated_cost > self.cost_threshold:
            # Use efficient model for routine data validation
            decision = self.llm_inference(input_data, model="gpt-3.5-turbo")
        else:
            # Use advanced model for complex data transformation analysis
            decision = self.llm_inference(input_data, model=self.model_name)

Dynamic model selection balances cost and capability - routine data validation tasks use cost-effective models, while complex transformations requiring sophisticated reasoning use premium models. This optimization can reduce API costs by 60-80% in production environments.

        # Validate against data governance and compliance requirements
        if not self.validate_compliance(decision):
            decision = self.get_compliant_alternative()

        return decision

Compliance validation ensures all processing decisions align with data governance policies, privacy regulations, and industry standards. When violations are detected, the system automatically generates compliant alternatives rather than failing the entire processing pipeline.

Part 2: The Five Fundamental Agentic Patterns

Agentic Patterns Overview

These five patterns form the intelligence foundation for data processing systems:

Agentic Patterns

  1. Reflection: Self-monitoring and optimization for pipeline performance
  2. Tool Use: Integration with cloud data infrastructure and processing services
  3. Planning: Workflow orchestration and resource allocation for data pipelines
  4. ReAct: Dynamic response to data anomalies and processing failures
  5. Multi-Agent: Coordination across distributed data processing nodes

Pattern 1: Reflection - Pipeline Performance Optimization

Implementation File: src/session1/reflection_agent_course.py

Concept

In data processing, reflection enables agents to continuously analyze their performance and optimize pipeline efficiency based on real processing metrics:

class ReflectiveAgent(BaseAgent):
    """Agent with self-monitoring for data pipeline optimization"""

    def __init__(self, model_name="gpt-4"):
        super().__init__(model_name)
        self.performance_history = []
        self.cost_tracker = CostTracker()
        self.optimization_threshold = 0.8

The ReflectiveAgent extends BaseAgent with self-monitoring capabilities essential for data pipeline optimization. The performance_history tracks processing efficiency across different data types and volumes, while the cost_tracker provides detailed insights into resource utilization patterns that inform optimization decisions.

Implementation

The reflection mechanism monitors processing efficiency across data workflows and triggers intelligent optimizations:

    def reflect_on_performance(self, metrics: dict) -> dict:
        """Analyze data processing performance and optimize workflows"""
        reflection_prompt = f"""
        Analyze this data processing performance:
        - Throughput: {metrics['throughput_gb_per_hour']} GB/hour of data processed
        - Cost efficiency: ${metrics['cost_per_gb']} per GB processed
        - Error rate: {metrics['error_rate']}% across ETL pipelines
        - Queue depth: {metrics['queue_depth']} pending processing jobs
        - Average latency: {metrics['avg_latency_ms']}ms for data transformation

The reflection analysis examines comprehensive performance metrics that matter for data pipeline optimization. These metrics provide visibility into throughput patterns, cost efficiency trends, and quality indicators that guide intelligent optimization decisions.

        Identify bottlenecks and suggest optimizations for:
        1. Kubernetes resource allocation (CPU/memory for data processing)
        2. Batch size configuration for optimal throughput
        3. Parallel processing strategy for distributed workloads
        4. Cost optimization for petabyte-scale processing requirements
        """

        optimization = self.llm_call(reflection_prompt)

        # Apply performance insights to future data processing
        self.update_processing_strategy(optimization)

        return optimization

The optimization analysis focuses on the four critical areas that impact data processing performance: resource allocation, batch sizing, parallelization, and cost management. The insights are automatically applied to improve future processing decisions, creating a self-optimizing data pipeline system.

Pattern 2: Tool Use - Cloud Service Integration

Implementation File: src/session1/tool_use_agent_course.py

Concept

Tool use in data processing enables seamless integration with the specialized cloud infrastructure required for modern data platforms:

class ToolUseAgent(BaseAgent):
    """Agent with cloud data service integration capabilities"""

    def __init__(self):
        super().__init__()
        self.register_data_tools()

The ToolUseAgent specializes in integrating with cloud data services and processing infrastructure. By inheriting from BaseAgent, it maintains core processing capabilities while adding specialized tool integration for data engineering workflows.

Tool Registration

Register tools optimized for data operations and pipeline workflows:

    def register_data_tools(self):
        """Register cloud service interfaces for data processing"""
        self.tools = {
            "query_data_lake": self.query_s3_data_bucket,
            "execute_analytics_query": self.execute_postgres_analytics_query,
            "trigger_etl_workflow": self.trigger_airflow_dag,
            "publish_data_stream": self.publish_to_kafka_topic,
            "search_data_catalog": self.search_elasticsearch_catalog,
            "update_pipeline_status": self.update_grafana_annotation
        }

The tool registry connects agents with essential cloud data services. Each tool provides a specific capability: S3 for data lake queries, PostgreSQL for analytics, Airflow for workflow orchestration, Kafka for streaming, Elasticsearch for data discovery, and Grafana for monitoring. This comprehensive integration enables agents to orchestrate complex data processing workflows across distributed cloud infrastructure.

Pattern 3: Planning - Workflow Orchestration

Implementation File: src/session1/planning_agent_course.py

Concept

Planning agents orchestrate the complex workflows required for data processing and analytics pipeline execution:

class PlanningAgent(BaseAgent):
    """Agent for data workflow orchestration"""

    def create_processing_plan(self, data_batch: dict) -> list:
        """Generate optimal processing plan for data batch"""
        planning_prompt = f"""
        Create processing plan for data batch:
        - Data volume: {data_batch['size_gb']} GB of structured/unstructured data
        - Data types: {data_batch['data_types']} (JSON, Parquet, CSV, logs)
        - Processing priority: {data_batch['priority']} (real-time/batch/archive)
        - SLA requirement: {data_batch['sla_hours']} hours for completion
        - Processing type: {data_batch['processing_type']} (ETL, analytics, ML training)

The planning agent analyzes comprehensive data batch characteristics to create optimal processing strategies. Each parameter influences the planning decision: data volume affects resource allocation, data types determine processing methods, priority impacts scheduling, SLA requirements guide resource reservation, and processing type selects appropriate tools and workflows.

        Consider current infrastructure:
        - Available Kubernetes nodes: {self.get_available_nodes()}
        - Current processing queue: {self.get_queue_status()} jobs
        - Budget allocation: ${data_batch['budget']} for this processing cycle
        - Compute availability: {self.get_compute_resources()} for data processing

        Generate step-by-step workflow with optimal resource allocation for data processing.
        """

        plan = self.llm_call(planning_prompt)
        return self.validate_resource_availability(plan)

Infrastructure awareness ensures plans are realistic and executable. The agent considers current node availability, queue depth, budget constraints, and compute resources to generate plans that can actually be executed within the available infrastructure. Plan validation confirms resource availability before committing to execution.

Pattern 4: ReAct - Adaptive Data Processing

Implementation File: src/session1/react_agent_course.py

Concept

ReAct pattern enables dynamic adaptation to the challenges common in data processing systems:

class ReActAgent(BaseAgent):
    """Agent implementing adaptive processing for data pipelines"""

    def process_with_reasoning(self, data_batch: dict, max_retries: int = 3):
        """Process data with reasoning and adaptive strategies"""
        for attempt in range(max_retries):
            thought = self.analyze_data_characteristics(data_batch)
            action = self.determine_processing_strategy(thought)
            observation = self.execute_data_processing(action)

The ReAct pattern enables adaptive data processing by combining reasoning with action in an iterative loop. Each cycle analyzes current data characteristics, determines the optimal processing strategy, and executes the action while observing results for the next iteration.

            if self.processing_successful(observation):
                break

            # Adapt strategy based on data processing challenges
            data_batch = self.adjust_processing_params(observation)

        return self.get_processing_result()

Success evaluation determines whether processing objectives have been met. When issues are detected, the agent adapts its strategy by adjusting processing parameters based on observed failures, creating resilient data processing workflows that can handle unexpected challenges and data anomalies.

Pattern 5: Multi-Agent - Distributed Processing Coordination

Implementation File: src/session1/multi_agent_system_course.py

Concept

Coordinate multiple specialized agents across your data processing pipeline:

class DataPipelineCoordinator:
    """Coordinator for distributed data processing agents"""

    def __init__(self):
        self.agents = {
            "data_ingestion": DataIngestionAgent(),
            "quality_validation": DataQualityAgent(),
            "transformation": DataTransformationAgent(),
            "analytics": DataAnalyticsAgent(),
            "storage_optimization": StorageOptimizationAgent()
        }

The coordinator manages a specialized team of data processing agents, each optimized for specific data engineering tasks. This division of labor enables parallel processing across different pipeline stages while maintaining coordination and data flow integrity.

Coordination Protocol

Implement coordination across distributed data processing systems:

    def orchestrate_data_pipeline(self, data_batch: dict) -> dict:
        """Coordinate multi-agent data processing"""
        # Ingestion agent handles multi-source data intake
        ingested = self.agents["data_ingestion"].ingest_data_batch(data_batch)

        # Quality validation agent checks data integrity
        validated = self.agents["quality_validation"].validate_data_quality(ingested)

The pipeline begins with data ingestion from multiple sources, followed by comprehensive quality validation. Each agent specializes in its domain - the ingestion agent handles diverse data formats and sources, while the quality agent applies validation rules and anomaly detection.

        # Transformation agent processes and enriches data
        if validated["quality_score"] > 0.85:  # High quality threshold for analytics
            transformed = self.agents["transformation"].transform_data(validated)
        else:
            transformed = self.agents["transformation"].clean_and_transform(validated)

Transformation strategy adapts based on data quality scores. High-quality data proceeds through standard transformation workflows, while lower-quality data triggers enhanced cleaning and enrichment processes to meet analytical requirements.

        # Analytics agent runs analytical processing
        analytics_results = self.agents["analytics"].run_analytics(transformed)

        # Storage optimization for long-term data retention
        self.agents["storage_optimization"].store_with_lifecycle_policy(analytics_results)

        return analytics_results

The analytics agent performs computational analysis on the processed data, while the storage optimization agent manages long-term retention policies, compression, and archival strategies. This coordinated approach ensures efficient processing and cost-effective storage across the entire data lifecycle.

📝 Participant Path: Production Implementation

Prerequisites: Complete 🎯 Observer Path sections above

Building on the foundational concepts, this section focuses on practical implementation patterns and production-ready considerations for enterprise data processing environments.

Production Considerations

Cost Optimization for Cloud Deployment

Agents must operate efficiently within enterprise budget constraints while maintaining the scale required for comprehensive data processing:

class CostOptimizedAgent(BaseAgent):
    """Agent optimized for enterprise-scale cloud cost management"""

    def __init__(self, monthly_budget: float = 100000):  # Enterprise scale budget
        super().__init__()
        self.budget_tracker = BudgetTracker(monthly_budget)
        self.cost_per_token = 0.00002  # GPT-4 pricing for cost estimation

Cost optimization is crucial for enterprise-scale data processing where agent systems can consume significant resources. The budget tracker monitors spending across different models and processing types, while token pricing awareness enables intelligent model selection based on cost-performance trade-offs.

Kubernetes Integration

Deploy agents as Kubernetes operators designed for data processing workloads:

    def deploy_as_data_k8s_operator(self):
        """Deploy agent as Kubernetes operator for data processing"""
        # Base deployment configuration
        deployment_config = {
            "apiVersion": "apps/v1",
            "kind": "Deployment",
            "metadata": {"name": "data-processing-agent"}
        }

This creates the foundation Kubernetes deployment for data processing agents with enterprise-scale configuration.

        # Scaling and resource configuration
        deployment_config["spec"] = {
            "replicas": 5,  # Scale for data processing volumes
            "template": {
                "spec": {
                    "containers": [{
                        "name": "agent",
                        "image": "data-processing-agent:latest"
                    }]
                }
            }
        }

The replica count provides sufficient parallelism for high-throughput data processing scenarios.

        # Resource limits for data processing workloads
        deployment_config["spec"]["template"]["spec"]["containers"][0]["resources"] = {
            "requests": {"memory": "2Gi", "cpu": "1000m"},
            "limits": {"memory": "4Gi", "cpu": "2000m"}
        }

        return deployment_config

Kubernetes deployment configuration optimizes agents for data processing workloads. The replica count of 5 provides sufficient parallelism for high-throughput scenarios, while resource allocations (2-4GB memory, 1-2 CPU cores) accommodate the computational requirements of intelligent data processing tasks without over-provisioning infrastructure.

Monitoring and Observability

Implement comprehensive monitoring tailored for data processing requirements:

    def setup_data_monitoring(self):
        """Configure Prometheus metrics and Grafana dashboards for data processing"""
        self.metrics = {
            "data_throughput": Gauge('data_throughput_gbps'),
            "pipeline_latency": Histogram('pipeline_latency_seconds'),
            "data_quality_error_rate": Counter('data_quality_errors_total'),
            "processing_cost_per_gb": Gauge('processing_cost_per_gb_dollars'),
            "compliance_score": Gauge('compliance_score_percentage')
        }

Comprehensive monitoring tracks the key performance indicators essential for data processing operations: throughput measures processing speed, latency tracks responsiveness, error rates monitor quality, cost per GB tracks financial efficiency, and compliance scores ensure regulatory adherence. These metrics enable proactive optimization and issue detection.

📝 Practical Exercise: Building a Data Quality Agent

This practical exercise demonstrates real-world application of bare metal agent patterns in production data environments.

Let's construct a complete agent for data quality assurance that operates at production scale:

class DataQualityAgent(BaseAgent):
    """Production-ready agent for data quality monitoring"""

    def __init__(self):
        super().__init__(model_name="gpt-4")
        self.quality_rules = self.load_data_quality_standards()
        self.anomaly_detector = DataAnomalyDetector()

The DataQualityAgent specializes in comprehensive data quality assessment using both rule-based validation and AI-powered anomaly detection. This dual approach ensures consistent quality standards while adapting to novel data patterns that rules might miss.

    def analyze_data_batch(self, batch_metadata: dict) -> dict:
        """Analyze data batch for quality and compliance"""
        # Check data completeness across all sources
        completeness = self.check_data_completeness(batch_metadata)

        # Detect anomalies in data patterns
        anomalies = self.anomaly_detector.detect_data_anomalies(batch_metadata)

        # Generate comprehensive quality report using domain knowledge
        quality_prompt = self.build_quality_prompt(
            batch_metadata, completeness, anomalies
        )

        analysis = self.llm_call(quality_prompt)

The analysis process combines multiple quality assessment techniques: completeness checking validates data coverage across sources, anomaly detection identifies unusual patterns, and LLM analysis provides contextual quality assessment using domain knowledge and business rules.

        # Determine processing action based on quality requirements
        action = self.determine_quality_action(analysis)

        return {
            "quality_score": self.calculate_quality_score(analysis),
            "issues_found": self.extract_issues(analysis),
            "recommended_action": action,
            "analytics_ready": action in ["approve", "approve_with_notes"],
            "reprocessing_cost_impact": self.estimate_reprocessing_cost(action),
            "compliance_status": self.check_compliance(analysis)
        }

The quality assessment produces actionable insights including numerical quality scores, detailed issue identification, processing recommendations, readiness flags for downstream analytics, cost impact estimates for any required reprocessing, and compliance status verification for regulatory requirements.

📝 Running the Complete Demo

Demo File: src/session1/demo_runner_course.py

Experience all five agentic patterns in action with our comprehensive demonstration:

# Run complete demonstration
python src/session1/demo_runner_course.py

# Run quick demo (faster execution)
python src/session1/demo_runner_course.py --quick

The demo showcases:

  1. BaseAgent Foundation: Core processing capabilities with different data types
  2. Reflection Pattern: Performance analysis and self-optimization
  3. Tool Use Pattern: Cloud service integration and workflow orchestration
  4. Planning Pattern: Resource allocation and workflow planning
  5. ReAct Pattern: Adaptive processing with reasoning transparency
  6. Multi-Agent System: Coordinated distributed processing pipeline

Expected Output: The demo will show each pattern in action, displaying metrics, decision-making processes, and coordination between agents. You'll see real-time cost tracking, performance optimization, and intelligent resource allocation working together.

⚙️ Implementer Path: Advanced Patterns

Prerequisites: Complete 🎯 Observer and 📝 Participant paths

For comprehensive advanced coverage, explore the specialized modules:

📝 Knowledge Check - Session 1

Test your understanding of bare metal agents in cloud data processing:

Question 1: Why is bare metal agent implementation critical for data pipeline systems?
A) It's required by Kubernetes
B) It provides full control over resource usage and API costs for petabyte-scale processing
C) It's easier to deploy
D) It uses less storage

Question 2: What is the primary purpose of the reflection pattern in data processing agents?
A) To generate better LLM responses
B) To analyze performance and optimize data pipeline efficiency
C) To reduce memory usage
D) To improve security

Question 3: How do agents manage cloud processing costs in data applications?
A) By using only free services
B) Through intelligent model selection based on data complexity and budget tracking
C) By caching everything
D) Through compression only

Question 4: What is the key consideration for memory management in Kubernetes-deployed data agents?
A) Unlimited memory allocation
B) Respecting pod memory limits while efficiently caching data processing context
C) Using only disk storage
D) Memory is not a concern

Question 5: Why is tool registration important for data processing agents?
A) It looks professional
B) To integrate with specialized cloud services like S3 for data storage, Kafka for streaming, and Argo Workflows for workflows
C) It's required by Python
D) To reduce code size

View Solutions →

Previous: Session 0 - Introduction →
Next: Session 2 - Implementation →