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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training AutoGen - Orchestrating High-Performing Multi-Agent AI Systems training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training AutoGen - Orchestrating High-Performing Multi-Agent AI Systems training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training AutoGen - Orchestrating High-Performing Multi-Agent AI Systems training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Expert installation of AutoGen in an optimized Python environment, configuration of LLMs such as GPT-4 or Llama via secure APIs, creation of individual agents with defined roles and custom tools, initial asynchronous workflows on real data analysis cases, practical exercises to test agent-human interactions, production of a functional agent prototype with detailed logging and code review by the trainer.
Deployment of agent groups in dynamic conversations via GroupChatManager, definition of hierarchies and specialized roles for distributed tasks, integration of external tools such as code execution or retrieval augmentation, simulation of enterprise scenarios like automated research or report generation, exercises on multi-step problem solving, development of a deliverable collaborative workflow with performance metrics and advanced debugging.
Optimization techniques for high-load agents with caching and parallelism, implementation of monitoring via LangSmith or custom dashboards, deployment on AWS or Azure cloud with Docker containers, concrete enterprise cases such as R&D automation or intelligent customer support, completion of the capstone multi-agent project, robustness testing in real conditions, maintenance plan and professional documentation for team integration.
Target audience
AI Engineers, data scientists, and ML developers seeking to upskill in autonomous agents for enterprise
Prerequisites
Advanced Python expertise, mastery of LLM APIs such as OpenAI or HuggingFace, asynchronous programming, and AI frameworks
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