Building Multi-Agent Systems: Sequential vs Parallel

When I first started building AI agents at rooguys.com, I thought more agents meant better results. I was wrong. The real question isn’t how many agents you need, but how they should work together.

Multi-agent systems are everywhere in 2026. Google’s report shows 65% of companies are experimenting with AI agents, but less than 25% have scaled them to production. One reason? They don’t understand how to orchestrate multiple agents effectively.

In this post, I’ll break down the two fundamental patterns for multi-agent systems: sequential and parallel execution. You’ll learn when to use each, see real code examples, and understand the tradeoffs that matter in production.

What is a Multi-Agent System?

A multi-agent system is exactly what it sounds like: multiple AI agents working together to accomplish a task. Instead of one agent doing everything, you have specialized agents that collaborate.

Think of it like a team. You wouldn’t ask one person to design, build, test, and deploy a feature. You’d have a designer, a developer, a QA engineer, and a DevOps specialist. Each brings expertise. Each focuses on their domain.

Multi-agent systems work the same way. You might have:

• A research agent that gathers information
• A planning agent that creates a strategy
• An execution agent that performs tasks
• A review agent that validates results

The magic isn’t in the agents themselves. It’s in how they communicate and coordinate.

Sequential Execution: One After Another

Sequential execution is the simplest pattern. Agents run one after another, with each agent receiving the output of the previous one.

When to Use Sequential

Sequential execution works best when:

1. Each step depends on the previous one. You can’t review code that hasn’t been written. You can’t deploy an app that hasn’t been tested.

2. Order matters. Research must come before planning. Planning must come before execution.

3. You need to catch errors early. If agent 1 fails, you stop before wasting resources on agents 2, 3, and 4.

4. You’re building a pipeline. Content creation, data processing, document analysis. These are natural pipelines.

Sequential Example: Content Creation Pipeline

Let me show you a real example. At rooguys.com, we built a content creation system with three agents:

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class ContentState(TypedDict):
topic: str
research: str
outline: str
draft: str
final: str

def research_agent(state: ContentState) -> dict:
"""Agent 1: Research the topic"""
topic = state["topic"]
# Simulate research - in production, this would call search APIs
research = f"Key findings about {topic}:\n- Point 1\n- Point 2\n- Point 3"
return {"research": research}

def outline_agent(state: ContentState) -> dict:
"""Agent 2: Create outline from research"""
research = state["research"]
# Create structured outline
outline = f"Outline based on research:\n1. Introduction\n2. Main Points\n3. Conclusion"
return {"outline": outline}

def writer_agent(state: ContentState) -> dict:
"""Agent 3: Write content from outline"""
outline = state["outline"]
research = state["research"]
# Generate full content
draft = f"Full article based on outline and research..."
return {"draft": draft}

def editor_agent(state: ContentState) -> dict:
"""Agent 4: Edit and polish"""
draft = state["draft"]
# Polish the content
final = f"Edited and polished version of the draft..."
return {"final": final}

# Build the sequential graph
workflow = StateGraph(ContentState)
workflow.add_node("research", research_agent)
workflow.add_node("outline", outline_agent)
workflow.add_node("writer", writer_agent)
workflow.add_node("editor", editor_agent)

# Define sequential edges
workflow.set_entry_point("research")
workflow.add_edge("research", "outline")
workflow.add_edge("outline", "writer")
workflow.add_edge("writer", "editor")
workflow.add_edge("editor", END)

app = workflow.compile()

This is clean and predictable. Each agent does one thing well. The output flows naturally from one to the next.

Sequential Pros and Cons

Pros:

• Simple to understand and debug
• Clear error handling at each step
• Predictable resource usage
• Easy to add logging and monitoring

Cons:

• Slower overall execution
• One slow agent blocks everything
• No parallelization benefits
• Can feel rigid for complex workflows

Parallel Execution: All at Once

Parallel execution runs multiple agents simultaneously. Each agent works independently, and results are combined at the end.

When to Use Parallel

Parallel execution works best when:

1. Tasks are independent. Analyzing three different documents. Checking three different APIs. Processing three different data sources.

2. Speed matters. You need results fast, and waiting sequentially would take too long.

3. You’re aggregating information. Gathering data from multiple sources to make a decision.

4. You have redundant verification. Multiple agents checking the same thing for accuracy.

Parallel Example: Multi-Source Research

Here’s a parallel system that researches a topic from multiple sources:

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated, List
import operator

def merge_lists(left: list, right: list) -> list:
"""Custom reducer to merge lists from parallel agents"""
return left + right

class ResearchState(TypedDict):
topic: str
web_findings: Annotated[List[str], merge_lists]
academic_findings: Annotated[List[str], merge_lists]
news_findings: Annotated[List[str], merge_lists]
social_findings: Annotated[List[str], merge_lists]
summary: str

def web_research_agent(state: ResearchState) -> dict:
"""Agent 1: Search the web"""
topic = state["topic"]
findings = [f"Web finding 1 about {topic}", f"Web finding 2 about {topic}"]
return {"web_findings": findings}

def academic_research_agent(state: ResearchState) -> dict:
"""Agent 2: Search academic papers"""
topic = state["topic"]
findings = [f"Academic paper 1 on {topic}", f"Academic paper 2 on {topic}"]
return {"academic_findings": findings}

def news_research_agent(state: ResearchState) -> dict:
"""Agent 3: Search news sources"""
topic = state["topic"]
findings = [f"News article 1 about {topic}", f"News article 2 about {topic}"]
return {"news_findings": findings}

def social_research_agent(state: ResearchState) -> dict:
"""Agent 4: Analyze social media"""
topic = state["topic"]
findings = [f"Social trend 1 about {topic}", f"Social trend 2 about {topic}"]
return {"social_findings": findings}

def synthesis_agent(state: ResearchState) -> dict:
"""Agent 5: Combine all findings"""
all_findings = (
state["web_findings"] +
state["academic_findings"] +
state["news_findings"] +
state["social_findings"]
)
summary = f"Synthesized summary from {len(all_findings)} sources"
return {"summary": summary}

# Build the parallel graph
workflow = StateGraph(ResearchState)
workflow.add_node("web", web_research_agent)
workflow.add_node("academic", academic_research_agent)
workflow.add_node("news", news_research_agent)
workflow.add_node("social", social_research_agent)
workflow.add_node("synthesis", synthesis_agent)

# All research agents run in parallel, then synthesis
workflow.set_entry_point("web")  # Entry doesn't matter much for parallel
workflow.add_edge("web", "synthesis")
workflow.add_edge("academic", "synthesis")
workflow.add_edge("news", "synthesis")
workflow.add_edge("social", "synthesis")
workflow.add_edge("synthesis", END)

app = workflow.compile()

In LangGraph, when multiple edges point to the same node, they execute in parallel. The synthesis agent waits for all four research agents to complete before running.

Parallel Pros and Cons

Pros:

• Much faster for independent tasks
• Better resource utilization
• Can aggregate diverse perspectives
• Natural for data gathering workflows

Cons:

• More complex error handling
• Harder to debug when things go wrong
• Need to handle partial failures
• State management gets tricky

Hybrid Patterns: The Best of Both Worlds

Real production systems rarely use pure sequential or pure parallel. They combine both.

Pattern 1: Parallel Research, Sequential Execution

Research in parallel, then execute sequentially:

[Research Agent 1] ─┐
[Research Agent 2] ─┼─> [Planning Agent] -> [Execution Agent] -> [Review Agent]
[Research Agent 3] ─┘

This is common in automated workflows. You gather information quickly, then process it carefully.

Pattern 2: Sequential with Parallel Verification

Execute sequentially, but verify in parallel:

[Research] -> [Planning] -> [Execution] -> [Review Agent 1]
└> [Review Agent 2]
                                           [Review Agent 3]
│
v
                                            [Final Decision]

Multiple reviewers catch more errors. This is how we handle critical operations at rooguys.com.

Hybrid Example: Production Content System

Here’s a more realistic example that combines both patterns:

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated, List, Optional
import operator

class ProductionContentState(TypedDict):
topic: str
# Parallel research outputs
competitor_analysis: str
keyword_research: str
trend_analysis: str
# Sequential outputs
brief: str
outline: str
draft: str
# Parallel review outputs
seo_score: int
quality_score: int
fact_check_results: str
# Final output
final_content: str
approved: bool

# Parallel research agents
def competitor_agent(state: ProductionContentState) -> dict:
return {"competitor_analysis": "Competitor insights..."}

def keyword_agent(state: ProductionContentState) -> dict:
return {"keyword_research": "Target keywords..."}

def trend_agent(state: ProductionContentState) -> dict:
return {"trend_analysis": "Current trends..."}

# Sequential creation agents
def brief_agent(state: ProductionContentState) -> dict:
# Combines all research into a brief
brief = f"Content brief based on research..."
return {"brief": brief}

def outline_agent(state: ProductionContentState) -> dict:
return {"outline": "Detailed outline..."}

def writer_agent(state: ProductionContentState) -> dict:
return {"draft": "Full draft content..."}

# Parallel review agents
def seo_reviewer(state: ProductionContentState) -> dict:
return {"seo_score": 85}

def quality_reviewer(state: ProductionContentState) -> dict:
return {"quality_score": 90}

def fact_checker(state: ProductionContentState) -> dict:
return {"fact_check_results": "All facts verified"}

# Final decision agent
def final_reviewer(state: ProductionContentState) -> dict:
seo_ok = state["seo_score"] >= 80
quality_ok = state["quality_score"] >= 80
facts_ok = "verified" in state["fact_check_results"].lower()

approved = seo_ok and quality_ok and facts_ok

if approved:
return {
"final_content": state["draft"],
"approved": True
}
else:
return {
"final_content": "Needs revision",
"approved": False
}

# Build the hybrid graph
workflow = StateGraph(ProductionContentState)

# Add all nodes
workflow.add_node("competitor", competitor_agent)
workflow.add_node("keyword", keyword_agent)
workflow.add_node("trend", trend_agent)
workflow.add_node("brief", brief_agent)
workflow.add_node("outline", outline_agent)
workflow.add_node("writer", writer_agent)
workflow.add_node("seo_review", seo_reviewer)
workflow.add_node("quality_review", quality_reviewer)
workflow.add_node("fact_check", fact_checker)
workflow.add_node("final_review", final_reviewer)

# Parallel research phase
workflow.set_entry_point("competitor")
workflow.add_edge("competitor", "brief")
workflow.add_edge("keyword", "brief")
workflow.add_edge("trend", "brief")

# Sequential creation phase
workflow.add_edge("brief", "outline")
workflow.add_edge("outline", "writer")

# Parallel review phase
workflow.add_edge("writer", "seo_review")
workflow.add_edge("writer", "quality_review")
workflow.add_edge("writer", "fact_check")

# Final decision
workflow.add_edge("seo_review", "final_review")
workflow.add_edge("quality_review", "final_review")
workflow.add_edge("fact_check", "final_review")
workflow.add_edge("final_review", END)

app = workflow.compile()

This hybrid approach gives you speed where it matters (research, review) and control where it counts (content creation).

Using Google’s Agent Development Kit (ADK)

Google’s Agent Development Kit (ADK) is a newer framework that’s gaining traction in 2026. It’s designed to make agent development feel more like traditional software development, with built-in support for sequential, parallel, and loop patterns.

ADK is model-agnostic (works with Gemini, OpenAI, Anthropic, and others) and deployment-agnostic (run locally, on Cloud Run, or Vertex AI Agent Engine). Let me show you how the same patterns look in ADK.

ADK Sequential Pipeline

ADK provides a SequentialAgent primitive that handles the orchestration automatically. The key is using output_key to pass data between agents:

from google.adk.agents import LlmAgent, SequentialAgent
from google.adk.tools import FunctionTool

# Define tools for each agent
def parse_pdf(file_path: str) -> str:
"""Parse PDF and extract text"""
# Your PDF parsing logic
return "Extracted text from PDF..."

def extract_data(text: str) -> dict:
"""Extract structured data from text"""
# Your extraction logic
return {"entities": [], "dates": [], "amounts": []}

def generate_summary(data: dict) -> str:
"""Generate summary from structured data"""
# Your summarization logic
return "Summary of extracted data..."

# Step 1: Parser Agent
parser = LlmAgent(
name="ParserAgent",
model="gemini-2.0-flash",
instruction="Parse the PDF file and extract all text content.",
tools=[FunctionTool(parse_pdf)],
output_key="raw_text"  # Writes to session.state["raw_text"]
)

# Step 2: Extractor Agent
extractor = LlmAgent(
name="ExtractorAgent",
model="gemini-2.0-flash",
instruction="Extract structured data from {raw_text}. Look for entities, dates, and amounts.",
tools=[FunctionTool(extract_data)],
output_key="structured_data"
)

# Step 3: Summarizer Agent
summarizer = LlmAgent(
name="SummarizerAgent",
model="gemini-2.0-flash",
instruction="Generate a concise summary from {structured_data}.",
tools=[FunctionTool(generate_summary)],
output_key="final_summary"
)

# Orchestrate with SequentialAgent
pipeline = SequentialAgent(
name="DocumentProcessingPipeline",
sub_agents=[parser, extractor, summarizer]
)

Notice how ADK uses {variable} syntax in instructions to reference state values. This makes the data flow explicit and readable.

ADK Parallel Fan-Out/Gather

For parallel execution, ADK provides ParallelAgent. Each agent writes to a unique key to avoid race conditions:

from google.adk.agents import LlmAgent, ParallelAgent, SequentialAgent

# Parallel review agents
security_auditor = LlmAgent(
name="SecurityAuditor",
model="gemini-2.0-flash",
instruction="Review the code for security vulnerabilities: injection attacks, auth issues, data exposure.",
output_key="security_report"
)

style_checker = LlmAgent(
name="StyleEnforcer",
model="gemini-2.0-flash",
instruction="Check code for style compliance, formatting issues, and best practices.",
output_key="style_report"
)

performance_analyst = LlmAgent(
name="PerformanceAnalyst",
model="gemini-2.0-flash",
instruction="Analyze time complexity, memory usage, and potential bottlenecks.",
output_key="performance_report"
)

# Fan-out: Run all reviews in parallel
parallel_reviews = ParallelAgent(
name="CodeReviewSwarm",
sub_agents=[security_auditor, style_checker, performance_analyst]
)

# Gather: Synthesize all reports
pr_summarizer = LlmAgent(
name="PRSummarizer",
model="gemini-2.0-flash",
instruction="""Create a consolidated Pull Request review using:
    - Security: {security_report}
    - Style: {style_report}
    - Performance: {performance_report}

Provide actionable feedback organized by priority.""",
output_key="final_review"
)

# Combine: Parallel reviews followed by synthesis
workflow = SequentialAgent(
name="CodeReviewWorkflow",
sub_agents=[parallel_reviews, pr_summarizer]
)

The ParallelAgent runs all sub-agents simultaneously in separate threads, but they share the same session state. That’s why unique output_key values are critical.

ADK Coordinator/Dispatcher Pattern

ADK supports LLM-driven routing where a coordinator agent decides which specialist to invoke:

from google.adk.agents import LlmAgent

# Specialist agents
billing_specialist = LlmAgent(
name="BillingSpecialist",
description="Handles billing inquiries, invoices, payment issues, and refund requests.",
model="gemini-2.0-flash",
instruction="Help the user with billing-related questions. Access the billing system as needed."
)

tech_support = LlmAgent(
name="TechSupportSpecialist",
description="Troubleshoots technical issues, bugs, errors, and platform problems.",
model="gemini-2.0-flash",
instruction="Help the user resolve technical issues. Use diagnostic tools when appropriate."
)

account_manager = LlmAgent(
name="AccountManager",
description="Handles account settings, profile changes, subscriptions, and access issues.",
model="gemini-2.0-flash",
instruction="Help the user with account-related requests."
)

# Coordinator with auto-routing
coordinator = LlmAgent(
name="SupportCoordinator",
model="gemini-2.0-flash",
instruction="""You are a support coordinator. Analyze the user's request and route to the appropriate specialist.

    - Billing issues -> BillingSpecialist
    - Technical problems -> TechSupportSpecialist  
    - Account changes -> AccountManager

If unsure, ask clarifying questions first.""",
sub_agents=[billing_specialist, tech_support, account_manager]
)

ADK’s AutoFlow mechanism uses the description field of sub-agents to make routing decisions. Be precise with your descriptions, they’re effectively your API documentation for the LLM.

ADK Generator-Critic with Loop

ADK’s LoopAgent enables iterative refinement patterns:

from google.adk.agents import LlmAgent, LoopAgent, SequentialAgent

# Generator: Creates SQL queries
generator = LlmAgent(
name="SQLGenerator",
model="gemini-2.0-flash",
instruction="""Generate a SQL query based on the user's request.

If you receive {feedback}, fix the errors and regenerate the query.

Output only the SQL query, nothing else.""",
output_key="draft_query"
)

# Critic: Validates the SQL
critic = LlmAgent(
name="SQLCritic",
model="gemini-2.0-flash",
instruction="""Review {draft_query} for:
    1. SQL syntax correctness
    2. Valid table and column names
    3. No SQL injection vulnerabilities

If the query is valid, output exactly: PASS
If invalid, output the specific errors to fix.""",
output_key="feedback"
)

# Loop until PASS or max iterations
validation_loop = LoopAgent(
name="SQLValidationLoop",
sub_agents=[generator, critic],
max_iterations=5,
exit_condition="PASS"  # Exits when critic outputs "PASS"
)

# Complete workflow
sql_workflow = SequentialAgent(
name="SQLQueryWorkflow",
sub_agents=[validation_loop]
)

This pattern is powerful for code generation, content creation, or any task where quality matters more than speed.

ADK vs LangGraph: When to Choose What

Feature

ADK

LangGraph

Learning curve

Gentler, more opinionated

Steeper, more flexible

State management

Built-in session.state

Manual StateGraph setup

Deployment

Native GCP integration

Any platform

Loop support

First-class LoopAgent

Requires conditional edges

Debugging

Built-in tracing

Manual instrumentation

Model support

Model-agnostic

Model-agnostic

Choose ADK if:

• You’re already in the Google Cloud ecosystem
• You want built-in deployment to Vertex AI
• You prefer convention over configuration
• You need quick prototyping

Choose LangGraph if:

• You need maximum control over execution flow
• You’re building complex cyclic workflows
• You want framework-agnostic deployment
• You have existing LangChain investments

Both are production-ready. The best choice depends on your existing stack and team familiarity.

Error Handling in Multi-Agent Systems

This is where most systems fail in production. You need to handle errors gracefully.

Sequential Error Handling

In sequential systems, you can stop early:

def safe_sequential_agent(state, agent_func, agent_name):
"""Wrapper that handles errors in sequential execution"""
try:
result = agent_func(state)
return result
except Exception as e:
print(f"Agent {agent_name} failed: {e}")
return {"error": str(e), "failed_at": agent_name}

# In your workflow, check for errors
def check_for_errors(state):
if "error" in state:
return END  # Stop the workflow
return "next_agent"

Parallel Error Handling

In parallel systems, you need to decide: fail all, or continue with partial results?

from concurrent.futures import ThreadPoolExecutor, as_completed

def run_parallel_agents(agents, state):
"""Run agents in parallel with error handling"""
results = {}
errors = []

with ThreadPoolExecutor(max_workers=len(agents)) as executor:
futures = {
executor.submit(agent.run, state): name
for name, agent in agents.items()
}

for future in as_completed(futures):
agent_name = futures[future]
try:
results[agent_name] = future.result()
except Exception as e:
errors.append((agent_name, str(e)))
# Decide: continue or fail?
# Option 1: Continue with partial results
results[agent_name] = None
# Option 2: Fail everything
# raise e

return results, errors

At rooguys.com, we use a “graceful degradation” approach. If one research agent fails, we continue with the others. But if a critical agent fails, we stop everything.

Cost Considerations

Multi-agent systems can get expensive. Here’s how to manage costs:

Sequential is Cheaper (Usually)

With sequential execution, you can stop early if something fails. You don’t pay for agents that never run.

Parallel Costs Add Up

Running 5 agents in parallel means 5 API calls at once. If each call costs $0.01 and you run 1000 requests per day, that’s $50/day just for one workflow.

Cost Optimization Strategies

1. Cache aggressively. If an agent’s input hasn’t changed, reuse its output.

import hashlib
import json

def get_cached_result(agent_name, input_state, cache):
"""Check cache before running agent"""
cache_key = hashlib.md5(
json.dumps(input_state, sort_keys=True).encode()
).hexdigest()

full_key = f"{agent_name}:{cache_key}"

if full_key in cache:
return cache[full_key]

return None

1. Use cheaper models for simple agents. Not every agent needs GPT-4. Use GPT-3.5 or local models for research and data gathering.

2. Implement timeouts. Don’t let agents run forever.

import signal
from contextlib import contextmanager

class TimeoutError(Exception):
pass

@contextmanager
def timeout(seconds):
def handler(signum, frame):
raise TimeoutError("Agent timed out")
signal.signal(signal.SIGALRM, handler)
signal.alarm(seconds)
try:
yield
finally:
signal.alarm(0)

# Usage
try:
with timeout(30):  # 30 second timeout
result = agent.run(state)
except TimeoutError:
result = {"error": "timeout"}

Observability: Seeing What’s Happening

You can’t debug what you can’t see. Multi-agent systems need good observability.

What to Track

1. Agent execution time. Which agents are slow?
2. Token usage per agent. Which agents are expensive?
3. Success/failure rates. Which agents fail most?
4. State transitions. What data flows between agents?

Simple Logging Approach

import time
import json
from datetime import datetime

class AgentLogger:
def __init__(self, workflow_name):
self.workflow_name = workflow_name
self.logs = []

def log_agent_start(self, agent_name, input_state):
self.logs.append({
"timestamp": datetime.now().isoformat(),
"event": "agent_start",
"agent": agent_name,
"input_keys": list(input_state.keys())
})

def log_agent_end(self, agent_name, output, duration_ms, tokens_used=0):
self.logs.append({
"timestamp": datetime.now().isoformat(),
"event": "agent_end",
"agent": agent_name,
"duration_ms": duration_ms,
"tokens_used": tokens_used,
"output_keys": list(output.keys()) if output else None
})

def log_error(self, agent_name, error):
self.logs.append({
"timestamp": datetime.now().isoformat(),
"event": "error",
"agent": agent_name,
"error": str(error)
})

# Usage in agent wrapper
def logged_agent(agent_func, agent_name, logger):
def wrapper(state):
logger.log_agent_start(agent_name, state)
start_time = time.time()
try:
result = agent_func(state)
duration = (time.time() - start_time) * 1000
logger.log_agent_end(agent_name, result, duration)
return result
except Exception as e:
logger.log_error(agent_name, e)
raise
return wrapper

Choosing the Right Pattern

Here’s a decision framework:

Situation

Pattern

Why

Each step needs previous output

Sequential

Dependencies require ordering

Tasks are completely independent

Parallel

No reason to wait

Speed is critical

Parallel

Maximize throughput

Cost is critical

Sequential

Can stop early on failure

Need multiple perspectives

Parallel

Gather diverse inputs

Building a pipeline

Sequential

Natural flow

Complex workflow

Hybrid

Best of both worlds

Real-World Lessons from Production

After running multi-agent systems in production at rooguys.com, here are my key takeaways:

1. Start Simple

Don’t build a 10-agent system on day one. Start with 2-3 agents. Add more only when you understand the flow.

2. Agent Specialization Matters

General-purpose agents seem flexible but create confusion. Specialized agents with clear responsibilities are easier to debug and improve.

3. State Management is Hard

As your system grows, state becomes complex. Use typed state classes and validate state transitions.

from pydantic import BaseModel, validator
from typing import List, Optional

class AgentState(BaseModel):
topic: str
research: Optional[str] = None
outline: Optional[str] = None
draft: Optional[str] = None

@validator('research')
def research_must_not_be_empty(cls, v):
if v is not None and len(v.strip()) == 0:
raise ValueError('Research cannot be empty string')
return v

4. Test Each Agent Independently

Before testing the full system, test each agent in isolation. Mock inputs and verify outputs.

def test_research_agent():
state = {"topic": "AI Agents"}
result = research_agent(state)
assert "research" in result
assert len(result["research"]) > 0
print("Research agent test passed")

5. Monitor Token Usage

LLM costs can spiral quickly. Set budgets and alerts.

Frameworks to Consider

Several frameworks make building multi-agent systems easier:

• Google ADK: Purpose-built for multi-agent systems with native sequential, parallel, and loop primitives. Great for GCP deployments.
• LangGraph: Excellent for complex workflows with cycles and state management. Maximum flexibility.
• CrewAI: Great for role-based agent teams with human-like collaboration patterns.
• AutoGen: Microsoft’s framework for conversational agents and multi-agent dialogs.
• LlamaIndex: Strong for RAG-based multi-agent systems with document-heavy workflows.

I use both ADK and LangGraph in production. ADK for Google Cloud deployments and rapid prototyping. LangGraph for complex workflows that need fine-grained control.

Conclusion

Multi-agent systems are powerful, but they require careful design. Sequential execution gives you control and simplicity. Parallel execution gives you speed and diversity. Hybrid patterns give you both.

The key is understanding your use case. Ask yourself:

1. Do my agents depend on each other’s output?
2. Does speed matter more than cost?
3. Do I need multiple perspectives or a single pipeline?

Answer these questions, and the right pattern becomes clear.

Start small. Test often. Monitor everything. And remember: more agents doesn’t mean better results. The right architecture does.

—

*Have questions about building multi-agent systems? Connect with me on LinkedIn or check out my other posts on mbakayoko.com.*