Imagine that instead of writing every line of code yourself, you managed a group of skilled AI bots to build an entire app. Someone plans the project, someone else writes the code, and someone else tests it. They all talk to each other like a real code team. This is the power of the Agentic AI Multi-Agent Pattern, a new and revolutionary way to use AI bots to work together to get things done. We’ll talk about how to use tools like AutoGen, CrewAI, and MetaGPT to build and launch these smart systems in this blog. Let’s look into how AI will change the way teams work in the future.
What is Agentic AI Multi-Agent Pattern?
Example: Think of a group of AI bots managing a software project one plans, another codes, another tests. Together, they build software autonomously.
What is a Multi-Agent System?
A Multi-Agent System (MAS) is a group of separate computer programs that can work together or against each other to complete certain tasks. Each worker works on their own, but they can talk to each other. Distributed decision-making in MAS is a great way to solve hard problems.
Key features:
- Decentralized decision-making
- Communication and collaboration
- Autonomous behavior
Real-world analogy: A team of doctors collaborating on a patient case, each bringing a unique specialty.
The Architecture of Agentic AI Multi-Agent Pattern
The architecture typically includes:
- Agents: Specialized LLMs or AI programs.
- Tools: Functions that agents can use.
- Memory: Keeps track of conversations and context.
- Environment: The problem space or domain.
Agents talk, access tools, update memory, and refine the strategy.
Components Explained:
- Agents: Think of each as an expert (e.g., coder, reviewer).
- Tools: APIs, scripts, or calculators they can use.
- Memory: Shared knowledge that guides actions.
- Coordinator: Sometimes used to manage the process.
Key Characteristics:
- Autonomy
- Collaboration
- Role-based design
- Goal-driven actions
- Asynchronous task execution
These make the system scalable and adaptable.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen by Microsoft is a framework that simplifies the creation of multi-agent conversations using LLMs.
Conversation Programming
Conversation programming involves designing agent dialogues as programmable sequences enabling structured problem-solving through conversations.
How to Use AutoGen to Program a Multi-agent Conversation?
Here are the steps you can refer to:
1. AutoGen Agents
Create agents with specific roles:
from autogen import AssistantAgent coder = AssistantAgent(name="Coder") tester = AssistantAgent(name="Tester")
2. Developer Code
Define how agents interact:
from autogen import GroupChat, GroupChatManager chat = GroupChat(agents=[coder, tester], messages=[]) manager = GroupChatManager(groupchat=chat)
3. Program Execution
Start the interaction:
manager.run("Build a Python script to sort a list of numbers")
Hands-on Agentic AI Multi-Agent Pattern
Define an AutoGen agent
coder = AssistantAgent(name="Coder", llm_config={"model": "gpt-4"})
Setting up the Conversation
chat = GroupChat(agents=[coder, reviewer], messages=[]) manager = GroupChatManager(groupchat=chat)
For Chat Termination
AutoGen supports logic to detect when tasks are complete.
Output Analysis
Review outputs to validate if the collaborative agents met the goal.
Key Points
- Each agent has a unique purpose.
- They communicate using natural language.
- Collaboration increases reliability and efficiency.
Agentic AI Multi-Agent Pattern from Scratch
What is a Crew?
A Crew is a coordinated team of AI agents, each assigned a specific role, working together through a structured pipeline to complete a task. It mirrors a real-world project team where collaboration and specialization lead to efficient problem-solving.
1. Core Concept of Crew
Each agent in a Crew has a defined role like Planner, Coder, or QA focused on a particular phase of a task. This role-based setup ensures clarity, modularity, and streamlined collaboration from start to finish.
2. Key Attributes and Methods in Crew Class
agents: A list defining who is on the team and their roles.run(): A method that launches the coordinated workflow, triggering each agent’s contribution step-by-step toward task completion.
3. How It Works?
The Planner agent analyzes the task and breaks it down into actionable steps. The Coder agent implements the solution. Finally, the QA agent tests the output for correctness, ensuring a full development cycle within an autonomous system.
What is an Agent?
For example:
CoderAgent – writes code based on specifications.
PlannerAgent – breaks down a project into tasks.
ReviewerAgent – checks code quality and suggests improvements.
What is a Tool?
A Tool is an external function or resource that an agent can call to perform actions it cannot handle on its own. Tools extend an agent’s capabilities by enabling access to APIs, databases, code execution, or external services.
For Example:
Web Search Tool – lets an agent fetch real-time information from the internet.
Math Solver Tool – helps solve complex equations or perform calculations.
Python Code Executor – allows an agent to run and test Python code dynamically.
File Reader Tool – enables reading and analyzing data from local files.
How It Works?
In a multi-agent system, each agent operates like a specialized team member, contributing to the overall goal through communication, tool usage, and collaboration. Here’s how the process typically unfolds:
1. Receive Tasks: Each agent is assigned a specific task based on its role in the system. For example, a PlannerAgent may be tasked with outlining the project plan, while a CoderAgent is given the job of writing the actual code. Tasks are usually provided in natural language or structured prompts, and agents interpret them using their language models.
2. Use Tools: To enhance their capabilities, agents can access tools external functions that help them perform actions beyond basic reasoning. For instance, if a MathAgent encounters a complex equation, it can call a Math Solver Tool to compute the result. Similarly, a DataAgent might use a File Reader Tool to extract information from spreadsheets or documents.
3. Collaborate: Agents communicate with one another using natural language, sharing findings, asking questions, or requesting support. For example, once the CoderAgent completes a feature, it might send the code to a ReviewerAgent, who checks for bugs or improvements. This back-and-forth enables higher accuracy and refined results.
4. Improve Results Iteratively: Multi-agent systems are designed to refine their output through multiple rounds of feedback and improvement. After each step, agents evaluate outcomes, make adjustments, and continue until the final goal is achieved. This loop mimics how human teams review and iterate on their work, leading to smarter and more reliable solutions.
Implementation
from crewai import Agent, Crew
agent1 = Agent(role="Planner")
agent2 = Agent(role="Developer")
crew = Crew(agents=[agent1, agent2])
crew.run("Create a to-do app")
MetaGPT Agents: Meta Programming for Multi-Agents in Standard Operating Procedures
MetaGPT is a powerful framework designed to bring structure and discipline to multi-agent systems by transforming Standard Operating Procedures (SOPs) into executable workflows. It organizes agents around predefined roles, tasks, and communication rules, making AI teams function more like real human teams.
Structured Methodologies and SOP-Driven Workflows
MetaGPT uses SOPs to guide agent behavior. Instead of free-form conversations, agents follow structured steps and decision trees, which leads to more predictable, repeatable outcomes. This is particularly useful in environments like software development or research where consistency is key.
Example: If the SOP says, “First plan the app, then code, then review,” each agent follows that sequence precisely, reducing ambiguity.
Role Differentiation and Task Management
Each agent in MetaGPT is assigned a distinct role—similar to a real-world team. These roles define what the agent is responsible for and how it should interact with others.
Examples of roles:
Project Manager (PMAgent): Plans tasks and coordinates agents.
Engineer (CoderAgent): Implements features or solutions.
Reviewer (QAAgent): Checks and validates output for quality and correctness.
This role clarity ensures smooth task handoffs and accountability.
Communication Protocol and Feedback System
Agents in MetaGPT communicate through a structured message-passing system. This includes status updates, task delegation, and feedback loops. The system allows agents to request clarifications, suggest changes, and iteratively improve outputs—just like team members in a Slack or email thread.
Example: A QAAgent can send feedback to the CoderAgent about a bug, who then revises the code and resubmits.
Application of Human-Centric Practices
MetaGPT is inspired by how human teams operate in real-world scenarios. It applies principles like task ownership, iterative feedback, peer review, and structured planning. These human-centric workflows make the system easier to understand, manage, and align with existing organizational practices.
Software Development Process in MetaGPT
MetaGPT simulates a complete development lifecycle using agents:
Requirement Gathering: PMAgent defines what needs to be built.
Planning: Tasks are broken down and assigned.
Coding: Engineer agents implement the solution.
Testing: QA agents validate the work.
Documentation: Specialized agents create supporting materials.
By automating this pipeline, MetaGPT enables fast, structured, and high-quality software creation through collaborative AI agents.
Why Multi-Agent Systems Require MetaGPT?
In complex multi-agent setups, managing coordination, maintaining consistency, and scaling the workflow becomes challenging. MetaGPT addresses these issues by structuring agent interactions using SOPs (Standard Operating Procedures), predefined roles, and clear communication protocols ensuring the system works like an organized, human-like team.
MetaGPT Addresses these Challenges through Several Key Innovations
- SOP as Code
- Structured agent classes
- Integrated memory and feedback
What are the Benefits of Agentic AI Multi-Agent Pattern?
- Better collaboration
- Specialization
- Scalable complexity handling
- Reusability of agents and tools
Conclusion
Agentic AI Multi-Agent Patterns offer a powerful approach to building intelligent, modular, and collaborative systems. Tools like AutoGen, CrewAI, and MetaGPT simplify implementation, making it easier to bring agent-based AI into real-world applications.
Agentic AI leverages goal-oriented agents capable of autonomous decision-making and action-taking to complete complex tasks. An Agentic AI certification equips learners with the skills to build intelligent systems that plan, reason, and interact across workflows, empowering innovation in fields like robotics, automation, customer support, and enterprise productivity.
FAQ
Q1: Can I use this with GPT-4 or Claude?
Yes, frameworks like AutoGen support major LLMs.
Q2: Are these agents really autonomous?
They follow instructions but can make decisions within a scope.
Q3: Can I use this in production?
Yes, with safety checks and proper supervision.
Q4: Do I need to write code for every agent?
Frameworks help reduce boilerplate, but you’ll need to configure roles and tools.