It is an autonomous system powered by large language models (LLMs) that, given high-level instructions, can plan, use tools, carry out steps of processing, and take actions to achieve specific goals.
It is an autonomous system powered by large language models (LLMs) that, given high-level instructions, can plan, use tools, carry out steps of processing, and take actions to achieve specific goals. These agents leverage advanced natural language processing capabilities to understand and execute complex tasks efficiently and can even collaborate with each other to achieve more sophisticated outcomes.
Users can create and configure agents using two primary methods: the La Plateforme Agent Builder, which provides a user-friendly interface, or the Agent API, which is a programmatic option for developers needing to integrate agent creation into existing workflows or applications. To start building an agent, users can visit the provided URL: https://console.mistral.ai/build/agents/new.
Customization options for agents include creating agents that communicate exclusively in French or generate Python code without explanations. For example, a French-speaking agent can be configured using specific instructions and few-shot learning, while a Python-focused agent can generate code snippets and test cases, ensuring functionality before delivering results to the user.
Agents can also be used in workflows, such as a Python agent that interprets user queries, generates and tests code, and retries if errors occur. Additionally, multiple agents can collaborate in workflows, such as a planning agent creating a data analysis plan, a Python agent generating and executing code, and a summarization agent producing a report.
Role-playing conversation agents are another use case, enabling humorous or entertaining exchanges between agents mimicking specific styles, such as stand-up comedians. These capabilities demonstrate the versatility of AI agents in achieving diverse tasks and outcomes.
It is a framework and suite of applications designed for developing and deploying large language model (LLM) applications based on Qwen (version 2.0 or higher).
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