---
title: "Multi-Agent Systems"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Multi-Agent Systems

This is a comprehensive tutorial on multi-agent systems as applied to modern AI agent architectures. This tutorial will explore the concepts behind multi-agent approaches, break down the architecture, and illustrate how multiple specialized agents can collaborate to solve complex problems. We’ll also provide practical examples and strategies for orchestrating these agents effectively.

## Understand Multi-Agent Systems using a Example/Diagram

![multi agents](multi.png)

This diagram illustrates a **multi-agent architecture** designed for collaboratively developing Python code. The system uses several specialized agents, each with distinct responsibilities, working together to fulfill a user’s request. Let’s break down each component and the workflow step-by-step:

### Key Entities in the Diagram

1. **User:**  
   The user provides a prompt or a feature request. This could be something like, “Implement a Python function that calculates the factorial of a number.”

2. **Controller Agent (Center):**  
   - The Controller Agent is the central coordinator of the entire workflow.
   - It can **execute Python code on behalf of the user**, meaning it can run code snippets, scripts, or test cases as needed.
   - It receives the user’s prompt and manages interactions between the different worker agents.

3. **Coder Worker Agent (Left):**  
   - The Coder Worker Agent is specialized in generating or improving code.
   - When the user or the Controller Agent requests a new feature or change, the Controller Agent sends a “Feature Request” to the Coder Worker Agent.
   - The Coder Worker Agent, likely powered by an LLM (Large Language Model) fine-tuned for coding tasks, returns code snippets or entire modules that attempt to implement the requested feature.

4. **Tester Worker Agent (Right):**  
   - The Tester Worker Agent is specialized in writing and running unit tests.
   - After code is produced, the Controller Agent obtains unit tests from the Tester Worker Agent.
   - The Tester Worker Agent likely uses an LLM with testing knowledge to produce relevant test cases and validation routines that confirm if the code behaves correctly.

5. **Python Execution Environment (Within the Controller Agent’s Box):**  
   - Inside the Controller Agent’s environment is a Python runtime. 
   - The Controller Agent can run the code provided by the Coder Worker Agent and apply the unit tests from the Tester Worker Agent.
   - This real-time code execution capability is what makes the architecture powerful: it can iteratively refine the code until tests pass.

### Step-by-Step Workflow

1. **User Prompt:**  
   The user provides a task, for example: “Create a Python function that calculates the factorial of a given number.”

2. **Feature Request from Controller Agent to Coder Worker Agent:**  
   The Controller Agent takes this request and sends it to the Coder Worker Agent, asking for code that implements the requested functionality.

3. **Coder Worker Agent Returns Code:**  
   The Coder Worker Agent responds with a Python code snippet (e.g., a `factorial.py` module with a function `factorial(n)`).

4. **Controller Agent Executes Code:**  
   The Controller Agent runs the code in the Python environment. At this point, the code might be syntactically correct, but we don’t yet know if it meets the functionality requirements fully.

5. **Controller Agent Requests Tests from Tester Worker Agent:**  
   The Controller Agent asks the Tester Worker Agent for unit tests. It might say, “Please provide tests to verify that `factorial(n)` returns the correct values for various inputs.”

6. **Tester Worker Agent Returns Unit Tests:**  
   The Tester Worker Agent provides a set of unit tests. For example, tests might check:
   - `factorial(0)` returns `1`
   - `factorial(5)` returns `120`
   - `factorial(-1)` raises an error or handles invalid input appropriately.

7. **Controller Agent Runs Tests:**  
   The Controller Agent executes these tests against the code. If some tests fail, the Controller Agent knows the implementation needs refinement.

8. **Iterative Refinement Loop:**  
   - If tests fail, the Controller Agent goes back to the Coder Worker Agent, requesting improved code based on the test failures and possibly providing hints or error messages.
   - The Coder Worker Agent updates the code and returns a new version.
   - The Tester Worker Agent might also provide updated tests if new features or corrections are requested.
   - The Controller Agent keeps iterating this loop—running code, applying tests, going back to the Coder Worker Agent—until all tests pass successfully.

9. **Successful Completion:**  
   Once the code passes all the tests provided by the Tester Worker Agent, the Controller Agent returns the final, validated solution to the user.

### Key Takeaways

- **Multi-Agent Collaboration:** The diagram shows how different agents with distinct specializations can collaborate: one focuses on code generation, another on testing, while a central controller coordinates everything.
- **Iterative Improvement:** The system improves code quality in a loop rather than producing a single static solution. It uses continuous feedback from tests to guide refinements.
- **Automation of Software Development Tasks:** By offloading coding and testing tasks to specialized agents, the user can focus on high-level requirements rather than managing the details of testing and debugging.

In essence, the diagram depicts a **multi-agent coding system** where:
- The **User** provides the goal.
- The **Controller Agent** manages the flow.
- The **Coder Worker Agent** writes or refines the code.
- The **Tester Worker Agent** validates functionality with tests.
  
This results in a structured, automated, and iterative process for producing reliable Python code that meets the user’s specifications.

---

## Introduction to Multi-Agent Systems

**What are Multi-Agent Systems?**  
A multi-agent system (MAS) is a collection of autonomous agents—each equipped with its own specialized capabilities, knowledge, and tools—working together to solve a larger problem. In the context of Large Language Models (LLMs) and AI-driven development, “agents” are typically AI services or components that can perform tasks such as retrieving data, reasoning, planning, code generation, or interacting with external APIs.

**Why Use Multiple Agents Instead of One?**  
While a single agent can handle straightforward tasks by prompting a large language model directly, many real-world challenges are too complex to solve with a single, monolithic agent. A multi-agent architecture allows you to:

1. **Divide and Conquer:** Break down complex problems into smaller, more manageable tasks.  
2. **Specialization:** Create agent “profiles” or “personas” with domain-specific knowledge, tools, and behaviors.  
3. **Parallelization:** Allow multiple agents to work simultaneously or asynchronously, improving efficiency and scalability.  
4. **Modularity and Reusability:** Individual agents can be reused across projects or scaled independently.

---

## Key Concepts

1. **Agent Profiles (Personas):**  
   Each agent in a multi-agent system has a defined role. For example:
   - *Research Agent:* Specialized in searching academic databases and extracting summarized knowledge.
   - *Code Generation Agent:* Skilled at writing and optimizing code, using coding tools or IDE integrations.
   - *Data Analytics Agent:* Capable of running queries against databases, generating reports, or performing statistical analyses.
   - *User Interface Agent:* Interacts with end-users, gathers requirements, and presents final outputs in a friendly manner.

   By assigning distinct roles, you ensure each agent is equipped with the right “toolbelt” and context to excel in its domain.

2. **Communication and Coordination:**  
   Multi-agent systems rely on effective communication:
   - Agents must share information, intermediate results, and requests for assistance.
   - An “Orchestrator” agent or a controller process often coordinates these interactions, ensuring that data flows smoothly and that agents stay aligned on the overall goal.

3. **Decision-Making and Task Allocation:**  
   The orchestrator decides:
   - Which agent should handle a given sub-task.
   - In what order tasks should be performed.
   - How to handle failures or unexpected results (e.g., re-assigning a task if one agent gets stuck).

4. **Tools and Knowledge Integration:**  
   Each agent can have its own specialized tools and data sources:
   - A Research Agent might have access to document retrieval APIs.
   - A Code Generation Agent might have access to a coding environment, testing frameworks, and API references.
   - By integrating tools at the agent level, you maintain a high degree of modularity and clarity.

---

## Architectural Patterns for Multi-Agent Systems

1. **Pipeline Model:**  
   Tasks move sequentially from one agent to the next. For example:
   - User’s request → Requirements Agent → Research Agent → Data Analytics Agent → Report Generation Agent.
   
   This model is simple but can become a bottleneck if one agent must wait for another to finish.

2. **Hierarchical/Orchestrator Model:**  
   An Orchestrator agent directs other agents:
   - User sends a request to the Orchestrator.
   - Orchestrator breaks the request into subtasks and dispatches them to specialized agents.
   - Results are consolidated by the Orchestrator, which then produces the final answer.

   This model is flexible, making it easier to handle complex tasks and dynamic re-planning.

3. **Blackboard Model:**  
   Inspired by AI problem-solving architectures, a shared “blackboard” holds the current state of the problem. Agents read from and write to the blackboard, contributing partial solutions:
   - Research Agent posts data findings.
   - Analytics Agent picks up these findings, performs calculations, and posts a summary.
   - A Presentation Agent formats the final solution from the aggregated data.

   This model is highly decoupled; agents don’t directly communicate with each other but instead interact through a common shared memory.

---

## Example Scenarios

### Example 1: Market Analysis Report

**Goal:** Produce a comprehensive market analysis report for a new product launch.

**Agents:**
- *User Interface Agent:* Interacts with the user (the product team) to understand requirements.
- *Data Retrieval Agent:* Pulls relevant market data (competitor pricing, market trends) from online APIs.
- *Analytics Agent:* Analyzes the data, identifying trends, performing statistical tests.
- *Research Agent:* Queries LLM for industry reports, synthesizes textual summaries of best practices.
- *Report Generation Agent:* Formats the final output into a coherent, well-structured PDF or presentation.

**Workflow:**
1. The User Interface Agent asks the product team for their target market, competition, and data preferences.
2. The Orchestrator instructs the Data Retrieval Agent to gather competitor pricing and historical sales data.
3. Analytics Agent processes these raw figures, computing market size estimates and price elasticity.
4. Research Agent consults the LLM to produce textual insight on emerging industry trends.
5. Finally, the Report Generation Agent takes the analytics, text insights, and formatting guidelines to produce the final report. The Orchestrator returns this to the User Interface Agent, who presents it to the product team.

### Example 2: Customer Support System

**Goal:** Handle a complex customer support query that involves technical troubleshooting and account management.

**Agents:**
- *Customer Service Agent (Frontend):* Interacts with the user, collecting their issue and account details.
- *Knowledge Base Agent:* Accesses a knowledge base tool or FAQ repository to find troubleshooting steps.
- *Diagnostics Agent:* Runs system checks via an API (e.g., checking account status, device logs).
- *Resolution Agent:* Suggests solutions or performs account updates based on inputs from the other agents.
- *Customer Communication Agent:* Drafts a clear, empathetic response to the customer, walking them through the solution.

**Workflow:**
1. Customer Service Agent receives the user’s complaint: “My device isn’t syncing with my account.”
2. Orchestrator asks Knowledge Base Agent to find known issues related to device syncing.
3. Diagnostics Agent checks the user’s account and device logs.
4. Resolution Agent decides on the best fix—resetting the sync token, or providing updated firmware instructions.
5. Customer Communication Agent drafts a final message, merging the recommended fix and steps into a friendly, human-readable solution. The Orchestrator sends this to the Customer Service Agent to relay to the user.

---

## Implementation Tips

1. **Defining Clear APIs for Agent Interaction:**  
   Standardize how agents talk to each other. For instance, define input and output formats (JSON objects, structured text prompts) and ensure each agent can parse and respond accordingly.

2. **Shared Context Management:**  
   Give the Orchestrator or a central context manager the responsibility of maintaining a “global state” or “project file.” This might include:
   - User’s initial prompt or request.
   - Partial results or temporary variables.
   - Metadata about each agent’s progress.

3. **Using Frameworks and Libraries:**
   - **CrewAI or LangGraph:** These frameworks offer building blocks for agent reasoning, tool use, and chaining tasks.  
   - **OpenAI Functions / Tool Integration:** If using LLMs, you can integrate tools directly as “functions,” allowing agents to call APIs through a structured interface.
   - **Message Passing and Event Queues:** For asynchronous systems, consider message queues (e.g., Kafka) or asynchronous tasks to let agents work concurrently.

4. **Iterative Development and Testing:**
   - Start with two agents and a simple Orchestrator. Validate that the workflow functions correctly.
   - Gradually add more agents and complexity, ensuring that each agent’s role and responsibilities are clearly defined.
   - Test failure modes: what if one agent returns incomplete data? Build in error-handling and fallback logic.

---

## Best Practices

1. **Keep Agents Focused:**  
   Each agent should have a narrow, well-defined purpose. Avoid “super agents” that try to do everything.

2. **Make Inter-Agent Communication Transparent:**  
   Log or record interactions so you can debug and understand why certain decisions were made.

3. **Leverage Domain Expertise:**
   If an agent is responsible for financial analysis, incorporate domain-specific tools and datasets so it can produce high-quality results. Conversely, a design-focused agent might integrate with image-generation models or UI mockup tools.

4. **Performance Considerations:**
   Distribute tasks so not all agents wait on a single bottleneck. Consider caching results—e.g., if the Research Agent already looked up a particular fact, store that so it doesn’t have to repeat the operation.

---

## The Future of Multi-Agent Systems

As LLMs grow more capable and tool integration more seamless, multi-agent systems will become central to building adaptive, dynamic AI solutions. Agents will not only collaborate but also negotiate, learn from each other’s outputs, and dynamically reconfigure themselves in response to new tasks.

We may see:
- **Adaptive Persona Management:** Agents that can shift roles depending on context.
- **Marketplaces of Agents:** Systems where different specialized agents compete or bid to solve tasks most efficiently.
- **Self-Improving Ecosystems:** Agents that monitor system performance and automatically refine their strategies or retrain underlying models to improve future outcomes.

---

## Conclusion

Multi-agent systems extend the capabilities of single-agent solutions, enabling you to tackle more complex, data-rich, and dynamic problems. By dividing tasks among specialized agent profiles, coordinating their efforts through an Orchestrator, and leveraging tools and shared resources, you can build robust, scalable, and intelligent workflows that surpass what any single agent can do alone.

This approach unlocks a whole new dimension of AI-driven problem solving—one where each part of your system can become an expert in its domain, and together, they produce outcomes greater than the sum of their parts.


## Multi-agent systems can work autonomously but may also function guided entirely by  human feedback

Multi-agent systems (MAS) are collections of autonomous entities (agents) that can work collaboratively or competitively within an environment. These agents typically have their own sets of goals, capabilities, and decision-making processes. While the hallmark of multi-agent systems is the autonomy of the agents—meaning they can operate without continuous external commands—this autonomy can exist on a spectrum, from fully independent operation to tight human-in-the-loop control.

**Autonomous Operation:**  
When MAS function autonomously, each agent internally decides how to act based on predefined rules, learned behaviors, or algorithmic strategies. Here’s what this entails:

1. **Distributed Decision-Making:** Instead of relying on a central authority, each agent can independently sense the environment, communicate with other agents (if needed), and choose actions that advance its goals (e.g., optimizing a route, balancing load in a distributed system, or cooperating to solve a complex puzzle).

2. **Adaptation and Learning:** In more advanced autonomous MAS, agents can use machine learning to improve their decision-making over time. They might discover new strategies by trial and error (reinforcement learning), adjust their behaviors based on the performance of past actions, or adapt to changing environmental conditions.

3. **Scalability and Robustness:** Because autonomous agents don’t need constant human input, such systems can scale to very large numbers of agents without overburdening human operators. Additionally, if one agent fails, others can continue operating, providing robustness and resilience.

**Human-Guided or Human-in-the-Loop Operation:**  
On the other end of the spectrum, MAS can be designed so that human users guide decisions to varying degrees. This approach can range from subtle input to full human oversight:

1. **Periodic Human Feedback:** Agents may propose actions or solutions, but pause for confirmation or guidance before executing them. For example, in a supply chain MAS, agents might suggest optimal shipping routes, but a human logistics manager must approve the plan before finalizing it.

2. **Interactive Training and Fine-Tuning:** Humans can shape agent behaviors by offering corrective feedback. For instance, if an agent in a multi-agent learning environment chooses a suboptimal or undesired action, a human can provide negative feedback. Over time, the agent refines its decision-making based on this human-in-the-loop training.

3. **Policy and Ethical Oversight:** In scenarios where the stakes are high—autonomous vehicles coordinating on roads, or surveillance agents monitoring critical infrastructure—humans might retain ultimate authority. The MAS runs autonomously under normal conditions but must seek human approval for certain sensitive actions, ensuring compliance with ethical guidelines, safety standards, and regulatory policies.

4. **Iterative Co-Creation:** Humans and agents can co-design solutions. Agents generate ideas or partial solutions that humans then refine. This cycle repeats, with human feedback continuously shaping agent behavior. For example, a team of engineering agents might suggest new component designs for a machine, while a human expert chooses the most promising concept and directs further exploration.

**Finding the Right Balance:**  
The decision about how autonomous or human-guided a MAS should be depends on the application and associated risks:

- **High-Risk, High-Stakes Domains (Medicine, Aviation, Defense):** MAS often retain strong human oversight, ensuring that critical decisions aren’t made purely by autonomous reasoning.
- **Routine, Low-Stakes Tasks (Automated Warehouses, Robot Vacuum Cleaners):** MAS can operate largely autonomously, intervening only if unexpected conditions arise.
- **Hybrid Settings (Customer Service Chatbots, Content Moderation):** Agents work mostly on their own but occasionally escalate tricky or ambiguous cases to human operators who provide the final judgment.

**In Summary:**  
Multi-agent systems provide flexibility: they can be built to function with full autonomy, continuously refining their strategies based on data and interactions, or they can be tightly integrated with human feedback and oversight. This adaptability makes MAS suitable for a wide variety of applications, from routine automation tasks that free humans from repetitive labor, to complex scenarios where human judgment and intuition remain essential.
