Key resources
Below sections contain the key take aways from each lesson.
Overview of Multi AI-Agent Systems
Use cases to be explored in this course
- Automated Project Planning
- Project Monitoring
- Lead Qualification and Scoring
- Analysis and Report on Support Data
- Custom Content Creation at Scale
Most of the use cases usually fo ground some form of research and analysis, summarization and reporting
Main building blocks for building multi AI-agent systems

We can orchestrate agents in different ways

We can build Agents and Crews even with a YAML file

Automated Project: Planning, Estimation and Allocation (L2)
Unlike the previous course, in this course, the Agent
(backstory
, role
, and goal
) and Task
(description
, and expected_output
) are provided as .yaml
files.
The Agentic Crew
to perform Project Planning, Estimation and Resource Allocation was assembled using 3 Agents
, and 3 Tasks
.
Agents
project_planning_agent
with role asThe Ultimate Project Planner
estimation_agent
with role asExpert Estimation Analyst
resource_allocation_agent
with role asResource Allocation Strategist
- Class
TaskEstimate
,MileStone
, andProjectPlan
usingpydantic BaseModel
Tasks
task_breakdown
time_resource_estimation
resource_allocation
withoutput_pydantic=ProjectPlan
Crew
Since the output of
resource_allocation
tasks is configured to be pydantic, it can easily be converted tojson
and as pandasDataFrame
subsequently for downstream processing.
Internal and External Agents
Internal tools could be
- RAG search
- SQL query
- Trigger side effect
External tools could be
- Search the internet
- Check calendar
- Reply Email

Building Project Progress Report (L4)
The Agentic Crew
to create a project progress report was assembled using 2 Agents
, and 3 Tasks
, and 2 custom built Tools
.
Tools
BoardDataFetcherTool
andCardDataFetcherTool
using crewaiBaseTool
Agents
data_collection_agent
withtools=[BoardDataFetcherTool(), CardDataFetcherTool()]
analysis_agent
Tasks
data_collection
data_analysis
report_generation
Crew
The
analysis_agent
used for two different tasks (data_analysis
,report_generation
) in this project

Complex crew Setups
Agentic Sales Pipeline (L6)


The Agentic Crew
to create a Sales Pipeline was assembled using 2 Crews
Crew
1: lead_scoring_crew
with 3 Agents
, and 3 Tasks
, and 2 Tools
.
Agents
lead_data_agent
cultural_fit_agent
scoring_validation_agent
Tasks
lead_data_task
cultural_fit_task
scoring_validation_task
withcontext=[lead_data_task, cultural_fit_task] and
output_pydantic=LeadScoringResult`
Crew
2: email_writing_crew
with 2 Agents
, and 2 Tasks
Agents
email_content_specialist
engagement_strategist
Tasks
email_drafting
engagement_optimization
SalesPipeline
- Built using crewai
’s Flow
feature
- Research a potential lead
- Score them given the person and the company details
- If it’s a qualified lead, then draft a proper initial email
Using
crewai
Flow
, we can create end-to-end pipelines by leveragingFlow
features likestart
,listen
,and_
,or_
,router
as well asstate
.CrewAI operates on a higher level of abstraction, centered around the concept of a “crew” of autonomous AI agents. The “flow” in CrewAI dictates the sequence in which these agents, each with a specific role, set of tools, and a defined task, are activated. Key aspects include
- Role-based task delegation
- Sequential and Hierarchical workflows
- Implicit state management
This approach simplifies the process of creating multi-agent systems by focusing on the “who” and “what” rather than the “how” of the workflow. It excels in scenarios where the division of labor is clear and the process can be broken down into a series of distinct, role-based steps.


Performance Optimization
Speed and Quality are two main variables. However, Consistency is paramount.

How can we test agents performance?
By comparing the
Task
description with expected outcome usingcrewai test
.![]()

How can we improve the
Task
,Agent
and theCrew
without having to spend long time?Leverage
crewai train
feature.
