Mario Riet-Muller’s Post

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts.

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts

The future of AI is Agentic Let's learn the basics: 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools. These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities. 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 1. Agent Core: The central processing unit that integrates all functionalities. 2. Memory Module: Stores and retrieves information to maintain context and continuity over time. 3. Tools: External resources and APIs the agent can use to perform specific tasks. 4. Planning Module: Analyzes problems and devises strategies to solve them. 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries. 2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions. 3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements. 4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion. The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. ____ Please repost it ♻️ and follow me, Armand Ruiz, for more similar posts