Session 0 - Module B: Advanced Pattern Theory¶
⚠️ ADVANCED OPTIONAL MODULE Prerequisites: Complete Session 0 core content first.
Picture a senior data engineer who has mastered individual tools - Apache Spark for processing, Kafka for streaming, PostgreSQL for storage. But what separates them from a true data architect is knowing how to orchestrate these tools into a symphony that transforms raw data into business intelligence. They don't just use tools; they compose systems that think, adapt, and evolve.
You've learned the five patterns that make AI agents intelligent. Now it's time to learn the secret that separates average developers from the architects of the future: how to combine these patterns into symphony orchestras of intelligence.
This is where computer science becomes art, where frameworks become instruments, and where you learn to compose digital minds that think, reason, and adapt with the sophistication of human consciousness itself.
Part 1: Pattern Implementation Strategies¶
Framework-Specific Pattern Implementations¶
Think of a data pipeline that can be built with raw Python scripts, Argo Workflows, or cloud-native solutions like Google Dataflow. The same data transformation, the same business logic - but the architecture, reliability, and scalability differ dramatically based on your implementation choice.
A violin sounds different in the hands of a street musician versus Itzhak Perlman. The same melody, the same instrument - but the technique, the understanding, the craftsmanship makes all the difference.
The same pattern implemented in bare Python versus LangChain versus PydanticAI creates entirely different capabilities. Here's how masters craft intelligence:
Reflection Pattern Implementations¶
Bare Metal Python Approach¶
The simplest approach implements reflection manually with basic iteration control. This gives you full control over the reflection process but requires handling all the logic yourself - like building a data processing pipeline from scratch with pure Python instead of using established frameworks.
class BareMetalReflectionAgent:
def reflect_and_improve(self, initial_response: str, task: str) -> str:
"""Manual implementation of reflection loop"""
max_iterations = 3
current_response = initial_response
The bare metal approach gives you complete control over the reflection process but requires implementing all the logic manually. The maximum iterations prevent infinite loops - a critical safety mechanism for production systems where compute costs matter.
for iteration in range(max_iterations):
# Generate critique
critique_prompt = f"""
Task: {task}
Response: {current_response}
Critique this response for accuracy, completeness, and clarity.
Provide specific suggestions for improvement.
"""
critique = self.llm.generate(critique_prompt)
Each critique uses a structured evaluation focusing on three key quality dimensions: accuracy (is it correct?), completeness (is anything missing?), and clarity (is it understandable?). The prompt asks for specific suggestions rather than general criticism, making the feedback actionable.
The satisfaction check provides a clear exit condition. When the critique indicates the response is adequate, the loop terminates early to avoid unnecessary processing and API costs.
# Generate improved response
improvement_prompt = f"""
Original task: {task}
Previous response: {current_response}
Critique: {critique}
Generate an improved response addressing the critique.
"""
current_response = self.llm.generate(improvement_prompt)
return current_response
The improvement phase creates a new response that specifically addresses the critique points. By including the original task, previous response, and critique, the model has complete context to generate meaningful improvements rather than generic enhancements.
If the critique indicates satisfaction, the loop terminates early. Otherwise, an improvement prompt incorporates the critique to generate a better response, which becomes the input for the next iteration.
LangChain Framework Approach¶
LangChain provides structured components that handle prompt templating and chain orchestration, reducing boilerplate code and enabling more maintainable reflection systems - like using Argo Workflows instead of cron jobs for data pipeline orchestration.
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
class LangChainReflectionAgent:
def __init__(self):
# Critique chain
self.critique_chain = LLMChain(
llm=self.llm,
prompt=PromptTemplate(
template="Critique this response: {response}\nFor task: {task}",
input_variables=["response", "task"]
)
)
LangChain abstracts away the manual prompt construction and variable substitution you saw in the bare metal approach. The PromptTemplate handles formatting and validation, reducing the chance of prompt engineering errors that could degrade agent performance.
# Improvement chain
self.improve_chain = LLMChain(
llm=self.llm,
prompt=PromptTemplate(
template="Improve response based on critique:\nOriginal: {response}\nCritique: {critique}",
input_variables=["response", "critique"]
)
)
By separating critique and improvement into distinct chains, each component can be tested, modified, and optimized independently. This modular architecture makes it easier to experiment with different critique strategies or improvement approaches.
def reflect_and_improve(self, response: str, task: str) -> str:
"""Framework-assisted reflection implementation"""
critique = self.critique_chain.run(response=response, task=task)
improved = self.improve_chain.run(response=response, critique=critique)
return improved
The main method becomes much simpler, focusing on high-level orchestration rather than prompt construction details. This abstraction allows you to concentrate on reflection logic rather than string manipulation and template management.
The main method becomes much simpler, focusing on the high-level flow rather than prompt construction details. Each chain handles its own complexity internally.
PydanticAI Type-Safe Approach¶
PydanticAI takes a different approach by enforcing type safety and structured outputs. This prevents common errors and makes agent behavior more predictable - like using strongly-typed schemas in data engineering to catch data quality issues at runtime:
from pydantic import BaseModel
from typing import List, Literal
class CritiqueResult(BaseModel):
accuracy_score: float
completeness_score: float
clarity_score: float
suggestions: List[str]
overall_rating: Literal["poor", "fair", "good", "excellent"]
class ImprovedResponse(BaseModel):
enhanced_content: str
improvements_made: List[str]
confidence_level: float
PydanticAI transforms free-form text outputs into structured, validated data models. These schemas catch errors at runtime, ensure consistent formats, and provide clear contracts for downstream systems that consume agent outputs. This is crucial for production reliability.
class PydanticReflectionAgent:
def reflect_and_improve(self, response: str, task: str) -> ImprovedResponse:
"""Type-safe reflection with structured outputs"""
# Generate structured critique
critique: CritiqueResult = self.critique_agent.run(
f"Critique response: {response} for task: {task}"
)
Structured critique replaces ambiguous text evaluation with concrete metrics. Instead of parsing text for satisfaction signals, the system gets numerical scores and categorized ratings that can be processed programmatically.
# Only improve if scores are below threshold
if critique.overall_rating in ["poor", "fair"]:
improved: ImprovedResponse = self.improvement_agent.run(
f"Improve: {response}\nSuggestions: {critique.suggestions}"
)
return improved
The type-safe approach enables intelligent decision-making based on structured data rather than string parsing. This conditional improvement saves computational resources by only processing responses that genuinely need enhancement.
When improvement isn't needed, the system still returns a structured response with high confidence, maintaining consistency in the output format while indicating that the original response was already satisfactory.
The type-safe approach enables intelligent decision-making based on structured scores rather than string parsing. The system only attempts improvement when the critique indicates it's necessary, avoiding unnecessary LLM calls.
Tool Use Pattern Implementations¶
Dynamic Tool Discovery¶
Advanced tool agents go beyond static tool sets by intelligently selecting and learning from tool usage. This approach combines task analysis with historical performance data - like a data processing system that learns which algorithms work best for different data characteristics over time.
class AdvancedToolAgent:
def __init__(self):
self.tool_registry = ToolRegistry()
self.tool_performance_tracker = ToolPerformanceTracker()
Advanced tool agents go beyond static tool sets by maintaining both a registry of available capabilities and a performance tracker that learns from experience. This creates an intelligent tool selection system that improves over time.
def select_optimal_tool(self, task_description: str) -> Tool:
"""Intelligent tool selection based on task analysis and historical performance"""
# Analyze task requirements
task_features = self.extract_task_features(task_description)
# Get candidate tools
candidate_tools = self.tool_registry.find_matching_tools(task_features)
Task analysis identifies the key characteristics and requirements, then matches them against tool capabilities. This feature-based matching ensures the agent considers all potentially relevant tools rather than making random selections.
# Rank by historical performance
ranked_tools = self.tool_performance_tracker.rank_tools(
tools=candidate_tools,
task_features=task_features
)
return ranked_tools[0] if ranked_tools else None
Instead of arbitrary selection among matching tools, the system uses historical performance data to identify which tools have proven most effective for similar task characteristics. This data-driven approach continuously improves selection accuracy.
def execute_with_fallback(self, tool: Tool, params: dict) -> dict:
"""Execute tool with automatic fallback to alternatives"""
try:
result = tool.execute(params)
self.tool_performance_tracker.record_success(tool, params, result)
return result
Every successful execution is recorded with context about the parameters and results. This creates a comprehensive knowledge base about tool effectiveness that informs future selection decisions.
except ToolExecutionError as e:
# Try fallback tools
fallback_tools = self.tool_registry.get_fallback_tools(tool)
for fallback in fallback_tools:
try:
result = fallback.execute(params)
self.tool_performance_tracker.record_fallback_success(fallback, params, result)
return result
except ToolExecutionError:
continue
# All tools failed
self.tool_performance_tracker.record_failure(tool, params, e)
raise e
The fallback mechanism provides resilience by automatically trying alternative tools when the primary choice fails. Both fallback successes and complete failures are recorded, creating a comprehensive understanding of tool reliability patterns that enhances future decision-making.
When tools fail, the system automatically tries fallback alternatives. Both failures and successful fallbacks are tracked to improve the robustness of future tool selection.
Part 2: Pattern Combinations - The Symphony of Intelligence¶
Sophisticated Pattern Orchestration - When 1+1 = Infinity¶
Think of the most elegant data architecture you've ever seen - perhaps a real-time analytics platform that seamlessly combines streaming ingestion, parallel processing, intelligent caching, and adaptive scaling. No single component makes it remarkable; it's the symphony of how they work together, each enhancing the others, creating capability that transcends their individual parts.
Mozart didn't just play single notes - he combined notes into chords, chords into harmonies, and harmonies into symphonies that moved entire civilizations. Pattern combination in AI agents follows the same principle: individual patterns are powerful, but combined patterns create intelligence that transcends their components.
This is where you stop building AI and start composing intelligence:
ReAct + Reflection Combination¶
Combining patterns creates sophisticated capabilities that exceed what each pattern can achieve individually. This example shows ReAct reasoning enhanced with quality assurance through reflection - like a data pipeline that not only processes data but validates and optimizes its own performance.
class ReActReflectionAgent:
"""Combines ReAct reasoning with reflection for robust problem solving"""
def solve_complex_problem(self, problem: str) -> dict:
"""Multi-pattern problem solving with quality assurance"""
# Phase 1: ReAct problem solving
react_solution = self.react_solve(problem)
# Phase 2: Reflection on solution quality
solution_critique = self.reflect_on_solution(react_solution, problem)
This combination creates a two-layer intelligence system: ReAct provides adaptive problem-solving through reasoning and action, while reflection adds quality assurance by evaluating the solution's adequacy. The synergy produces more reliable results than either pattern alone.
# Phase 3: Iterative improvement
if solution_critique.needs_improvement:
improved_solution = self.improve_solution(
original=react_solution,
critique=solution_critique,
problem=problem
)
return improved_solution
return react_solution
The improvement phase only activates when reflection identifies genuine issues. This conditional processing saves computational resources while ensuring solution quality - the system doesn't waste time "improving" solutions that are already adequate.
def react_solve(self, problem: str) -> dict:
"""ReAct pattern: iterative reasoning and acting"""
solution_steps = []
current_state = {"problem": problem, "progress": []}
for step in range(self.max_react_steps):
# Think
thought = self.generate_thought(current_state)
# Act
action = self.decide_action(thought, current_state)
observation = self.execute_action(action)
The ReAct core implements the thinking-acting cycle that enables adaptive problem-solving. Each cycle builds understanding through interaction with tools and environment, allowing the agent to adjust its approach based on real observations rather than assumptions.
# Update state
current_state["progress"].append({
"step": step,
"thought": thought,
"action": action,
"observation": observation
})
# Check if problem is solved
if self.is_problem_solved(current_state):
break
return {
"solution": self.extract_solution(current_state),
"reasoning_chain": current_state["progress"]
}
Each step is carefully documented, creating a complete reasoning chain that shows how the agent reached its solution. This transparency is crucial for debugging, auditing, and understanding agent decision-making in complex problem-solving scenarios.
Each step is recorded to provide transparency into the reasoning process, and the system checks for completion after each iteration to avoid unnecessary work.
Planning + Multi-Agent Coordination¶
This combination tackles complex workflows that require both strategic planning and coordinated execution across multiple specialized agents - like orchestrating a data migration project where different teams handle different aspects (infrastructure, ETL, validation, rollback) but must work in perfect synchronization.
class PlanningCoordinationAgent:
"""Combines planning with multi-agent coordination for complex workflows"""
def execute_collaborative_plan(self, high_level_goal: str) -> dict:
"""Create plan and coordinate multiple agents for execution"""
# Phase 1: High-level planning
master_plan = self.create_master_plan(high_level_goal)
# Phase 2: Agent assignment and task delegation
agent_assignments = self.assign_tasks_to_agents(master_plan)
# Phase 3: Coordinated execution with dynamic re-planning
results = self.coordinate_execution(agent_assignments)
This three-phase architecture combines the strategic thinking of planning agents with the collaborative execution of multi-agent systems. The separation allows for independent optimization of each phase while maintaining end-to-end workflow coherence.
return {
"original_goal": high_level_goal,
"plan": master_plan,
"execution_results": results,
"success_metrics": self.evaluate_success(results, high_level_goal)
}
Complete workflow transparency enables post-execution analysis and continuous improvement. By tracking the original goal, plan, execution details, and success metrics, the system can learn from each workflow and improve future planning decisions.
def coordinate_execution(self, assignments: List[AgentAssignment]) -> dict:
"""Dynamic coordination with replanning capability"""
execution_results = {}
for phase in self.execution_phases:
phase_agents = [a for a in assignments if a.phase == phase]
# Parallel execution within phase
phase_results = asyncio.gather(*[
agent.execute_assignment(assignment)
for agent, assignment in phase_agents
])
The execution engine balances parallelization with coordination by organizing work into sequential phases where agents can work in parallel within each phase. This maximizes efficiency while maintaining logical dependencies.
# Check if replanning needed
if self.requires_replanning(phase_results):
updated_plan = self.replan_remaining_phases(
original_plan=self.master_plan,
completed_phases=[phase],
phase_results=phase_results
)
self.update_agent_assignments(updated_plan)
execution_results[phase] = phase_results
return execution_results
Dynamic replanning enables adaptation to changing conditions during execution. When phase results indicate that the original plan is no longer viable, the system intelligently revises remaining phases rather than rigidly following an outdated plan.
After each phase, the system evaluates whether the original plan is still viable. If not, it dynamically replans the remaining phases based on actual results, ensuring adaptability to changing conditions.
Pattern Synergy Effects¶
Compound Benefits of Pattern Combination¶
- ReAct + Reflection: Robust reasoning with quality assurance - like data processing with built-in validation
- Planning + Tool Use: Strategic tool deployment for complex workflows - like infrastructure-as-code for data platforms
- Multi-Agent + Reflection: Team learning and continuous improvement - like data teams that learn from pipeline failures
- Tool Use + Planning: Dynamic tool selection based on strategic needs - like adaptive query optimization
- All Patterns: Sophisticated autonomous systems with full capabilities - like self-managing data platforms
Part 3: Emerging Patterns - The Future Taking Shape¶
Next-Generation Agentic Patterns - Tomorrow's Intelligence Today¶
Imagine the first data engineers who worked with batch processing systems, thinking that was the pinnacle of data processing. They couldn't envision stream processing, real-time analytics, or AI-driven data platforms. Today's emerging patterns are like glimpsing the future of data intelligence - systems that don't just process information but truly understand, adapt, and evolve.
The five core patterns were just the beginning. Like the invention of the wheel leading to cars, planes, and spacecraft, these foundational patterns are evolving into forms of intelligence that would seem like magic to developers just five years ago.
Here are the patterns that are reshaping what's possible:
Constitutional AI Pattern - Digital Ethics in Action¶
Imagine a data processing system that doesn't just transform data efficiently, but ensures every transformation respects privacy regulations, maintains data integrity, and follows ethical guidelines. What if every AI agent came with an built-in moral compass? Constitutional AI doesn't just make agents smarter - it makes them principled, ensuring they never sacrifice ethics for efficiency or safety for speed:
class ConstitutionalAgent:
"""Implements constitutional AI for ethical and safe agent behavior"""
def __init__(self):
self.constitution = self.load_constitutional_principles()
self.violation_detector = ConstitutionalViolationDetector()
Constitutional AI creates agents with built-in ethical guardrails. The constitution encodes principles like harmlessness, truthfulness, and fairness, while the violation detector identifies when responses might violate these principles. This creates inherently safer AI systems.
def constitutional_response(self, query: str) -> dict:
"""Generate response that adheres to constitutional principles"""
# Generate initial response
initial_response = self.base_agent.generate(query)
# Check for constitutional violations
violations = self.violation_detector.check_response(
response=initial_response,
constitution=self.constitution
)
The two-stage approach first generates a natural response, then evaluates it against constitutional principles. This maintains response quality while ensuring ethical compliance - the agent can think freely but must align its output with ethical guidelines.
if violations:
# Revise response to address violations
revised_response = self.revise_for_constitution(
original=initial_response,
violations=violations,
query=query
)
return {
"response": revised_response,
"constitutional_status": "revised",
"violations_addressed": violations
}
return {
"response": initial_response,
"constitutional_status": "compliant",
"violations_addressed": []
}
When violations are detected, the system automatically revises the response to comply with constitutional principles while maintaining helpfulness. The transparent status reporting enables audit trails and continuous improvement of ethical behavior patterns.
When violations are detected, the system revises the response to comply with constitutional principles while maintaining helpfulness. The return structure provides transparency about any revisions made.
Self-Debugging Pattern - The Self-Healing Mind¶
Think of a data pipeline that doesn't just fail gracefully when errors occur, but actually diagnoses the root cause, implements fixes, and documents what it learned for future prevention. Imagine an AI that doesn't just make mistakes - it learns from them, fixes them, and comes back stronger. The Self-Debugging Pattern creates agents that think like the best software engineers: constantly questioning their own work, testing assumptions, and iteratively improving:
class SelfDebuggingAgent:
"""Agent that can debug and fix its own reasoning errors"""
def self_debugging_execute(self, task: str) -> dict:
"""Execute task with automatic error detection and correction"""
execution_trace = []
max_debug_iterations = 3
for iteration in range(max_debug_iterations):
try:
# Attempt task execution
result = self.execute_task(task)
# Validate result quality
validation_result = self.validate_execution(task, result, execution_trace)
Self-debugging agents implement the troubleshooting patterns that expert developers use instinctively. Each execution attempt includes validation that goes beyond syntax checking to evaluate logical consistency, completeness, and correctness of the solution.
if validation_result.is_valid:
return {
"result": result,
"debug_iterations": iteration,
"execution_trace": execution_trace
}
else:
# Debug and fix issues
debug_analysis = self.analyze_execution_issues(
task=task,
failed_result=result,
validation_issues=validation_result.issues
)
# Apply fixes
self.apply_debugging_fixes(debug_analysis.fixes)
execution_trace.append(debug_analysis)
When validation identifies issues, the system enters diagnostic mode - analyzing what went wrong, why it happened, and what specific changes might fix the problem. This mirrors the debugging process that experienced developers use to identify and resolve complex issues.
except Exception as e:
# Handle execution errors
error_analysis = self.analyze_execution_error(e, task, execution_trace)
self.apply_error_fixes(error_analysis.fixes)
execution_trace.append(error_analysis)
return {
"result": None,
"debug_iterations": max_debug_iterations,
"execution_trace": execution_trace,
"status": "failed_after_debugging"
}
Exception handling treats runtime errors as debugging opportunities rather than failures. The complete execution trace provides a detailed record of all debugging attempts, enabling post-mortem analysis and system improvement even when the agent cannot resolve the issue autonomously.
Exception handling addresses runtime errors through systematic analysis and fix application. If all debugging attempts fail, the system returns a detailed trace of what was attempted, enabling human developers to understand and address the underlying issues.
Meta-Learning Pattern¶
Picture a data processing system that doesn't just optimize individual queries, but learns how to learn from different data patterns, automatically adapting its optimization strategies based on the characteristics of each dataset and workload. Meta-Learning enables agents to adapt their learning strategies based on the domain and task characteristics, essentially "learning how to learn":
class MetaLearningAgent:
"""Agent that learns how to learn and adapt its learning strategies"""
def __init__(self):
self.learning_strategies = LearniningStrategyRegistry()
self.meta_optimizer = MetaLearningOptimizer()
def adaptive_learning_execution(self, new_domain_task: str) -> dict:
"""Execute task in new domain while adapting learning approach"""
# Analyze domain characteristics
domain_analysis = self.analyze_domain(new_domain_task)
# Select appropriate learning strategy
learning_strategy = self.learning_strategies.select_strategy(domain_analysis)
Meta-learning creates agents that don't just solve problems - they learn how to learn more effectively. The domain analysis identifies task characteristics that inform which learning approaches are most likely to succeed, creating adaptive intelligence that improves its own learning processes.
# Execute with continuous meta-learning
execution_results = []
for attempt in range(self.max_learning_attempts):
# Execute with current strategy
result = self.execute_with_strategy(new_domain_task, learning_strategy)
execution_results.append(result)
# Meta-evaluate learning effectiveness
meta_feedback = self.meta_optimizer.evaluate_learning_progress(
results=execution_results,
domain=domain_analysis,
strategy=learning_strategy
)
The execution loop includes a meta-cognitive layer that evaluates not just "did I solve this problem?" but "am I learning effectively?" This second-order evaluation enables the agent to optimize its learning process in real-time, adapting its approach based on how well it's acquiring new capabilities.
# Adapt learning strategy if needed
if meta_feedback.should_adapt:
learning_strategy = self.meta_optimizer.adapt_strategy(
current_strategy=learning_strategy,
feedback=meta_feedback,
domain=domain_analysis
)
return {
"final_result": execution_results[-1],
"learning_progression": execution_results,
"adapted_strategy": learning_strategy,
"meta_insights": meta_feedback
}
When the current learning strategy proves ineffective, the meta-optimizer dynamically adjusts the approach. The complete learning journey is preserved, showing how the agent evolved its learning strategy and what insights it gained about effective learning in this domain.
When the current strategy isn't effective, the meta-optimizer adapts the approach based on what has been learned about this domain. The system returns the complete learning journey, showing how the agent improved its learning approach.
Swarm Intelligence Pattern¶
Imagine a distributed data processing system where multiple processing nodes don't just work in parallel, but actively collaborate, share insights about data patterns, and collectively optimize the entire system's performance. Swarm Intelligence leverages collective problem-solving by coordinating multiple diverse agents, similar to how ant colonies or bee swarms solve complex problems:
class SwarmIntelligenceAgent:
"""Implements swarm intelligence for collective problem solving"""
def swarm_solve(self, complex_problem: str, swarm_size: int = 10) -> dict:
"""Use swarm intelligence for collaborative problem solving"""
# Initialize diverse agent swarm
swarm = self.create_diverse_swarm(swarm_size, complex_problem)
# Swarm exploration phase
exploration_results = self.swarm_exploration(swarm, complex_problem)
Swarm intelligence harnesses the collective problem-solving power of multiple diverse agents. Each agent brings different perspectives and approaches, creating a solution space coverage that no single agent could achieve. This mirrors how ant colonies or bee swarms solve complex problems through distributed intelligence.
# Information sharing and convergence
shared_knowledge = self.share_swarm_knowledge(exploration_results)
# Collaborative solution refinement
refined_solutions = self.swarm_refinement(swarm, shared_knowledge)
The knowledge sharing phase creates collective intelligence by combining individual discoveries into a shared understanding. This enables each agent to benefit from the exploration efforts of all others, leading to solution refinements that incorporate insights from across the entire swarm.
# Consensus building
final_solution = self.build_swarm_consensus(refined_solutions)
return {
"solution": final_solution,
"swarm_size": swarm_size,
"exploration_diversity": self.measure_solution_diversity(exploration_results),
"consensus_strength": self.measure_consensus_strength(refined_solutions),
"collective_intelligence_metrics": self.calculate_swarm_metrics(swarm)
}
Consensus building synthesizes the refined solutions into a final answer that benefits from collective intelligence. The comprehensive metrics provide insight into how effectively the swarm collaborated, measuring both the diversity of exploration and the strength of the final consensus.
Consensus building synthesizes the refined solutions into a final solution that benefits from collective intelligence. The return includes metrics about solution diversity and consensus strength, providing insight into the swarm's problem-solving effectiveness.
Module Summary¶
You've now mastered advanced pattern theory and implementation strategies:
✅ Implementation Strategies: Understood how patterns are implemented across different frameworks ✅ Pattern Combinations: Learned how patterns synergize for sophisticated behaviors ✅ Emerging Patterns: Explored next-generation patterns being developed in research ✅ Strategic Thinking: Can make informed decisions about pattern selection and combination
🔬 Advanced Topics for Further Exploration:
- Constitutional AI research papers
- Meta-learning in multi-agent systems
- Swarm intelligence algorithms
- Pattern composition optimization strategies
📝 Multiple Choice Test - Session 0¶
Test your understanding of advanced pattern theory and implementation strategies:
Question 1: What is the key advantage of LangChain's framework approach over bare metal Python for reflection patterns?
A) Faster execution speed
B) Structured prompt templating and reduced boilerplate code
C) Lower memory usage
D) Built-in GPU acceleration
Question 2: In advanced tool agents, what determines which tool is selected for a given task?
A) Random selection from available tools
B) Task analysis combined with historical performance data
C) Alphabetical ordering of tool names
D) The tool with the most recent update
Question 3: What are the three phases of ReAct + Reflection combination pattern?
A) Planning, execution, evaluation
B) ReAct problem solving, reflection on solution quality, iterative improvement
C) Analysis, synthesis, deployment
D) Input processing, model inference, output generation
Question 4: Which emerging pattern enables agents to debug and fix their own reasoning errors?
A) Constitutional AI Pattern
B) Meta-Learning Pattern
C) Self-Debugging Pattern
D) Swarm Intelligence Pattern
Question 5: What is the primary benefit of combining Planning with Multi-Agent Coordination?
A) Reduced computational costs
B) Simplified code architecture
C) Strategic planning with coordinated execution across specialized agents
D) Faster individual agent performance
🧭 Navigation¶
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