⚙️ Session 7 Advanced: Multi-Agent Orchestration¶
⚙️ IMPLEMENTER PATH CONTENT Prerequisites: Complete all previous modules (Observer, Participant, Advanced Reasoning, Production Systems) Time Investment: 4-5 hours Outcome: Master collaborative intelligence with multi-agent RAG systems
Advanced Learning Outcomes¶
After completing this multi-agent orchestration module, you will master:
- Collaborative multi-agent RAG architectures
- Specialized agent role implementations
- Agent coordination and communication patterns
- Advanced task distribution strategies
- Comprehensive multi-agent validation systems
Multi-Agent RAG Architecture¶
The most sophisticated information challenges require specialized expertise that no single agent can provide comprehensively. Consider a question about implementing a new AI policy: it requires legal expertise for regulatory compliance, technical expertise for implementation feasibility, financial expertise for cost analysis, and strategic expertise for business impact assessment.
Multi-agent RAG orchestration creates collaborative intelligence where specialized agents contribute their unique capabilities to complex information challenges, coordinated by an orchestration layer that ensures coherent, comprehensive responses.
Foundation Architecture¶
# Multi-agent collaborative RAG system foundations
from abc import ABC, abstractmethod
from enum import Enum
class AgentRole(Enum):
RESEARCHER = "researcher"
ANALYZER = "analyzer"
SYNTHESIZER = "synthesizer"
VALIDATOR = "validator"
COORDINATOR = "coordinator"
These imports establish our multi-agent framework. The ABC ensures all agents follow the same interface, while Enum creates type-safe role definitions. Each role represents a specialized function: researchers gather information, analyzers interpret data, synthesizers combine insights, validators ensure quality, and coordinators orchestrate the entire process.
class RAGAgent(ABC):
"""Base class for specialized RAG agents."""
def __init__(self, role: AgentRole, llm_model, specialized_tools: List[Tool] = None):
self.role = role
self.llm_model = llm_model
self.specialized_tools = specialized_tools or []
self.agent_id = f"{role.value}_{id(self)}"
@abstractmethod
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Process assigned task and return results."""
pass
@abstractmethod
def get_capabilities(self) -> List[str]:
"""Return list of agent capabilities."""
pass
The RAGAgent base class defines the contract that all specialized agents must follow. Each agent gets a unique ID combining its role and memory address, ensuring traceability in collaborative workflows.
Specialized Agent Implementations¶
Researcher Agent¶
class ResearcherAgent(RAGAgent):
"""Specialized agent for information research and retrieval."""
def __init__(self, llm_model, vector_store, web_search_tool=None):
super().__init__(AgentRole.RESEARCHER, llm_model,
[web_search_tool] if web_search_tool else [])
self.vector_store = vector_store
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Research information for given query or sub-query."""
query = task['query']
research_depth = task.get('depth', 'standard')
print(f"Researcher agent processing: {query[:100]}...")
# Multi-source research
research_results = {}
# Vector search
vector_results = await self._perform_vector_research(query, research_depth)
research_results['vector_search'] = vector_results
The ResearcherAgent specializes in information gathering from multiple sources. It inherits the base agent structure but adds specific capabilities for vector database searches and optional web search tools.
# Web search if tool available
if self.specialized_tools:
web_results = await self.specialized_tools[0].execute(query, task)
research_results['web_search'] = web_results
# Consolidate findings
consolidated_findings = await self._consolidate_research(
query, research_results
)
return {
'agent_id': self.agent_id,
'task_completed': True,
'research_results': research_results,
'consolidated_findings': consolidated_findings,
'confidence': self._calculate_research_confidence(research_results),
'sources_found': self._count_total_sources(research_results)
}
After gathering information from multiple sources, the researcher consolidates findings to eliminate redundancy and identify key insights. The return structure includes confidence metrics and source counts, enabling other agents to assess the reliability and comprehensiveness of the research.
def get_capabilities(self) -> List[str]:
return [
"vector_database_search",
"web_research",
"information_consolidation",
"source_evaluation"
]
The capabilities list acts as a service directory for the orchestrator, allowing it to match tasks with the most appropriate agents.
Analyzer Agent¶
class AnalyzerAgent(RAGAgent):
"""Specialized agent for information analysis and interpretation."""
def __init__(self, llm_model, analysis_tools: List[Tool] = None):
super().__init__(AgentRole.ANALYZER, llm_model, analysis_tools or [])
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze provided information and extract insights."""
information = task['information']
analysis_type = task.get('analysis_type', 'comprehensive')
print(f"Analyzer agent processing {analysis_type} analysis...")
# Perform different types of analysis
analysis_results = {}
The AnalyzerAgent transforms raw information into structured insights through various analytical lenses. Unlike researchers who gather data, analyzers interpret patterns, relationships, and implications.
if analysis_type in ['comprehensive', 'factual']:
factual_analysis = await self._perform_factual_analysis(information)
analysis_results['factual'] = factual_analysis
if analysis_type in ['comprehensive', 'comparative']:
comparative_analysis = await self._perform_comparative_analysis(information)
analysis_results['comparative'] = comparative_analysis
if analysis_type in ['comprehensive', 'trend']:
trend_analysis = await self._perform_trend_analysis(information)
analysis_results['trend'] = trend_analysis
Different analysis types provide specialized insights: factual analysis validates claims, comparative analysis identifies similarities and differences, and trend analysis identifies patterns over time.
# Generate insights summary
insights_summary = await self._generate_insights_summary(
information, analysis_results, analysis_type
)
return {
'agent_id': self.agent_id,
'task_completed': True,
'analysis_results': analysis_results,
'insights_summary': insights_summary,
'analysis_confidence': self._calculate_analysis_confidence(analysis_results),
'key_findings': self._extract_key_findings(analysis_results)
}
def get_capabilities(self) -> List[str]:
return [
"factual_analysis",
"comparative_analysis",
"trend_analysis",
"insight_extraction",
"pattern_recognition"
]
The analyzer provides structured insights and confidence metrics that other agents can use to make informed decisions about information reliability and significance.
Synthesizer Agent¶
class SynthesizerAgent(RAGAgent):
"""Specialized agent for information synthesis and integration."""
def __init__(self, llm_model):
super().__init__(AgentRole.SYNTHESIZER, llm_model)
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Synthesize multiple information sources into coherent response."""
sources = task['sources'] # From multiple agents
query = task['original_query']
synthesis_requirements = task.get('requirements', {})
print(f"Synthesizer agent integrating {len(sources)} sources...")
# Build comprehensive synthesis prompt
synthesis_prompt = f"""
Synthesize the following information sources into a comprehensive response:
Original Query: {query}
Information Sources:
{self._format_sources_for_synthesis(sources)}
Requirements:
- Integrate all relevant information coherently
- Resolve any contradictions by noting source reliability
- Maintain logical flow and structure
- Cite sources appropriately
- Highlight key insights from analysis
"""
The SynthesizerAgent combines information from multiple specialized agents into coherent, comprehensive responses. It receives outputs from researchers and analyzers and creates unified answers.
try:
synthesis_response = await self.llm_model.generate(
synthesis_prompt, temperature=0.3
)
# Assess synthesis quality
synthesis_quality = await self._assess_synthesis_quality(
synthesis_response, sources, query
)
return {
'agent_id': self.agent_id,
'task_completed': True,
'synthesized_response': synthesis_response,
'synthesis_quality': synthesis_quality,
'sources_integrated': len(sources),
'integration_complexity': self._calculate_integration_complexity(sources)
}
except Exception as e:
return {
'agent_id': self.agent_id,
'task_completed': False,
'error': str(e)
}
def get_capabilities(self) -> List[str]:
return [
"multi_source_integration",
"contradiction_resolution",
"coherent_response_generation",
"source_attribution"
]
The synthesizer provides quality metrics and integration complexity scores, helping the orchestrator understand how challenging the synthesis task was and how reliable the final response is.
Validator Agent¶
class ValidatorAgent(RAGAgent):
"""Specialized agent for response validation and quality assessment."""
def __init__(self, llm_model, fact_checker=None):
super().__init__(AgentRole.VALIDATOR, llm_model)
self.fact_checker = fact_checker
async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
"""Validate response quality and accuracy."""
response = task['response']
original_query = task['original_query']
sources = task.get('sources', [])
print("Validator agent assessing response quality...")
validation_results = {}
# Factual consistency validation
factual_validation = await self._validate_factual_consistency(
response, sources
)
validation_results['factual_consistency'] = factual_validation
The ValidatorAgent ensures response quality through multiple validation dimensions. It checks factual accuracy, logical consistency, completeness, and source attribution.
# Logical coherence validation
logical_validation = await self._validate_logical_coherence(
response, original_query
)
validation_results['logical_coherence'] = logical_validation
# Completeness validation
completeness_validation = await self._validate_completeness(
response, original_query
)
validation_results['completeness'] = completeness_validation
# Overall quality assessment
overall_quality = self._calculate_overall_quality(validation_results)
return {
'agent_id': self.agent_id,
'task_completed': True,
'validation_results': validation_results,
'overall_quality': overall_quality,
'validation_passed': overall_quality >= 0.7,
'improvement_suggestions': self._generate_improvement_suggestions(
validation_results
)
}
def get_capabilities(self) -> List[str]:
return [
"factual_consistency_check",
"logical_coherence_validation",
"completeness_assessment",
"quality_scoring",
"improvement_recommendations"
]
The validator provides comprehensive quality assessment and actionable improvement suggestions, enabling iterative refinement of responses.
Multi-Agent Orchestration Framework¶
Coordinator Agent¶
class CoordinatorAgent(RAGAgent):
"""Orchestrates collaboration between specialized agents."""
def __init__(self, llm_model, available_agents: Dict[AgentRole, RAGAgent]):
super().__init__(AgentRole.COORDINATOR, llm_model)
self.available_agents = available_agents
self.execution_history = []
async def orchestrate_response(self, query: str,
orchestration_config: Dict = None) -> Dict[str, Any]:
"""Orchestrate multi-agent response generation."""
config = orchestration_config or {
'enable_parallel_execution': True,
'validation_rounds': 2,
'max_iterations': 3,
'quality_threshold': 0.8
}
print(f"Coordinating multi-agent response for: {query[:100]}...")
# Step 1: Task decomposition and planning
execution_plan = await self._create_execution_plan(query, config)
The CoordinatorAgent orchestrates the entire multi-agent workflow, decomposing complex queries into specialized tasks and coordinating agent collaboration.
# Step 2: Execute research phase
research_tasks = execution_plan['research_tasks']
research_results = await self._execute_research_phase(
research_tasks, config
)
# Step 3: Execute analysis phase
analysis_results = await self._execute_analysis_phase(
research_results, execution_plan, config
)
# Step 4: Synthesize integrated response
synthesis_result = await self._execute_synthesis_phase(
query, research_results, analysis_results, config
)
The coordinator manages the workflow through distinct phases: research gathers information, analysis provides insights, and synthesis creates the final response.
# Step 5: Validate and refine response
final_response = synthesis_result['synthesized_response']
for round_num in range(config['validation_rounds']):
validation_result = await self._execute_validation_phase(
final_response, query, research_results, config
)
if validation_result['validation_passed']:
break
# Apply improvements based on validation feedback
final_response = await self._apply_validation_improvements(
final_response, validation_result, config
)
return {
'query': query,
'final_response': final_response,
'execution_plan': execution_plan,
'research_results': research_results,
'analysis_results': analysis_results,
'validation_results': validation_result,
'orchestration_metadata': {
'agents_used': list(execution_plan['agents_required']),
'total_execution_time': execution_plan.get('execution_time', 0),
'validation_rounds_needed': round_num + 1
}
}
The coordinator ensures quality through iterative validation and refinement, providing complete transparency into the collaborative process.
Task Distribution Strategy¶
async def _create_execution_plan(self, query: str, config: Dict) -> Dict[str, Any]:
"""Create detailed execution plan for multi-agent collaboration."""
planning_prompt = f"""
Analyze this query and create an execution plan for multi-agent collaboration:
Query: {query}
Available agents and their capabilities:
{self._format_agent_capabilities()}
Create a JSON execution plan with:
1. Required agents for this query
2. Task decomposition for each agent
3. Dependencies between tasks
4. Parallel execution opportunities
"""
try:
response = await self.llm_model.generate(planning_prompt, temperature=0.2)
execution_plan = json.loads(self._extract_json_from_response(response))
# Validate plan feasibility
validated_plan = self._validate_execution_plan(execution_plan)
return validated_plan
except Exception as e:
print(f"Planning error: {e}")
return self._create_fallback_plan(query)
The execution planning uses the LLM to analyze query requirements and match them with available agent capabilities, creating optimized task distribution strategies.
Advanced Collaboration Patterns¶
async def _execute_parallel_tasks(self, tasks: List[Dict],
config: Dict) -> Dict[str, Any]:
"""Execute multiple tasks in parallel for efficiency."""
if not config.get('enable_parallel_execution', True):
return await self._execute_sequential_tasks(tasks, config)
# Group tasks by dependency level
task_groups = self._group_tasks_by_dependencies(tasks)
all_results = {}
for group_level, task_group in enumerate(task_groups):
print(f"Executing task group {group_level + 1}/{len(task_groups)}")
# Execute tasks in this group in parallel
group_tasks = []
for task in task_group:
agent = self.available_agents[task['agent_role']]
group_tasks.append(agent.process_task(task))
# Wait for all tasks in group to complete
group_results = await asyncio.gather(*group_tasks)
# Merge results for next group's dependency resolution
for result in group_results:
all_results[result['agent_id']] = result
return all_results
Parallel execution with dependency management significantly improves performance while ensuring logical task ordering.
This comprehensive multi-agent orchestration system provides sophisticated collaborative intelligence capabilities that exceed what any single agent could achieve independently.
Advanced Integration and Deployment¶
Multi-agent systems require sophisticated monitoring, debugging, and optimization capabilities for production deployment.
Agent Communication Protocol¶
class AgentCommunicationProtocol:
"""Manages communication between agents in the orchestration system."""
def __init__(self):
self.message_queue = asyncio.Queue()
self.agent_registry = {}
self.communication_history = []
async def register_agent(self, agent: RAGAgent) -> None:
"""Register agent for communication."""
self.agent_registry[agent.agent_id] = agent
print(f"Registered agent: {agent.agent_id}")
async def send_message(self, sender_id: str, receiver_id: str,
message: Dict[str, Any]) -> None:
"""Send message between agents."""
message_envelope = {
'sender': sender_id,
'receiver': receiver_id,
'message': message,
'timestamp': datetime.now().isoformat()
}
await self.message_queue.put(message_envelope)
self.communication_history.append(message_envelope)
The communication protocol enables agents to exchange information, request assistance, and coordinate complex workflows beyond simple task-result patterns.
This multi-agent orchestration system represents the pinnacle of collaborative intelligence in RAG systems, providing sophisticated coordination capabilities for complex information challenges.
🧭 Navigation¶
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