Practical Lessons from Building Multi-Agent Systems with CrewAI and LangChain
This post is a field guide packed with pro tips from the trenches of building real AI agents. You’ll learn how to structure tasks, keep agents focused, speed up execution, and keep your system stable as it grows.
Building multi-agent systems with LLMs? It's nothing like building regular software. Actually, it's way closer to doing data science. You form a hypothesis, test it out, tweak things, and then do it all over again. And again. If you think your agents and tasks are going to work perfectly right from the start, well, I hate to break it to you, but that's not how this works.
I've been knee-deep in building and refining agent-based systems for the past few months, and let me tell you, using CrewAI and LangChain has been a game-changer. Not just because I can build faster, but because experimenting and iterating becomes so much easier. If you're looking for a step-by-step guide to building multi-agent AI systems with CrewAI, we cover reusable patterns, guardrails, and YAML-first workflows.
So this post? It's basically me sharing what's actually worked. Not a tutorial, not some feature tour. Think of it more like notes from the field. How to structure tasks so they don't fall apart, how to keep agents from wandering off into the weeds, how to make things run faster, and honestly, how to keep the whole system from imploding as it grows. With any luck, this saves you some time and more than a few headaches.
Start with Tasks, Keep Them Small
Clearly Define and Structure Your Tasks
When I first started building multi-agent systems, I quickly realized you absolutely need to start by clearly defining your tasks. Writing out detailed, step-by-step instructions really helps clarify what you're trying to accomplish. More importantly, it helps you figure out what you actually expect from each agent. But here's what I discovered pretty fast: tasks have this tendency to balloon into these complex monsters, especially when you're trying to be thorough.
Avoid Task Overload
Here's something that kept happening to me. When tasks had more than, say, four or five steps, even with those massive context windows we have now, my LLM-based agents would just start dropping things. Essential instructions would just vanish. It's like they'd get halfway through and completely forget what they were supposed to be doing. When this starts happening, it's basically your system screaming that your tasks are trying to do way too much.
Breaking Down Tasks
What works for me? Keep tasks concise. Really concise. I try to limit them to around 3 or 4 clear steps each. The moment I notice a task getting unwieldy, I split it up. For instance, I used to have this one task that would classify user intent and then immediately execute based on that classification. Total mess. Now I separate those processes completely. Everything becomes clearer, more accurate. And honestly, breaking down those large tasks has made such a difference in agent performance and how manageable the whole system is.
Example
# What not to do
god_task:
description: >
Analyze the user query, classify the intent, extract relevant entities,
query the knowledge base, generate a markdown response, ask for clarification
if needed, log the request for analytics, and refresh the cache if the user is
a premium member.
expected_output: >
A complete markdown response to the user query, with intent classified,
relevant entities extracted, cache refreshed (if needed), and analytics updated.
agent: TBD# What works
classify_intent:
description: >
Analyze the user's input and classify it into a predefined intent category
(e.g., information request, action request, greeting). If the intent is unclear,
ask a clarifying question before proceeding.
expected_output: >
A JSON object with the intent category and any follow-up question if clarification is needed.
agent: TBD
extract_entities:
description: >
Based on the classified intent, extract any relevant entities from the
user's input. These may include product names, locations and dates.
expected_output: >
A JSON object containing the extracted entities as key-value pairs.
agent: TBD
retrieve_and_respond:
description: >
Using the classified intent and extracted entities, search the appropriate
data sources and generate a markdown-formatted response that directly
answers the user query.
expected_output: >
A well-formatted markdown answer that is accurate and relevant to the user query.
agent: TBD
log_and_refresh:
description: >
If the user is a premium member, log the query metadata to the analytics system
and refresh the corresponding cache entries. This task is optional and should
run independently.
expected_output: >
A status report indicating whether analytics were logged and cache was refreshed.
agent: TBD
Use Pydantic Models to Control Inputs and Outputs
The Role of Structured Data in Multi-Agent Systems
Once you've got your tasks defined and your agents actually focused on what they're supposed to do, the next headache is keeping them talking to each other properly. This is where structured input and output becomes absolutely essential. Without clearly defined data formats, information just gets lost. Or worse, it gets misinterpreted. Or it becomes this ambiguous mess that the next agent can't make heads or tails of. Trust me, I learned this one the hard way.
Why Pydantic Makes a Difference
Using Pydantic models is like creating a shared contract between tasks. Actually, that's exactly what it is. For more on best practices for prompt engineering and reliable LLM outputs, including how to structure prompts and enforce output formats, check out our in-depth guide. These models basically spell out exactly what an agent expects to receive and what it's going to send back. This becomes especially crucial when you've got multiple agents passing information back and forth like a game of telephone, or when you're trying to integrate with external tools or APIs.
What Has Worked for Me
Here's what I do now: I define a Pydantic model for each task's output as early as possible. Like, before I even write the task description sometimes. It forces clarity for both me and the LLM. And it ensures that the flow between tasks doesn't turn into a game of broken telephone. If something needs to change in the structure later, you adjust it in one place. This approach? It's made debugging so much easier. The friction when chaining tasks together in complex workflows has basically disappeared.
Example
from pydantic import BaseModel, Field
from typing import Optional, Dict
class EntityExtractionOutput(BaseModel):
"""Extracted entities from the user's input."""
product: Optional[str] = Field(None, description="The name of the product")
location: Optional[str] = Field(None, description="Any location reference input")
date: Optional[str] = Field(None, description="Relevant date or time information")# Example YAML for assigning the model to a task
# (this would be in your crewai task YAML file)
extract_entities:
description: >
Based on the classified intent, extract any relevant entities from
the user's input. These may include product names, locations and dates.
expected_output: EntityExtractionOutput
agent: TBD# Example defining the task in your crew
extract_entities = Task(
config=tasks_config['extract_entities'],
output_pydantic=EntityExtractionOutput
)Keep Agents Focused
Avoid the "Do-It-All" Agent
When you're just starting with multi-agent systems, there's this really tempting trap. You try to make one super-agent that can handle everything. It seems efficient, right? But agents lose their effectiveness incredibly fast when they're juggling unrelated responsibilities. It's just like real teams, actually. Specialization matters. A lot. For a step-by-step tutorial on building a specialized LLM agent, including reasoning, actions, and automation, see our guide using the GPT-4 ReAct pattern.
Group Related Tasks
What I've found works best is giving each agent a really clear role. I limit them to 3 or 4 closely related tasks, max. Once you've broken your tasks down into those small, focused steps I mentioned earlier, take a step back. Look at what each task is actually doing. Group the similar ones together and assign them to a single agent. If a task feels weird or out of place, it probably belongs to a completely different agent.
Push Shared Logic Up to the Agent
Sometimes you've got certain behaviors or instructions that keep showing up across multiple tasks. Instead of copying and pasting that logic into each task (which I definitely did at first), I now put it in the agent definition itself. Let's say all of an agent's tasks need to maintain a specific tone or follow a particular reasoning pattern. I define that expectation once at the agent level. Keeps the task definitions cleaner. And more importantly, it reduces those annoying inconsistencies that pop up during execution.
# Handles: classify_intent
intent_classifier:
role: >
User Intent Classification Specialist
goal: >
Accurately identify the user's intent to guide downstream task execution
backstory: >
You're an expert in natural language understanding with a strong intuition
for interpreting human queries. Your precision in classifying intent ensures
that the rest of the system can act with clarity and purpose. You never assume—
if the user's intent is unclear, you ask the right follow-up question to
clarify it.
# Handles: extract_entities, retrieve_and_respond, log_and_refresh
retrieval_specialist:
role: >
Intelligent Retrieval and Response Generator
goal: >
Deliver precise and well-formatted responses based on user needs
backstory: >
You're a results-driven AI agent skilled in using structured inputs like
classified intent and extracted metadata to retrieve accurate information.
You're also a master of markdown formatting, ensuring your answers are always
clean, informative, and ready to be presented to the user. You always maintain a
helpful, professional tone, and your reasoning is structured and explicit—start
from known facts, explain your steps clearly, and avoid skipping
logical connections.
Since many of your tasks require consistent formatting and structured thinking,
you've been designed to always follow a markdown-friendly output style,
using bullet points, headings, and code blocks where appropriate. You prioritize
clarity and readability across all responses, avoiding repetition and verbosity.
Optimize Execution Speed and Flexibility
The Speed Challenge with Multi-Agent Systems
As you start scaling up your agents and tasks, speed becomes a real problem. If every task sits there waiting for the previous one to finish, even when they have absolutely nothing to do with each other, you've created a massive bottleneck. This can slow your system down to a crawl, especially when you realize half your tasks could be running at the same time without any issues.
Using Asynchronous and Conditional Tasks
Here's what's been working for me: I use async_execution=True for tasks that can run in parallel. If your multi-agent system uses retrieval-augmented generation, you might find our guide on RAG techniques to boost answer accuracy really useful for optimizing both speed and quality. This lets the system actually take advantage of concurrency without breaking the task logic. Tasks that do independent lookups or data enrichment? Those can almost always run simultaneously.
I also lean heavily on context-based chaining and conditional tasks to control the flow. Some tasks only need to run if certain conditions are met. Why waste time on them otherwise? Conditional logic makes it easy to skip the unnecessary stuff. One thing to remember though: always, always end your sequence with a non-async task. You need everything to sync back together before you produce that final output or move to the next phase.
Keeping Things Flexible and Fast
This approach gives you tons of flexibility and way better performance. You're not stuck in some rigid, step-by-step structure that can't adapt. You can design flows that actually respond to what's happening. And they still run fast. In practice, this has let me scale workflows without watching response times go through the roof or losing control of what's happening.
Example
classify_intent:
description: >
Analyze the user's input and classify it into a predefined intent category
(e.g., information request, action request, greeting). If the intent is unclear,
ask a clarifying question before proceeding.
expected_output: IntentClassificationOutput
agent: intent_classifier
extract_entities:
description: >
Based on the classified intent, extract any relevant entities from the user's
input. These may include product names, locations and dates.
expected_output: EntityExtractionOutput
agent: retrieval_specialist
context: [classify_intent]
retrieve_and_respond:
description: >
Using the classified intent and extracted entities, search the appropriate data
sources and generate a markdown-formatted response that directly answers
the user query.
expected_output: MarkdownResponseOutput
agent: retrieval_specialist
context: [extract_entities]
# Async Task
log_and_refresh:
description: >
If the user is a premium member, log the query metadata to the analytics system
and refresh the corresponding cache entries. This task is optional and should
run independently.
expected_output: CacheLoggingStatus
agent: retrieval_specialist
async_execution: true
context: [intent_classifier]
Conditional Task
from crewai.tasks.conditional_task import ConditionalTask
# Output of the classify_intent task
class IntentClassificationOutput(BaseModel):
intent: str
is_premium_user: bool
# Define the condition function for the conditional task
def is_premium_user(output: TaskOutput) -> bool:
return output.pydantic.is_premium_user
# log_and_refresh conditional task
log_and_refresh = ConditionalTask(
config=tasks_config['log_and_refresh '],
output_pydantic=CacheLoggingStatus,
condition=is_premium_user
)Conclusion
Multi-agent systems are incredibly powerful. But here's the thing, they're only powerful if you structure them right. The more complex your system gets, the more those small mistakes start to pile up. Overloading agents, writing unclear tasks, poor communication between components. It all compounds. For additional guidance on how to structure system and user prompts to avoid conflicts and ensure clarity, see our analysis of prompt hierarchies.
What's worked best for me is keeping things simple, modular, and easy to reason about. Three to four steps per task. Three to four tasks per agent. Structured I/O between them. And a framework that lets me adapt quickly when things inevitably don't go as planned. Because they won't.
Frameworks like CrewAI and LangChain? They give you a really solid foundation to build on. But the design decisions, how you actually write your tasks, how you assign your agents, how you handle execution. That's where you either succeed or fail.
If you're just starting out, expect to iterate. A lot. Expect to refactor. But also know that once the pieces start falling into place, multi-agent workflows become incredibly powerful. They're flexible, fast to maintain, and honestly kind of fun to work with once you get the hang of it. Hopefully, the patterns I've shared here help you get there without quite as many late nights as I had.
