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Agentic Design Pattern: Agentic Workflows

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    """
Pattern 167: Agentic Workflows

Description:
    The Agentic Workflows pattern implements complex, multi-step processes where agents
    autonomously navigate through workflow states, make decisions, handle branching logic,
    and coordinate multiple tasks. It uses state machines and graph-based execution for
    sophisticated task orchestration.

Components:
    1. Workflow Definition: Graph structure with states and transitions
    2. State Manager: Tracks current workflow state
    3. Decision Engine: Makes branching decisions
    4. Task Executor: Executes tasks at each state
    5. Condition Evaluator: Evaluates transition conditions
    6. Error Handler: Manages workflow failures

Use Cases:
    - Multi-step business processes
    - Complex data pipelines
    - Decision trees and workflows
    - Approval workflows
    - Multi-stage content generation
    - Automated testing workflows

Benefits:
    - Complex process automation
    - Clear workflow visualization
    - Conditional branching support
    - State persistence
    - Error recovery
    - Audit trail

Trade-offs:
    - Increased complexity
    - State management overhead
    - Debugging challenges
    - Performance considerations
    - Requires workflow design

LangChain Implementation:
    Uses LangGraph-inspired state machines with LangChain agents for task execution.
    Implements graph-based workflows with conditional branching and state management.
"""

import os
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

load_dotenv()


class WorkflowStatus(Enum):
    """Status of workflow execution"""
    NOT_STARTED = "not_started"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"
    PAUSED = "paused"


class TransitionType(Enum):
    """Types of state transitions"""
    AUTOMATIC = "automatic"
    CONDITIONAL = "conditional"
    MANUAL = "manual"


@dataclass
class WorkflowState:
    """Represents a state in the workflow"""
    name: str
    description: str
    task: Optional[Callable] = None
    is_terminal: bool = False
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class StateTransition:
    """Represents a transition between states"""
    from_state: str
    to_state: str
    condition: Optional[Callable] = None
    transition_type: TransitionType = TransitionType.AUTOMATIC
    description: str = ""


@dataclass
class WorkflowContext:
    """Context passed through workflow execution"""
    data: Dict[str, Any] = field(default_factory=dict)
    history: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def add_result(self, state: str, result: Any):
        """Add result from a state execution"""
        self.data[f"{state}_result"] = result
        self.history.append(state)
    
    def get_result(self, state: str) -> Any:
        """Get result from previous state"""
        return self.data.get(f"{state}_result")


class AgenticWorkflow:
    """Implements agentic workflow pattern with state machines"""
    
    def __init__(self, name: str):
        """Initialize workflow"""
        self.name = name
        self.states: Dict[str, WorkflowState] = {}
        self.transitions: List[StateTransition] = []
        self.start_state: Optional[str] = None
        self.current_state: Optional[str] = None
        self.status = WorkflowStatus.NOT_STARTED
        self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
        
        # Execution log
        self.execution_log: List[Dict[str, Any]] = []
    
    def add_state(self, state: WorkflowState, is_start: bool = False):
        """Add a state to the workflow"""
        self.states[state.name] = state
        if is_start:
            self.start_state = state.name
    
    def add_transition(self, transition: StateTransition):
        """Add a transition between states"""
        if transition.from_state not in self.states:
            raise ValueError(f"Unknown from_state: {transition.from_state}")
        if transition.to_state not in self.states:
            raise ValueError(f"Unknown to_state: {transition.to_state}")
        self.transitions.append(transition)
    
    def execute(self, initial_context: Optional[WorkflowContext] = None) -> WorkflowContext:
        """Execute the workflow"""
        if not self.start_state:
            raise ValueError("No start state defined")
        
        context = initial_context or WorkflowContext()
        self.current_state = self.start_state
        self.status = WorkflowStatus.IN_PROGRESS
        
        self._log_execution("workflow_started", {
            "start_state": self.start_state,
            "timestamp": datetime.now().isoformat()
        })
        
        try:
            while True:
                state = self.states[self.current_state]
                
                self._log_execution("state_entered", {
                    "state": self.current_state,
                    "timestamp": datetime.now().isoformat()
                })
                
                # Execute state task
                if state.task:
                    try:
                        result = state.task(context, self.llm)
                        context.add_result(self.current_state, result)
                        
                        self._log_execution("state_executed", {
                            "state": self.current_state,
                            "result": str(result)[:100]
                        })
                    except Exception as e:
                        self._log_execution("state_error", {
                            "state": self.current_state,
                            "error": str(e)
                        })
                        self.status = WorkflowStatus.FAILED
                        raise
                
                # Check if terminal state
                if state.is_terminal:
                    self.status = WorkflowStatus.COMPLETED
                    self._log_execution("workflow_completed", {
                        "final_state": self.current_state,
                        "timestamp": datetime.now().isoformat()
                    })
                    break
                
                # Find next state
                next_state = self._get_next_state(context)
                
                if next_state is None:
                    self.status = WorkflowStatus.FAILED
                    raise ValueError(f"No valid transition from state: {self.current_state}")
                
                self._log_execution("transition", {
                    "from": self.current_state,
                    "to": next_state,
                    "timestamp": datetime.now().isoformat()
                })
                
                self.current_state = next_state
        
        except Exception as e:
            self.status = WorkflowStatus.FAILED
            self._log_execution("workflow_failed", {
                "error": str(e),
                "timestamp": datetime.now().isoformat()
            })
            raise
        
        return context
    
    def _get_next_state(self, context: WorkflowContext) -> Optional[str]:
        """Determine next state based on transitions"""
        # Find applicable transitions from current state
        applicable = [
            t for t in self.transitions 
            if t.from_state == self.current_state
        ]
        
        if not applicable:
            return None
        
        # Evaluate conditions for conditional transitions
        for transition in applicable:
            if transition.transition_type == TransitionType.AUTOMATIC:
                return transition.to_state
            elif transition.transition_type == TransitionType.CONDITIONAL:
                if transition.condition and transition.condition(context):
                    return transition.to_state
        
        # If no condition matched, try first automatic transition
        automatic = [t for t in applicable if t.transition_type == TransitionType.AUTOMATIC]
        if automatic:
            return automatic[0].to_state
        
        return None
    
    def _log_execution(self, event_type: str, data: Dict[str, Any]):
        """Log workflow execution event"""
        self.execution_log.append({
            "event": event_type,
            "data": data
        })
    
    def get_execution_summary(self) -> Dict[str, Any]:
        """Get summary of workflow execution"""
        return {
            "workflow_name": self.name,
            "status": self.status.value,
            "states_visited": len(set(e["data"].get("state", "") for e in self.execution_log)),
            "total_events": len(self.execution_log),
            "current_state": self.current_state
        }
    
    def visualize(self) -> str:
        """Create text visualization of workflow"""
        lines = [f"Workflow: {self.name}", "=" * 60, ""]
        
        lines.append("States:")
        for name, state in self.states.items():
            marker = "→" if name == self.start_state else " "
            terminal = " (terminal)" if state.is_terminal else ""
            lines.append(f"  {marker} {name}{terminal}: {state.description}")
        
        lines.append("\nTransitions:")
        for trans in self.transitions:
            cond = f" [conditional]" if trans.transition_type == TransitionType.CONDITIONAL else ""
            lines.append(f"  {trans.from_state} -> {trans.to_state}{cond}")
            if trans.description:
                lines.append(f"    {trans.description}")
        
        return "\n".join(lines)


def demonstrate_agentic_workflows():
    """Demonstrate agentic workflows"""
    print("=" * 80)
    print("AGENTIC WORKFLOWS PATTERN DEMONSTRATION")
    print("=" * 80)
    
    # Example 1: Content generation workflow
    print("\n" + "=" * 80)
    print("Example 1: Content Generation Workflow")
    print("=" * 80)
    
    def research_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Research phase"""
        topic = context.data.get("topic", "AI")
        prompt = f"List 3 key points about {topic} for a blog post"
        chain = ChatPromptTemplate.from_messages([
            ("user", prompt)
        ]) | llm | StrOutputParser()
        return chain.invoke({})
    
    def outline_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Outline creation"""
        research = context.get_result("research")
        prompt = f"Create a blog post outline based on: {research}"
        chain = ChatPromptTemplate.from_messages([
            ("user", prompt)
        ]) | llm | StrOutputParser()
        return chain.invoke({})
    
    def write_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Writing phase"""
        outline = context.get_result("outline")
        prompt = f"Write the first paragraph based on: {outline}"
        chain = ChatPromptTemplate.from_messages([
            ("user", prompt)
        ]) | llm | StrOutputParser()
        return chain.invoke({})
    
    def review_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Review phase"""
        draft = context.get_result("write")
        prompt = f"Review and provide feedback on: {draft}"
        chain = ChatPromptTemplate.from_messages([
            ("user", prompt)
        ]) | llm | StrOutputParser()
        return chain.invoke({})
    
    # Create workflow
    content_workflow = AgenticWorkflow("Content Generation")
    
    # Add states
    content_workflow.add_state(WorkflowState(
        "research",
        "Research topic and gather key points",
        task=research_task
    ), is_start=True)
    
    content_workflow.add_state(WorkflowState(
        "outline",
        "Create content outline",
        task=outline_task
    ))
    
    content_workflow.add_state(WorkflowState(
        "write",
        "Write content draft",
        task=write_task
    ))
    
    content_workflow.add_state(WorkflowState(
        "review",
        "Review and provide feedback",
        task=review_task
    ))
    
    content_workflow.add_state(WorkflowState(
        "complete",
        "Content generation complete",
        is_terminal=True
    ))
    
    # Add transitions
    content_workflow.add_transition(StateTransition(
        "research", "outline", 
        transition_type=TransitionType.AUTOMATIC
    ))
    content_workflow.add_transition(StateTransition(
        "outline", "write",
        transition_type=TransitionType.AUTOMATIC
    ))
    content_workflow.add_transition(StateTransition(
        "write", "review",
        transition_type=TransitionType.AUTOMATIC
    ))
    content_workflow.add_transition(StateTransition(
        "review", "complete",
        transition_type=TransitionType.AUTOMATIC
    ))
    
    # Visualize workflow
    print("\nWorkflow Structure:")
    print(content_workflow.visualize())
    
    # Execute workflow
    print("\n" + "-" * 60)
    print("Executing Workflow...")
    print("-" * 60)
    
    initial_context = WorkflowContext(data={"topic": "Machine Learning"})
    
    try:
        result_context = content_workflow.execute(initial_context)
        
        print("\nWorkflow Execution Summary:")
        summary = content_workflow.get_execution_summary()
        print(f"  Status: {summary['status']}")
        print(f"  States visited: {summary['states_visited']}")
        print(f"  Total events: {summary['total_events']}")
        
        print("\nState Execution Path:")
        for state in result_context.history:
            print(f"  → {state}")
        
        print("\nFinal Results:")
        print(f"  Research: {result_context.get_result('research')[:80]}...")
        print(f"  Outline: {result_context.get_result('outline')[:80]}...")
        print(f"  Draft: {result_context.get_result('write')[:80]}...")
        print(f"  Review: {result_context.get_result('review')[:80]}...")
        
    except Exception as e:
        print(f"Workflow failed: {e}")
    
    # Example 2: Conditional branching workflow
    print("\n" + "=" * 80)
    print("Example 2: Conditional Branching Workflow")
    print("=" * 80)
    
    def analyze_task(context: WorkflowContext, llm: ChatOpenAI) -> Dict[str, Any]:
        """Analyze input"""
        text = context.data.get("text", "")
        prompt = f"Is this text positive or negative sentiment? Answer with just 'positive' or 'negative': {text}"
        chain = ChatPromptTemplate.from_messages([
            ("user", prompt)
        ]) | llm | StrOutputParser()
        sentiment = chain.invoke({}).strip().lower()
        return {"sentiment": sentiment}
    
    def positive_response_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Handle positive sentiment"""
        return "Thank you for the positive feedback!"
    
    def negative_response_task(context: WorkflowContext, llm: ChatOpenAI) -> str:
        """Handle negative sentiment"""
        return "We're sorry to hear that. How can we improve?"
    
    # Create branching workflow
    branching_workflow = AgenticWorkflow("Sentiment Analysis")
    
    # Add states
    branching_workflow.add_state(WorkflowState(
        "analyze",
        "Analyze sentiment",
        task=analyze_task
    ), is_start=True)
    
    branching_workflow.add_state(WorkflowState(
        "positive_response",
        "Generate positive response",
        task=positive_response_task
    ))
    
    branching_workflow.add_state(WorkflowState(
        "negative_response",
        "Generate negative response",
        task=negative_response_task
    ))
    
    branching_workflow.add_state(WorkflowState(
        "end",
        "End state",
        is_terminal=True
    ))
    
    # Add conditional transitions
    branching_workflow.add_transition(StateTransition(
        "analyze", "positive_response",
        condition=lambda ctx: ctx.get_result("analyze").get("sentiment") == "positive",
        transition_type=TransitionType.CONDITIONAL,
        description="If sentiment is positive"
    ))
    
    branching_workflow.add_transition(StateTransition(
        "analyze", "negative_response",
        condition=lambda ctx: ctx.get_result("analyze").get("sentiment") == "negative",
        transition_type=TransitionType.CONDITIONAL,
        description="If sentiment is negative"
    ))
    
    branching_workflow.add_transition(StateTransition(
        "positive_response", "end",
        transition_type=TransitionType.AUTOMATIC
    ))
    
    branching_workflow.add_transition(StateTransition(
        "negative_response", "end",
        transition_type=TransitionType.AUTOMATIC
    ))
    
    # Visualize
    print("\nWorkflow Structure:")
    print(branching_workflow.visualize())
    
    # Execute with positive text
    print("\n" + "-" * 60)
    print("Test 1: Positive Text")
    print("-" * 60)
    
    context1 = WorkflowContext(data={"text": "This product is amazing! I love it."})
    result1 = branching_workflow.execute(context1)
    
    print(f"Path taken: {' → '.join(result1.history)}")
    print(f"Response: {result1.get_result('positive_response')}")
    
    # Execute with negative text
    print("\n" + "-" * 60)
    print("Test 2: Negative Text")
    print("-" * 60)
    
    branching_workflow.status = WorkflowStatus.NOT_STARTED  # Reset
    context2 = WorkflowContext(data={"text": "This is terrible. Very disappointed."})
    result2 = branching_workflow.execute(context2)
    
    print(f"Path taken: {' → '.join(result2.history)}")
    print(f"Response: {result2.get_result('negative_response')}")
    
    # Summary
    print("\n" + "=" * 80)
    print("SUMMARY")
    print("=" * 80)
    print("""
The Agentic Workflows pattern enables:
✓ Complex multi-step process automation
✓ Conditional branching based on runtime state
✓ State-based execution with context passing
✓ Clear workflow visualization
✓ Execution logging and audit trails
✓ Error handling and recovery
✓ Flexible workflow composition

This pattern is valuable for:
- Business process automation
- Multi-stage content generation
- Decision tree execution
- Approval workflows
- Complex data pipelines
- Testing and validation workflows
- Orchestrating multiple agents
    """)


if __name__ == "__main__":
    demonstrate_agentic_workflows()

Agentic Design Pattern: Agentic Workflows

Pattern 167: Agentic Workflows – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Agentic Workflows pattern implementation.

Pattern Description

Pattern 167: Agentic Workflows

Description: The Agentic Workflows pattern implements complex, multi-step processes where agents autonomously navigate through workflow states, make decisions, handle branching logic, and coordinate multiple tasks. It uses state machines and graph-based execution for sophisticated task orchestration.

Components: 1. Workflow Definition: Graph structure with states and transitions 2. State Manager: Tracks current workflow state 3. Decision Engine: Makes branching decisions 4. Task Executor: Executes tasks at each state 5. Condition Evaluator: Evaluates transition conditions 6. Error Handler: Manages workflow failures

Use Cases: – Multi-step business processes – Complex data pipelines – Decision trees and workflows – Approval workflows – Multi-stage content generation – Automated testing workflows

Benefits: – Complex process automation – Clear workflow visualization – Conditional branching support – State persistence – Error recovery – Audit trail

Trade-offs: – Increased complexity – State management overhead – Debugging challenges – Performance considerations – Requires workflow design

LangChain Implementation: Uses LangGraph-inspired state machines with LangChain agents for task execution. Implements graph-based workflows with conditional branching and state management.

File Statistics

  • Total Lines: 543
  • Classes: 6
  • Functions: 1
  • Constants: 0

Module Documentation

"""
Pattern 167: Agentic Workflows

Description:
    The Agentic Workflows pattern implements complex, multi-step processes where agents
    autonomously navigate through workflow states, make decisions, handle branching logic,
    and coordinate multiple tasks. It uses state machines and graph-based execution for
    sophisticated task orchestration.

Components:
    1. Workflow Definition: Graph structure with states and transitions
    2. State Manager: Tracks current workflow state
    3. Decision Engine: Makes branching decisions
    4. Task Executor: Executes tasks at each state
    5. Condition Evaluator: Evaluates transition conditions
    6. Error Handler: Manages workflow failures

Use Cases:
    - Multi-step business processes
    - Complex data pipelines
    - Decision trees and workflows
    - Approval workflows
    - Multi-stage content generation
    - Automated testing workflows

Benefits:
    - Complex process automation
    - Clear workflow visualization
    - Conditional branching support
    - State persistence
    - Error recovery
    - Audit trail

Trade-offs:
    - Increased complexity
    - State management overhead
    - Debugging challenges
    - Performance considerations
    - Requires workflow design

LangChain Implementation:
    Uses LangGraph-inspired state machines with LangChain agents for task execution.
    Implements graph-based workflows with conditional branching and state management.
"""

Purpose: This module implements the Agentic Workflows pattern, providing a structured approach to agent design and implementation.

Import Statements

Standard Library Imports

import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional

Purpose: Core Python functionality: – dataclasses.dataclass: Simplified class definitions with @dataclass – dataclasses.field: Simplified class definitions with @dataclass – datetime.datetime: Date and time handling – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

Classes

Class: WorkflowStatus

Lines 60-66

Inherits from: Enum

Purpose:

Status of workflow execution

Class: TransitionType

Lines 69-73

Inherits from: Enum

Purpose:

Types of state transitions

Class: WorkflowState

Lines 77-83

Purpose:

Represents a state in the workflow

Class: StateTransition

Lines 87-93

Purpose:

Represents a transition between states

Class: WorkflowContext

Lines 97-110

Purpose:

Context passed through workflow execution

Methods:

add_result(self, state, result)

Lines 103-106

Purpose:

Add result from a state execution

Parameters: – state: State – result: Result

get_result(self, state)

Lines 108-110

Purpose:

Get result from previous state

Parameters: – state: State

Returns: Any

Class: AgenticWorkflow

Lines 113-278

Purpose:

Implements agentic workflow pattern with state machines

Methods:

__init__(self, name)

Lines 116-127

Purpose:

Initialize workflow

Parameters: – name: Name

add_state(self, state, is_start)

Lines 129-133

Purpose:

Add a state to the workflow

Parameters: – state: State – is_start: Is start

add_transition(self, transition)

Lines 135-141

Purpose:

Add a transition between states

Parameters: – transition: Transition

execute(self, initial_context)

Lines 143-216

Purpose:

Execute the workflow

Parameters: – initial_context: Initial context

Returns: WorkflowContext

_get_next_state(self, context)

Lines 218-242

Purpose:

Determine next state based on transitions

Parameters: – context: Context

Returns: <ast.Subscript object at 0x000001A06D5F5B90>

_log_execution(self, event_type, data)

Lines 244-249

Purpose:

Log workflow execution event

Parameters: – event_type: Event type – data: Data

get_execution_summary(self)

Lines 251-259

Purpose:

Get summary of workflow execution

Parameters:

Returns: <ast.Subscript object at 0x000001A06D83F710>

visualize(self)

Lines 261-278

Purpose:

Create text visualization of workflow

Parameters:

Returns: str

Functions

demonstrate_agentic_workflows()

Lines 281-538

Purpose:

Demonstrate agentic workflows

Key Concepts Summary

Pattern Overview

The Agentic Workflows pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

  1. Agent Class: Main implementation of the pattern logic
  2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
  3. Tool Integration: Optional tools for extended functionality
  4. Memory Management: State handling across interactions
  5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

Best Practices Demonstrated

1. Environment Configuration

  • ✅ Use .env files for API keys
  • ✅ Never hardcode credentials
  • ✅ Validate environment variables on startup

2. Error Handling

  • ✅ Graceful degradation on failures
  • ✅ Informative error messages
  • ✅ Logging for debugging

3. Prompt Engineering

  • ✅ Clear, specific instructions
  • ✅ Structured output formats
  • ✅ Few-shot examples when beneficial

4. Code Organization

  • ✅ Separation of concerns (class-based design)
  • ✅ Type hints for clarity
  • ✅ Docstrings for documentation

5. Performance Optimization

  • ✅ Appropriate temperature settings
  • ✅ Token budget management
  • ✅ Caching where applicable

LangChain Components Used

  • ChatOpenAI: OpenAI model interface
  • Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

Performance Considerations

Computational Cost

  • LLM Calls: Primary cost factor (based on tokens)
  • Latency: Depends on model and input size
  • Memory: Minimal for most patterns

Optimization Strategies

  1. Prompt Optimization: Minimize tokens while maintaining clarity
  2. Caching: Cache repeated queries/responses
  3. Model Selection: Use smaller models when appropriate
  4. Batch Processing: Group similar requests
  5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

  • GPT-3.5-turbo: ~$0.002 per request
  • GPT-4: ~$0.03 per request
  • GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

Further Improvements

Production Enhancements

  1. Monitoring: Add logging, metrics, tracing
  2. Validation: Input/output schema validation
  3. Rate Limiting: Implement request throttling
  4. Caching: Redis or in-memory cache for responses
  5. Async Support: Non-blocking operations for scalability

Feature Extensions

  1. Multi-Model Support: Switch between providers
  2. Fallback Models: Graceful degradation strategy
  3. Custom Tools: Domain-specific function calling
  4. Advanced Memory: Vector stores for long-term memory
  5. Evaluation: Automated testing and benchmarking

Code Quality

  1. Testing: Unit tests, integration tests
  2. Documentation: API docs, usage examples
  3. Type Safety: Stricter type hints
  4. Configuration: Externalized config files

Related Patterns

  • Pattern 166
  • Pattern 168

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

Summary

The Agentic Workflows pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

File: 167_agentic_workflows.py
Total Lines: 543
Pattern Category: AI Agent Design Pattern #167

Generated from actual implementation code
For educational and reference purposes

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