Summary
The video provides a detailed guide on building a multi-agent system using watsonx.ai, focusing on the concept of AI multi-agent systems and the necessary framework for automation. It covers importing dependencies like crewAI, setting up access to tools like OS and watsonx, and creating LLM instances. The speaker demonstrates setting parameters, model IDs, URLs, and decoding methods, as well as creating specialized agents for tasks like speech writing. They emphasize defining roles, goals, and backstories for agents and successfully running a multi-agent system for research and speech writing tasks on Quantum Computing, ensuring task completion within a time limit.
Introduction to Multi-Agent Systems with WatsonX.AI
Introduction to building a multi-agent system using watsonx.ai. Explains the concept of AI multi-agent systems and the framework needed to create a team of specialist agents to automate tasks.
Importing Dependencies and Setting Up the Environment
Importing dependencies like crewAI and setting up access to tools like OS and watsonx. Setting API keys for watsonx and Serper Dev tool environment.
Creating the First LLM Instance
Creating the first LLM instance using watsonx LLM class. Setting parameters for the model and project ID. Specifying model ID, URL, and decoding methods.
Building the Second LLM and Agent
Creating a second LLM for function calling and building a new agent for speech writing tasks. Defining roles, goals, and backstories for the agent and setting up the task for writing a keynote speech on Quantum Computing.
Running the Multi-Agent System
Running the multi-agent system with the first agent performing research tasks and the second agent writing a keynote speech. Debugging and ensuring successful task completion within the time limit.
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