Andrew Ng’s Post

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks. Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons: - It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent.  - Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. - It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction. In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! To learn more, I recommend:  - Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023)  - MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) [Original text: https://lnkd.in/g7gShRnf ]