Multi agent AI systems: Applications, components and examples

Table of Contents

• Understanding multi agent AI systems
• Exploring common applications of multi agent AI systems
• Breaking down the core building blocks of multi agent AI systems
• Various frameworks of multi agent AI systems 
• Introducing CrewAI: One of the platforms for managing and leveraging multi agent AI systems
• An example of using CrewAI’s multi agent AI systems for creating an automated project  
• Partner with High Peak for multi agent AI systems development

AI agents have revolutionized technology by analyzing data, making decisions, and performing tasks, thus enhancing efficiency across sectors. However, as we encounter problems that are too complex for single agents, necessitating seamless integration across multiple platforms, the need for multi agent AI systems becomes evident. 

Supporting this shift, a McKinsey report highlights that 90% of companies have seen significant workflow improvements after implementing multi agent AI solutions. Now,

“What if a task is too complex for a single agent to handle? Is one AI agent sufficient for every detailed task?”

No, the answer stays the same. AI agents have significantly boosted efficiency across sectors. However, a single agent cannot tackle all challenges. Transitioning to multi-agent AI marks a collective strategy. It harnesses various AI agents’ strengths. Also, this approach addresses challenges insurmountable by individual agents. This blog intends to provide readers with an understanding of multi agent AI systems and their components, showcasing the power of collaborative AI in solving complex problems.

Understanding multi agent AI systems

Multi agent AI involves the design and implementation of systems where multiple intelligent agents interact. These agents are designed to work towards individual or shared goals. Unlike traditional AI systems that focus on singular tasks or objectives, multi agent AI harnesses the complexity and efficiency of multiple agents. These agents collaborate or compete to achieve optimal results.

How multi agent AI functions

At the core of multi agent AI is the principle of distributed problem-solving. Here, tasks are divided and conquered by specialized agents. Each agent has a unique role. This division allows for more efficient resolution of complex problems. Agents in these systems can communicate, negotiate, and collaborate. They can also work independently, depending on the goal at hand. This flexibility is key to the success of multi agent AI systems.

Key components of multi-agent systems

To ensure these systems operate smoothly in real-world applications, several features are critical.

• Data caching: Data caching reduces unnecessary tool usage. It prevents agents from performing the same actions repeatedly. This efficiency is crucial. It saves resources and time.
• Shared memory: A shared memory feature allows agents to remember and learn from past actions. This ability is vital. When faced with familiar tasks or data, agents can quickly recall previous solutions. This shared memory promotes efficiency and learning within the system.
• Continuous learning: The integration of training data is essential for the evolution of these systems. Agents use this data to learn and improve over time. Continuous learning enables the system to adapt. It can tackle new challenges more effectively as it encounters them.
• Output integrity: To maintain the reliability of these systems, guardrails are necessary. They prevent agents from generating incorrect or misleading information. Ensuring the integrity of outputs is crucial for user trust and system reliability.

Orchestration in multi-agent systems

Managing how agents work together is critical for multi agent AI. This management ensures that the system functions seamlessly. Whether agents complete tasks in sequence, under the direction of a manager agent, through a blend of methods, or in a fully asynchronous manner, proper orchestration is key. It ensures the efficient and effective collaboration of agents.

Multi agent AI is a dynamic and complex field. It offers significant potential for tackling problems that are too intricate for traditional AI systems. By understanding and properly implementing the key components of multi-agent systems, researchers and developers can unlock this potential. They can create systems that are not only intelligent but also collaborative and adaptive.

Sounds intriguing? You’re going to need High Peak’s AI development solutions to build the perfect system for you.

Exploring common applications of multi agent AI systems

Multi agent AI systems are revolutionizing various industry sectors by offering advanced solutions that enable more complex, nuanced, and efficient task management.

Operations automation

Operations automation stands out as the most significant application at 42.7%. multi agent AI streamlines project planning, resource allocation, and monitoring. In a manufacturing context, for example, these systems can predict equipment failure, optimize supply chains, and manage inventory dynamically, thereby significantly reducing downtime and resource wastage.

Marketing

In marketing, with an application rate of 12.0%, multi agent AI systems are disrupting traditional practices. They engage in lead qualification, tailor personalized marketing campaigns, and conduct granular market research. Through AI, companies can automate and personalize customer interaction, enhancing engagement and increasing conversion rates.

Code development

Code development enjoys a 9.8% application share, taking advantage of multi agent AI for code generation, real-time bug detection, and optimization. These systems support developers by providing assistance, streamlining workflows, and accelerating development cycles, allowing human coders to focus on more complex tasks that require creative thinking.

Research

Within research, holding an 8.7% usage rate, multi agent AI systems facilitate the analysis of vast datasets, the generation of hypotheses, and the crunching of complex calculations faster than traditional methods. AI agents sift through scientific data, enabling discoveries and innovations at a pace previously unimaginable.

Education

Education, at 6.7%, is benefiting from multi agent AI through the customization of learning experiences and the management of administrative tasks. Also, AI can tailor content to suit individual student needs, assess performances, and provide feedback, thus enhancing the learning process.

Support

In support services, 5.5% of the utilization of AI agents is evident in activities such as data analysis, extracting insights, and automatic report generation. Also, these systems can provide customer service round the clock, manage tickets, and process information to resolve issues promptly.

Other applications

The remaining 14.6% covers numerous other domains where multi agent AI systems play crucial roles. These activities include coordinating autonomous vehicles and monitoring the environment. Also, they involve diagnosing in healthcare and providing security and surveillance. Each application leverages the collaborative nature of multi-agent systems to perform tasks that are beyond the scope of individual AI agents.

Breaking down the core building blocks of multi agent AI systems

In understanding the framework of multi-AI agent systems, it is vital to grasp the fundamental components that allow these systems to function efficiently and effectively. We delve into the four critical building blocks: Agents, Tasks, Tools, and Crews. These elements work in concert to enable complex, coordinated actions within the multi-agent system.

Agents

At the heart of any multi-AI agent system are the agents themselves. An agent can be described as an autonomous entity with specific roles, goals, and backstories that drive its actions within the system.

• Role: Defines the agent’s function and what it is capable of doing within the system.
• Goal: Outlines what the agent aims to achieve, driving its decision-making process.
• Backstory: Provides context that influences an agent’s priorities and preferences.

Utilizing the YAML (YAML Ain’t Markup Language) file format to define these attributes offers a dual benefit. It not only simplifies the configuration process for technical users but also makes the multi-agent system’s design readily understandable to non-technical stakeholders. This inclusivity enhances collaboration and ease of modification.

Tasks

Within a multi-AI agent system, a task represents a specific job or function that needs to be carried out. Each task is defined by:

• Description: A detailed explanation of the task.
• Expected Output: The desired result or product of the task.
• Assigned Agent: The agent responsible for completing the task.

Identifying tasks clearly and assigning them to appropriate agents are crucial steps in system design. Thus ensuring that the collective efforts of agents are directed towards achieving the overarching goals of the multi-agent system.

Tools

Tools serve as the means through which agents can complete their tasks. These can be software libraries, data sources, or any other resources that facilitate task completion. The assignment of tools can be twofold:

• Directly to an agent, equipping them with specific capabilities.
• To a task, specifying the resources needed for its completion.

By delineating tools in this manner, the system ensures that every task is approachable with the right set of capabilities, whether inherent to an agent or provided externally.

Crews

A crew is a collective unit within the multi-agent system, comprising multiple agents along with their assigned tasks. Crews are formed based on the synergy between agents’ roles and the tasks at hand, focusing on maximizing efficiency and effectiveness in achieving common goals. The concept of crews emphasizes the collaborative nature of multi-AI agent systems, where the sum of coordinated efforts often exceeds the capabilities of individual agents.

Various frameworks of multi agent AI systems

Discover leading frameworks that serve as prime examples of AI agents, designed to enhance efficiency and innovation across multiple sectors by solving complex challenges through intelligent automation.

Agency Swarm

Agency Swarm is a cutting-edge framework designed for automating multi-agent AI systems through a collaborative swarm of agents with specific roles such as CEO or developer. This approach to multi-agent AI systems simplifies the creation and management of these agents. Thus making the process more intuitive for users. Also, Agency Swarm supports tasks like communication, state management, and tool creation. Thus making it ideal for businesses aiming to boost operational efficiency with automation.

Play AI,

Play AI, powered by Play HT, is at the forefront of transforming conversational multi-agent AI systems. Specializing in technologies like AI IVR, AI phone calls, and AI Answering Services, Play AI is democratizing access to multi-agent AI systems for creating nuanced voice experiences. This platform tailors to businesses and developers seeking innovative solutions to engage customers more personally and effectively through advanced communication tools.

AutoGen

AutoGen is an open-source framework by Microsoft for creating AI agent systems. Thus enabling simplified construction of event-driven, distributed, scalable, and resilient applications where multiple AI agents collaborate autonomously or with human oversight. Also, it’s beneficial for developing complex, distributed agent networks that can enhance productivity across various industries and applications.

MetaGPT

MetaGPT is a multi-agent AI framework designed to facilitate the collaboration of multiple GPT (Generative Pre-trained Transformer) agents. It allows various AI models to interact and work together to achieve complex tasks, improving learning and decision-making processes. This framework can benefit scenarios that require collective intelligence and serve fields like NLP, chatbot development, and virtual assistants.

ChatDev

ChatDev is a framework that creates AI agents designed for software development through communicative agents like CEOs, CTOs, programmers, and more. It benefits multi-agent systems by offering highly customizable and extendable structures, fostering collaboration among agents in specialized functions. This introduces a revolutionary approach to digital development, emphasizing collective intelligence and task-oriented cooperation powered by LLMs.

OpenAI’s Swarm

OpenAI’s Swarm is an educational framework that teaches developers how to orchestrate multi-agent systems, distinct from production-level agency swarms focusing on complex, stateful interactions. It serves as a controlled environment for exploring agent coordination dynamics using Python 3.10+, without maintaining state across interactions. Unlike Agency Swarm aimed at real-world applications, Swarm is designed for experimental and learning purposes only.

Introducing CrewAI: One of the platforms for managing and leveraging multi agent AI systems

Now that we’ve discussed various frameworks of multi agent AI systems, CrewAI is another one. It is a hypothetical framework that specializes in managing and enhancing multi-agent AI systems. It streamlines the process for deploying collaborative intelligence across various applications, providing tools and support for the efficient orchestration of AI agents to solve complex tasks through coordinated efforts and shared learning.

CrewAI provides a suite of features designed to optimize performance, encourage cooperation, and ensure stability within multi-agent environments. Let’s explore some of the standout functionalities that CrewAI offers:

Caching

In multi agent AI systems, efficiency is paramount. CrewAI’s caching feature is designed to optimize tool usage, conserving resources by avoiding the unnecessary repetition of tasks. This not only saves computational power but ensures that agents can work faster and more cohesively by utilizing pre-existing data or results.

Memory

An agent’s ability to learn and recall past experiences is crucial. CrewAI empowers agents with memory capabilities, which allow them to learn from past interactions and improve over time. This shared knowledge base enhances the whole system’s performance and ensures that historical outcomes inform future decisions.

Guardrails

As AI systems grow more complex, maintaining control over their actions becomes essential. CrewAI implements guardrails to prevent unexpected or harmful behaviors, such as hallucinations in AI parlance. These checks and balances ensure that agents operate within acceptable and ethical parameters, promoting responsible AI deployment.

Testing

To measure the prowess of the multi-agent system, CrewAI includes comprehensive testing capabilities. This allows developers and system managers to evaluate the quality and performance of individual agents and tasks. Regular testing underpins continuous improvement cycles and helps identify potential areas for enhancement.

Delegation

A key to seamless multi-agent collaboration is the ability to effectively delegate tasks. CrewAI’s delegation system streamlines the automatic assignment of tasks and underpins fluid communication between agents. This feature ensures that agents can operate with a degree of autonomy while remaining cohesive in their collective purpose.

Training data

Continuous learning is at the core of AI’s progressive nature. With CrewAI’s mechanisms for utilizing training data, agents can expand their expertise and refine their skills over time. This ongoing improvement is crucial for maintaining an edge in dynamic environments where adaptability is vital.

Orchestration

Perhaps one of the most critical aspects of CrewAI is its orchestration capability, providing different options to control how agents work together. This includes:

• Sequential Orchestration: Where agents perform tasks one after another, in a specific order.
• Parallel Orchestration: Where agents work simultaneously, allowing tasks to be completed in tandem, boosting speed and efficiency.
• Hybrid Orchestration: A combination of sequential and parallel approaches, custom-tailored to suit complex tasks that require both coordination and concurrent processing.

For example, in a customer service scenario, CrewAI could use sequential orchestration to manage a ticketing system, where one agent’s resolution of an issue triggers the next relevant task for another agent. Conversely, parallel orchestration could come into play in real-time situations. It suits the monitoring of multiple data streams, like in security surveillance. Here, agents analyze different feeds simultaneously. In complex project management tasks, hybrid orchestration manage interdependent stages requiring varied levels of agent collaboration.

CrewAI provides the tools needed to build robust, flexible, and scalable multi-agent systems that can tackle an array of challenges. Its comprehensive featureset ensures that both new and established systems can operate seamlessly, learning from experiences and adapting to new requirements in an ever-evolving technological landscape.

An example of using CrewAI’s multi agent AI systems for creating an automated project

Introduction to automated project planning

Let’s dive into automated project planning. The goal is to utilize a crew to break a project into tasks, estimate them, and allocate resources effectively. This approach is common among web agencies aiming to quickly generate project offers. By reaching out to these agencies, they can swiftly estimate project requirements and plan accordingly. The crew consists of three agents: a project planner, an estimation analyst, and an allocation strategist, each assigned a specific task: task breakdown, time estimation, and research allocation.

The setup

The setup is straightforward, with three agents and three tasks, ensuring each agent focuses on a single task. Initial inputs regarding the project, its criteria, and available personnel are crucial for creating a project plan. This plan will include tasks, allocations, and milestones to track progress. The output will be structured in a JSON format, suitable for integration with project management tools like Jira or Trello. This setup gives clarity on how the crew functions. Thus leading into the deeper technical aspects of integrating necessary libraries and CrewAI examples.

First steps and libraries

The first step involves importing necessary libraries and loading environment variables. The OS library and ML library are used for loading agents and tasks, along with three main classes from CrewAI: agent class, task class, and crew class. The model selected for this project is GPT-40 mini, chosen for cost efficiency and effective execution.

Loading agents and tasks

The agents and tasks are loaded from ML files, assigning configurations to variables for agents and tasks. The agents’ configuration file includes three roles: product planning agent, estimations agent, and research allocation agent, each with a defined role, goal, and backstory. Delegation of work among agents is not permitted to maintain streamlined execution, and the agents operate in a mode that allows observation of their behavior and tool usage. Successfully setting up agent roles is crucial for synchronized task allocation, leading directly to the specific task definitions utilized in CrewAI examples.

Task definitions

The task definitions also include three tasks: task breakdown, time resource allocation, and resource allocation, with similar interpolated variables as the agents. The project type, objectives, industry, requirements, and team members are defined, allowing for dynamic adjustments to the agents and tasks’ descriptions.

Structured output model

A structured output model is created, consisting of three classes: task estimate, milestone, and project plan. The project plan will encompass a list of tasks and milestones, while the task estimate includes task names, required hours, and resources needed. The goal is to produce a project plan object that one can convert into JSON or a dictionary for external system integration. This structured output model shows how detailed planning leads seamlessly into crew initiation processes. Thus emphasizing practical applications in CrewAI examples.

Crew initiation

Referring back to the demo configuration files for agents and tasks initiates the crew. Creating each agent by referencing the corresponding configuration key ensures clarity in task ownership. The output-pidentic attribute in the Spinal Research Publication Task indicates that the final output should be an identity object, specifically the project plan structure.

Kickoff and inputs

The crew starts with three agents and tasks, and provides inputs to the crew’s kickoff function. A dictionary that includes all project-related inputs, such as project type, industry, objectives, team members, and requirements, is created. The project involves creating a website for a technology sector company, with specific requirements for design, functionality, and content. Kickoff procedures outline the initial stages of the practical application of crew operations. Thus setting a strong foundation for the detailed project requirements in the context of CrewAI examples.

Detailed project requirements

The project requirements include responsive design, modern aesthetics, user-friendliness, and essential pages like services and contact information. Additional features such as a blog section, social media links, and testimonials are also specified. This information is crucial for the crew to break down tasks and provide accurate estimates.

Crew execution and monitoring

Upon running the crew, the project planner agent begins analyzing the project requirements. The agent generates a comprehensive project breakdown, detailing tasks, timelines, dependencies, and assigned personnel. The crew execution is monitored, providing insights into task durations and dependencies. These detailed requirements underscore the importance of precise planning in automated project management. Thus leading to efficient crew execution and monitoring within CrewAI examples.

Estimation analyst

The estimation analyst agent then estimates each task, contributing to the overall project plan. Cost analysis is also performed, revealing that running the project at scale is cost-effective, with minimal expenses associated with token usage. The structured output from the crew includes a dictionary with tasks and milestones, facilitating easy visualization and understanding of the project.

Visualization in DataFrames

The tasks are displayed in a pandas DataFrame, showcasing task names, estimated hours, and required resources. Milestones are also presented in a DataFrame, highlighting the tasks necessary for completion. This automated approach significantly reduces the time required for project planning, demonstrating its value for small businesses and consultancies seeking to expedite their project offers.

Partner with High Peak for multi agent AI systems development

Given the complexity and the specialized knowledge required to develop effective multi agent AI systems, it often becomes a prudent choice to entrust this intricate work to the experts. High Peak is equipped to handle these complexities, ensuring your systems are optimized for your needs. Contact us to build advanced AI solutions tailored to your projects.